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BT 227.982 767.476 Td /F1 9.8 Tf [(Emergence: Complexity and Organization. Emergence: Complexity and Organization.)] TJ ET
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BT 15.000 745.137 Td /F2 21.0 Tf [(Pharmaceutical discovery as a complex system of decisions)] TJ ET
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BT 15.000 723.567 Td /F2 13.5 Tf [(The case of front-loaded experimentation)] TJ ET
Q
BT 15.000 705.093 Td /F3 9.8 Tf [(September 30, 2006)] TJ ET
BT 97.592 705.093 Td /F3 9.8 Tf [(·)] TJ ET
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BT 102.467 705.093 Td /F3 9.8 Tf [(Academic)] TJ ET
BT 26.250 693.252 Td /F1 9.8 Tf [(Walter Dyck)] TJ ET
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BT 78.793 693.252 Td /F1 9.8 Tf [(, )] TJ ET
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BT 84.214 693.252 Td /F1 9.8 Tf [(Peter Allen)] TJ ET
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BT 26.250 681.348 Td /F1 9.8 Tf [(Dyck W, Allen P. Pharmaceutical discovery as a complex system of decisions: The case of front-loaded experimentation. )] TJ ET
BT 26.250 669.443 Td /F1 9.8 Tf [(Emergence: Complexity and Organization. 2006 Sep 30 [last modified: 2016 Nov 26]. Edition 1. doi: )] TJ ET
BT 26.250 657.538 Td /F1 9.8 Tf [(10.emerg/10.17357.ef971751542fc194296140520e2c5dd3.)] TJ ET
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BT 26.250 628.436 Td /F4 12.0 Tf [(Introduction)] TJ ET
BT 26.250 608.481 Td /F1 9.8 Tf [(In an article to appear shortly \(McCarthy )] TJ ET
BT 202.355 608.481 Td /F5 9.8 Tf [(et al)] TJ ET
BT 220.782 608.481 Td /F1 9.8 Tf [(., 2006\) the NPD process has been shown to be a complex adaptive system. In )] TJ ET
BT 26.250 596.577 Td /F1 9.8 Tf [(earlier articles \(Allen & Ebeling, 1983; Allen, 2001; Allen & Strathern, 2005\) the theory behind the emergent nature of the )] TJ ET
BT 26.250 584.672 Td /F1 9.8 Tf [(innovation and the new product development process has been presented and discussed, linking it to the inherent uncertainties )] TJ ET
BT 26.250 572.767 Td /F1 9.8 Tf [(involved in system instabilities when new dimensions and descriptors are turned on. This is the fuzzy front end of the innovation )] TJ ET
BT 26.250 560.862 Td /F1 9.8 Tf [(process and corresponds in practice to “the period between when an opportunity is first considered and when an idea is judged )] TJ ET
BT 26.250 548.958 Td /F1 9.8 Tf [(ready for development” \(Kim & Wilemon, 2002\). In the fuzzy front end, ambiguity about the performance of the idea prevails, )] TJ ET
BT 26.250 537.053 Td /F1 9.8 Tf [(preventing it from being transferred to development where it becomes increasingly expensive to rework or kill non-performing )] TJ ET
BT 26.250 525.148 Td /F1 9.8 Tf [(product ideas as one proceeds through the process \(Verganti, 1997; Thomke & Fujimoto, 2000\).)] TJ ET
BT 26.250 505.743 Td /F1 9.8 Tf [(The impact of fuzzy front-end early problem-solving on subsequent development and/or commercialization performance is )] TJ ET
BT 26.250 493.839 Td /F1 9.8 Tf [(widely acknowledged \(Clark & Wheelwright, 1993; Bacon )] TJ ET
BT 275.509 493.839 Td /F5 9.8 Tf [(et al)] TJ ET
BT 293.936 493.839 Td /F1 9.8 Tf [(., 1994; Khurana & Rosenthal, 1997; Kim & Wilemon, 2002\). )] TJ ET
BT 26.250 481.934 Td /F1 9.8 Tf [(Thus, problem anticipation or “front-loaded” experimentation is known as “a strategy that seeks to improve development )] TJ ET
BT 26.250 470.029 Td /F1 9.8 Tf [(performance by shifting the identification and solving of [design] problems to earlier phases of a product development process” )] TJ ET
BT 26.250 458.124 Td /F1 9.8 Tf [(\(Thomke & Fujimoto, 2000\). Existing management research on front-loaded experimentation conducted during front-end )] TJ ET
BT 26.250 446.220 Td /F1 9.8 Tf [(innovation has focused predominantly on the resulting cost efficiency and lead-time performance of subsequent product )] TJ ET
BT 26.250 434.315 Td /F1 9.8 Tf [(development in a variety of industries \(Verganti, 1997; Verganti, 1999; Thomke & Fujimoto, 2000; Thomke, 2001, 2003\).)] TJ ET
BT 26.250 414.910 Td /F1 9.8 Tf [(However, in a pharmaceutical R&D context, a technology-intensive sector where typically lots of poor drug candidates get killed )] TJ ET
BT 26.250 403.005 Td /F1 9.8 Tf [(too late in the innovation process, it is of great interest to try to improve the detection of failure as early as possible. In an )] TJ ET
BT 26.250 391.101 Td /F1 9.8 Tf [(industry where a newly discovered therapeutic agent with blockbuster potential still faces more than a 90% chance of failure )] TJ ET
BT 26.250 379.196 Td /F1 9.8 Tf [(during the development phase \(Kennedy, 1997; Duyck, 2003\), and knowing that the fully loaded cost for the development of the )] TJ ET
BT 26.250 367.291 Td /F1 9.8 Tf [(agent now amounts to about one billion dollars \(Duyck, 2003; Pacl )] TJ ET
BT 314.558 367.291 Td /F5 9.8 Tf [(et al)] TJ ET
BT 332.985 367.291 Td /F1 9.8 Tf [(., 2004\), it becomes clear that enhancing the )] TJ ET
BT 26.250 355.386 Td /F1 9.8 Tf [(“predictability” of the discovery process has become an immediate priority area for investment \(Duyck, 2003\).)] TJ ET
BT 26.250 335.982 Td /F1 9.8 Tf [(Therefore, the question explored in this paper is: Can we increase predictive performance of the pharmaceutical fuzzy front-end )] TJ ET
BT 26.250 324.077 Td /F1 9.8 Tf [(innovation process — called discovery research — through front-loaded experimentation? By front loaded, we mean that )] TJ ET
BT 26.250 312.172 Td /F1 9.8 Tf [(different possible dimensions in which failure could occur should be tested early. The purpose of this study, then, is to show how )] TJ ET
BT 26.250 300.267 Td /F1 9.8 Tf [(front-loaded experimentation strategies can lead to increased predictive and business performance of discovery research. The )] TJ ET
BT 26.250 288.363 Td /F1 9.8 Tf [(unit of analysis is the experimentation and decision making carried out in the fuzzy front-end part of the innovation process, by a )] TJ ET
BT 26.250 276.458 Td /F1 9.8 Tf [(purposeful and adaptive entity — the innovation team — “by itself or in interaction with others, constructing an envisioned end )] TJ ET
BT 26.250 264.553 Td /F1 9.8 Tf [(state, taking action to reach it, and monitoring its progress” \(Poole )] TJ ET
BT 312.920 264.553 Td /F5 9.8 Tf [(et al)] TJ ET
BT 331.347 264.553 Td /F1 9.8 Tf [(., 2000\).)] TJ ET
BT 26.250 245.148 Td /F1 9.8 Tf [(Using a Monte Carlo simulation-based Bayesian inference framework to study the predictive performance of the innovation )] TJ ET
BT 26.250 233.244 Td /F1 9.8 Tf [(process, our simulation results show that certain front-loaded strategies in pharmaceutical discovery increase the odds of )] TJ ET
BT 26.250 221.339 Td /F1 9.8 Tf [(compounds succeeding subsequent development testing, provided they were found positive in discovery. Also, increasing the )] TJ ET
BT 26.250 209.434 Td /F1 9.8 Tf [(number of parallel concept explorations to an optimum level in discovery research reduces significantly the probability of missed )] TJ ET
BT 26.250 197.529 Td /F1 9.8 Tf [(opportunities in development. However, in contrast to some practitioner views \(DeWitte, 2002\), simulation results show that )] TJ ET
BT 26.250 185.625 Td /F1 9.8 Tf [(front-loaded strategies do not significantly decrease the probability of missed opportunities in development. Nor can this study )] TJ ET
BT 26.250 173.720 Td /F1 9.8 Tf [(confirm the benefit of )] TJ ET
BT 120.006 173.720 Td /F5 9.8 Tf [(early)] TJ ET
BT 141.134 173.720 Td /F1 9.8 Tf [( front loading, integrating early knowledge on the therapeutic agent gained through virtual )] TJ ET
BT 528.638 173.720 Td /F5 9.8 Tf [(in-silico)] TJ ET
BT 26.250 161.815 Td /F1 9.8 Tf [(methods \(Pickering, 2001\).)] TJ ET
BT 26.250 142.410 Td /F1 9.8 Tf [(The business implication of our simulation-based findings is that the key to reduced spend and overruns in pharmaceutical )] TJ ET
BT 26.250 130.506 Td /F1 9.8 Tf [(development is not simply the reduction in time-to-market nor efficiency enhancements, but is mainly to be found in discovery, )] TJ ET
BT 26.250 118.601 Td /F1 9.8 Tf [(where efforts to better understand drug candidates lead to higher success rates or lower attrition later in the innovation process.)] TJ ET
BT 26.250 81.998 Td /F4 12.0 Tf [(Improving experimentation process predictability)] TJ ET
BT 26.250 62.044 Td /F1 9.8 Tf [(Empirical studies acknowledge the vital role and value of search through experimentation in complex and novel environments )] TJ ET
BT 26.250 50.139 Td /F1 9.8 Tf [(such as semiconductor \(West & Iansiti, 2003\) and pharmaceutical R&D \(Thomke )] TJ ET
BT 376.831 50.139 Td /F5 9.8 Tf [(et al)] TJ ET
BT 395.258 50.139 Td /F1 9.8 Tf [(., 1998; Thomke, 2003\).)] TJ ET
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BT 15.000 745.137 Td /F2 21.0 Tf [(Pharmaceutical discovery as a complex system of decisions)] TJ ET
0.271 0.267 0.267 rg
BT 15.000 723.567 Td /F2 13.5 Tf [(The case of front-loaded experimentation)] TJ ET
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BT 15.000 705.093 Td /F3 9.8 Tf [(September 30, 2006)] TJ ET
BT 97.592 705.093 Td /F3 9.8 Tf [(·)] TJ ET
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BT 102.467 705.093 Td /F3 9.8 Tf [(Academic)] TJ ET
BT 26.250 693.252 Td /F1 9.8 Tf [(Walter Dyck)] TJ ET
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BT 78.793 693.252 Td /F1 9.8 Tf [(, )] TJ ET
0.267 0.267 0.267 rg
BT 84.214 693.252 Td /F1 9.8 Tf [(Peter Allen)] TJ ET
0.271 0.267 0.267 rg
BT 26.250 681.348 Td /F1 9.8 Tf [(Dyck W, Allen P. Pharmaceutical discovery as a complex system of decisions: The case of front-loaded experimentation. )] TJ ET
BT 26.250 669.443 Td /F1 9.8 Tf [(Emergence: Complexity and Organization. 2006 Sep 30 [last modified: 2016 Nov 26]. Edition 1. doi: )] TJ ET
BT 26.250 657.538 Td /F1 9.8 Tf [(10.emerg/10.17357.ef971751542fc194296140520e2c5dd3.)] TJ ET
q
15.000 40.259 577.500 614.899 re W n
0.271 0.267 0.267 rg
BT 26.250 628.436 Td /F4 12.0 Tf [(Introduction)] TJ ET
BT 26.250 608.481 Td /F1 9.8 Tf [(In an article to appear shortly \(McCarthy )] TJ ET
BT 202.355 608.481 Td /F5 9.8 Tf [(et al)] TJ ET
BT 220.782 608.481 Td /F1 9.8 Tf [(., 2006\) the NPD process has been shown to be a complex adaptive system. In )] TJ ET
BT 26.250 596.577 Td /F1 9.8 Tf [(earlier articles \(Allen & Ebeling, 1983; Allen, 2001; Allen & Strathern, 2005\) the theory behind the emergent nature of the )] TJ ET
BT 26.250 584.672 Td /F1 9.8 Tf [(innovation and the new product development process has been presented and discussed, linking it to the inherent uncertainties )] TJ ET
BT 26.250 572.767 Td /F1 9.8 Tf [(involved in system instabilities when new dimensions and descriptors are turned on. This is the fuzzy front end of the innovation )] TJ ET
BT 26.250 560.862 Td /F1 9.8 Tf [(process and corresponds in practice to “the period between when an opportunity is first considered and when an idea is judged )] TJ ET
BT 26.250 548.958 Td /F1 9.8 Tf [(ready for development” \(Kim & Wilemon, 2002\). In the fuzzy front end, ambiguity about the performance of the idea prevails, )] TJ ET
BT 26.250 537.053 Td /F1 9.8 Tf [(preventing it from being transferred to development where it becomes increasingly expensive to rework or kill non-performing )] TJ ET
BT 26.250 525.148 Td /F1 9.8 Tf [(product ideas as one proceeds through the process \(Verganti, 1997; Thomke & Fujimoto, 2000\).)] TJ ET
BT 26.250 505.743 Td /F1 9.8 Tf [(The impact of fuzzy front-end early problem-solving on subsequent development and/or commercialization performance is )] TJ ET
BT 26.250 493.839 Td /F1 9.8 Tf [(widely acknowledged \(Clark & Wheelwright, 1993; Bacon )] TJ ET
BT 275.509 493.839 Td /F5 9.8 Tf [(et al)] TJ ET
BT 293.936 493.839 Td /F1 9.8 Tf [(., 1994; Khurana & Rosenthal, 1997; Kim & Wilemon, 2002\). )] TJ ET
BT 26.250 481.934 Td /F1 9.8 Tf [(Thus, problem anticipation or “front-loaded” experimentation is known as “a strategy that seeks to improve development )] TJ ET
BT 26.250 470.029 Td /F1 9.8 Tf [(performance by shifting the identification and solving of [design] problems to earlier phases of a product development process” )] TJ ET
BT 26.250 458.124 Td /F1 9.8 Tf [(\(Thomke & Fujimoto, 2000\). Existing management research on front-loaded experimentation conducted during front-end )] TJ ET
BT 26.250 446.220 Td /F1 9.8 Tf [(innovation has focused predominantly on the resulting cost efficiency and lead-time performance of subsequent product )] TJ ET
BT 26.250 434.315 Td /F1 9.8 Tf [(development in a variety of industries \(Verganti, 1997; Verganti, 1999; Thomke & Fujimoto, 2000; Thomke, 2001, 2003\).)] TJ ET
BT 26.250 414.910 Td /F1 9.8 Tf [(However, in a pharmaceutical R&D context, a technology-intensive sector where typically lots of poor drug candidates get killed )] TJ ET
BT 26.250 403.005 Td /F1 9.8 Tf [(too late in the innovation process, it is of great interest to try to improve the detection of failure as early as possible. In an )] TJ ET
BT 26.250 391.101 Td /F1 9.8 Tf [(industry where a newly discovered therapeutic agent with blockbuster potential still faces more than a 90% chance of failure )] TJ ET
BT 26.250 379.196 Td /F1 9.8 Tf [(during the development phase \(Kennedy, 1997; Duyck, 2003\), and knowing that the fully loaded cost for the development of the )] TJ ET
BT 26.250 367.291 Td /F1 9.8 Tf [(agent now amounts to about one billion dollars \(Duyck, 2003; Pacl )] TJ ET
BT 314.558 367.291 Td /F5 9.8 Tf [(et al)] TJ ET
BT 332.985 367.291 Td /F1 9.8 Tf [(., 2004\), it becomes clear that enhancing the )] TJ ET
BT 26.250 355.386 Td /F1 9.8 Tf [(“predictability” of the discovery process has become an immediate priority area for investment \(Duyck, 2003\).)] TJ ET
BT 26.250 335.982 Td /F1 9.8 Tf [(Therefore, the question explored in this paper is: Can we increase predictive performance of the pharmaceutical fuzzy front-end )] TJ ET
BT 26.250 324.077 Td /F1 9.8 Tf [(innovation process — called discovery research — through front-loaded experimentation? By front loaded, we mean that )] TJ ET
BT 26.250 312.172 Td /F1 9.8 Tf [(different possible dimensions in which failure could occur should be tested early. The purpose of this study, then, is to show how )] TJ ET
BT 26.250 300.267 Td /F1 9.8 Tf [(front-loaded experimentation strategies can lead to increased predictive and business performance of discovery research. The )] TJ ET
BT 26.250 288.363 Td /F1 9.8 Tf [(unit of analysis is the experimentation and decision making carried out in the fuzzy front-end part of the innovation process, by a )] TJ ET
BT 26.250 276.458 Td /F1 9.8 Tf [(purposeful and adaptive entity — the innovation team — “by itself or in interaction with others, constructing an envisioned end )] TJ ET
BT 26.250 264.553 Td /F1 9.8 Tf [(state, taking action to reach it, and monitoring its progress” \(Poole )] TJ ET
BT 312.920 264.553 Td /F5 9.8 Tf [(et al)] TJ ET
BT 331.347 264.553 Td /F1 9.8 Tf [(., 2000\).)] TJ ET
BT 26.250 245.148 Td /F1 9.8 Tf [(Using a Monte Carlo simulation-based Bayesian inference framework to study the predictive performance of the innovation )] TJ ET
BT 26.250 233.244 Td /F1 9.8 Tf [(process, our simulation results show that certain front-loaded strategies in pharmaceutical discovery increase the odds of )] TJ ET
BT 26.250 221.339 Td /F1 9.8 Tf [(compounds succeeding subsequent development testing, provided they were found positive in discovery. Also, increasing the )] TJ ET
BT 26.250 209.434 Td /F1 9.8 Tf [(number of parallel concept explorations to an optimum level in discovery research reduces significantly the probability of missed )] TJ ET
BT 26.250 197.529 Td /F1 9.8 Tf [(opportunities in development. However, in contrast to some practitioner views \(DeWitte, 2002\), simulation results show that )] TJ ET
BT 26.250 185.625 Td /F1 9.8 Tf [(front-loaded strategies do not significantly decrease the probability of missed opportunities in development. Nor can this study )] TJ ET
BT 26.250 173.720 Td /F1 9.8 Tf [(confirm the benefit of )] TJ ET
BT 120.006 173.720 Td /F5 9.8 Tf [(early)] TJ ET
BT 141.134 173.720 Td /F1 9.8 Tf [( front loading, integrating early knowledge on the therapeutic agent gained through virtual )] TJ ET
BT 528.638 173.720 Td /F5 9.8 Tf [(in-silico)] TJ ET
BT 26.250 161.815 Td /F1 9.8 Tf [(methods \(Pickering, 2001\).)] TJ ET
BT 26.250 142.410 Td /F1 9.8 Tf [(The business implication of our simulation-based findings is that the key to reduced spend and overruns in pharmaceutical )] TJ ET
BT 26.250 130.506 Td /F1 9.8 Tf [(development is not simply the reduction in time-to-market nor efficiency enhancements, but is mainly to be found in discovery, )] TJ ET
BT 26.250 118.601 Td /F1 9.8 Tf [(where efforts to better understand drug candidates lead to higher success rates or lower attrition later in the innovation process.)] TJ ET
BT 26.250 81.998 Td /F4 12.0 Tf [(Improving experimentation process predictability)] TJ ET
BT 26.250 62.044 Td /F1 9.8 Tf [(Empirical studies acknowledge the vital role and value of search through experimentation in complex and novel environments )] TJ ET
BT 26.250 50.139 Td /F1 9.8 Tf [(such as semiconductor \(West & Iansiti, 2003\) and pharmaceutical R&D \(Thomke )] TJ ET
BT 376.831 50.139 Td /F5 9.8 Tf [(et al)] TJ ET
BT 395.258 50.139 Td /F1 9.8 Tf [(., 1998; Thomke, 2003\).)] TJ ET
Q
q
15.000 714.359 577.500 50.736 re W n
0.267 0.267 0.267 rg
BT 15.000 745.137 Td /F2 21.0 Tf [(Pharmaceutical discovery as a complex system of decisions)] TJ ET
0.271 0.267 0.267 rg
BT 15.000 723.567 Td /F2 13.5 Tf [(The case of front-loaded experimentation)] TJ ET
Q
BT 15.000 705.093 Td /F3 9.8 Tf [(September 30, 2006)] TJ ET
BT 97.592 705.093 Td /F3 9.8 Tf [(·)] TJ ET
0.267 0.267 0.267 rg
BT 102.467 705.093 Td /F3 9.8 Tf [(Academic)] TJ ET
BT 26.250 693.252 Td /F1 9.8 Tf [(Walter Dyck)] TJ ET
0.271 0.267 0.267 rg
BT 78.793 693.252 Td /F1 9.8 Tf [(, )] TJ ET
0.267 0.267 0.267 rg
BT 84.214 693.252 Td /F1 9.8 Tf [(Peter Allen)] TJ ET
0.271 0.267 0.267 rg
BT 26.250 681.348 Td /F1 9.8 Tf [(Dyck W, Allen P. Pharmaceutical discovery as a complex system of decisions: The case of front-loaded experimentation. )] TJ ET
BT 26.250 669.443 Td /F1 9.8 Tf [(Emergence: Complexity and Organization. 2006 Sep 30 [last modified: 2016 Nov 26]. Edition 1. doi: )] TJ ET
BT 26.250 657.538 Td /F1 9.8 Tf [(10.emerg/10.17357.ef971751542fc194296140520e2c5dd3.)] TJ ET
q
15.000 40.259 577.500 614.899 re W n
0.271 0.267 0.267 rg
BT 26.250 628.436 Td /F4 12.0 Tf [(Introduction)] TJ ET
BT 26.250 608.481 Td /F1 9.8 Tf [(In an article to appear shortly \(McCarthy )] TJ ET
BT 202.355 608.481 Td /F5 9.8 Tf [(et al)] TJ ET
BT 220.782 608.481 Td /F1 9.8 Tf [(., 2006\) the NPD process has been shown to be a complex adaptive system. In )] TJ ET
BT 26.250 596.577 Td /F1 9.8 Tf [(earlier articles \(Allen & Ebeling, 1983; Allen, 2001; Allen & Strathern, 2005\) the theory behind the emergent nature of the )] TJ ET
BT 26.250 584.672 Td /F1 9.8 Tf [(innovation and the new product development process has been presented and discussed, linking it to the inherent uncertainties )] TJ ET
BT 26.250 572.767 Td /F1 9.8 Tf [(involved in system instabilities when new dimensions and descriptors are turned on. This is the fuzzy front end of the innovation )] TJ ET
BT 26.250 560.862 Td /F1 9.8 Tf [(process and corresponds in practice to “the period between when an opportunity is first considered and when an idea is judged )] TJ ET
BT 26.250 548.958 Td /F1 9.8 Tf [(ready for development” \(Kim & Wilemon, 2002\). In the fuzzy front end, ambiguity about the performance of the idea prevails, )] TJ ET
BT 26.250 537.053 Td /F1 9.8 Tf [(preventing it from being transferred to development where it becomes increasingly expensive to rework or kill non-performing )] TJ ET
BT 26.250 525.148 Td /F1 9.8 Tf [(product ideas as one proceeds through the process \(Verganti, 1997; Thomke & Fujimoto, 2000\).)] TJ ET
BT 26.250 505.743 Td /F1 9.8 Tf [(The impact of fuzzy front-end early problem-solving on subsequent development and/or commercialization performance is )] TJ ET
BT 26.250 493.839 Td /F1 9.8 Tf [(widely acknowledged \(Clark & Wheelwright, 1993; Bacon )] TJ ET
BT 275.509 493.839 Td /F5 9.8 Tf [(et al)] TJ ET
BT 293.936 493.839 Td /F1 9.8 Tf [(., 1994; Khurana & Rosenthal, 1997; Kim & Wilemon, 2002\). )] TJ ET
BT 26.250 481.934 Td /F1 9.8 Tf [(Thus, problem anticipation or “front-loaded” experimentation is known as “a strategy that seeks to improve development )] TJ ET
BT 26.250 470.029 Td /F1 9.8 Tf [(performance by shifting the identification and solving of [design] problems to earlier phases of a product development process” )] TJ ET
BT 26.250 458.124 Td /F1 9.8 Tf [(\(Thomke & Fujimoto, 2000\). Existing management research on front-loaded experimentation conducted during front-end )] TJ ET
BT 26.250 446.220 Td /F1 9.8 Tf [(innovation has focused predominantly on the resulting cost efficiency and lead-time performance of subsequent product )] TJ ET
BT 26.250 434.315 Td /F1 9.8 Tf [(development in a variety of industries \(Verganti, 1997; Verganti, 1999; Thomke & Fujimoto, 2000; Thomke, 2001, 2003\).)] TJ ET
BT 26.250 414.910 Td /F1 9.8 Tf [(However, in a pharmaceutical R&D context, a technology-intensive sector where typically lots of poor drug candidates get killed )] TJ ET
BT 26.250 403.005 Td /F1 9.8 Tf [(too late in the innovation process, it is of great interest to try to improve the detection of failure as early as possible. In an )] TJ ET
BT 26.250 391.101 Td /F1 9.8 Tf [(industry where a newly discovered therapeutic agent with blockbuster potential still faces more than a 90% chance of failure )] TJ ET
BT 26.250 379.196 Td /F1 9.8 Tf [(during the development phase \(Kennedy, 1997; Duyck, 2003\), and knowing that the fully loaded cost for the development of the )] TJ ET
BT 26.250 367.291 Td /F1 9.8 Tf [(agent now amounts to about one billion dollars \(Duyck, 2003; Pacl )] TJ ET
BT 314.558 367.291 Td /F5 9.8 Tf [(et al)] TJ ET
BT 332.985 367.291 Td /F1 9.8 Tf [(., 2004\), it becomes clear that enhancing the )] TJ ET
BT 26.250 355.386 Td /F1 9.8 Tf [(“predictability” of the discovery process has become an immediate priority area for investment \(Duyck, 2003\).)] TJ ET
BT 26.250 335.982 Td /F1 9.8 Tf [(Therefore, the question explored in this paper is: Can we increase predictive performance of the pharmaceutical fuzzy front-end )] TJ ET
BT 26.250 324.077 Td /F1 9.8 Tf [(innovation process — called discovery research — through front-loaded experimentation? By front loaded, we mean that )] TJ ET
BT 26.250 312.172 Td /F1 9.8 Tf [(different possible dimensions in which failure could occur should be tested early. The purpose of this study, then, is to show how )] TJ ET
BT 26.250 300.267 Td /F1 9.8 Tf [(front-loaded experimentation strategies can lead to increased predictive and business performance of discovery research. The )] TJ ET
BT 26.250 288.363 Td /F1 9.8 Tf [(unit of analysis is the experimentation and decision making carried out in the fuzzy front-end part of the innovation process, by a )] TJ ET
BT 26.250 276.458 Td /F1 9.8 Tf [(purposeful and adaptive entity — the innovation team — “by itself or in interaction with others, constructing an envisioned end )] TJ ET
BT 26.250 264.553 Td /F1 9.8 Tf [(state, taking action to reach it, and monitoring its progress” \(Poole )] TJ ET
BT 312.920 264.553 Td /F5 9.8 Tf [(et al)] TJ ET
BT 331.347 264.553 Td /F1 9.8 Tf [(., 2000\).)] TJ ET
BT 26.250 245.148 Td /F1 9.8 Tf [(Using a Monte Carlo simulation-based Bayesian inference framework to study the predictive performance of the innovation )] TJ ET
BT 26.250 233.244 Td /F1 9.8 Tf [(process, our simulation results show that certain front-loaded strategies in pharmaceutical discovery increase the odds of )] TJ ET
BT 26.250 221.339 Td /F1 9.8 Tf [(compounds succeeding subsequent development testing, provided they were found positive in discovery. Also, increasing the )] TJ ET
BT 26.250 209.434 Td /F1 9.8 Tf [(number of parallel concept explorations to an optimum level in discovery research reduces significantly the probability of missed )] TJ ET
BT 26.250 197.529 Td /F1 9.8 Tf [(opportunities in development. However, in contrast to some practitioner views \(DeWitte, 2002\), simulation results show that )] TJ ET
BT 26.250 185.625 Td /F1 9.8 Tf [(front-loaded strategies do not significantly decrease the probability of missed opportunities in development. Nor can this study )] TJ ET
BT 26.250 173.720 Td /F1 9.8 Tf [(confirm the benefit of )] TJ ET
BT 120.006 173.720 Td /F5 9.8 Tf [(early)] TJ ET
BT 141.134 173.720 Td /F1 9.8 Tf [( front loading, integrating early knowledge on the therapeutic agent gained through virtual )] TJ ET
BT 528.638 173.720 Td /F5 9.8 Tf [(in-silico)] TJ ET
BT 26.250 161.815 Td /F1 9.8 Tf [(methods \(Pickering, 2001\).)] TJ ET
BT 26.250 142.410 Td /F1 9.8 Tf [(The business implication of our simulation-based findings is that the key to reduced spend and overruns in pharmaceutical )] TJ ET
BT 26.250 130.506 Td /F1 9.8 Tf [(development is not simply the reduction in time-to-market nor efficiency enhancements, but is mainly to be found in discovery, )] TJ ET
BT 26.250 118.601 Td /F1 9.8 Tf [(where efforts to better understand drug candidates lead to higher success rates or lower attrition later in the innovation process.)] TJ ET
BT 26.250 81.998 Td /F4 12.0 Tf [(Improving experimentation process predictability)] TJ ET
BT 26.250 62.044 Td /F1 9.8 Tf [(Empirical studies acknowledge the vital role and value of search through experimentation in complex and novel environments )] TJ ET
BT 26.250 50.139 Td /F1 9.8 Tf [(such as semiconductor \(West & Iansiti, 2003\) and pharmaceutical R&D \(Thomke )] TJ ET
BT 376.831 50.139 Td /F5 9.8 Tf [(et al)] TJ ET
BT 395.258 50.139 Td /F1 9.8 Tf [(., 1998; Thomke, 2003\).)] TJ ET
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BT 291.710 19.825 Td /F1 11.0 Tf [(1)] TJ ET
BT 25.000 19.825 Td /F1 11.0 Tf [(Emergence: Complexity and Organization)] TJ ET
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BT 26.250 767.476 Td /F1 9.8 Tf [(However, current experimentation research focuses mainly on strategies aimed at accelerating product development lead-time )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(and enhancing efficiency. Thus, strategies are proposed to compress the experimentation process using concurrent engineering )] TJ ET
BT 26.250 743.667 Td /F1 9.8 Tf [(\(Krishnan )] TJ ET
BT 70.681 743.667 Td /F5 9.8 Tf [(et al)] TJ ET
BT 89.108 743.667 Td /F1 9.8 Tf [(., 1997; Smith & Eppinger, 1997a, 1997b; Loch & Terwiesch, 1998; Roemer )] TJ ET
BT 418.073 743.667 Td /F5 9.8 Tf [(et al)] TJ ET
BT 436.501 743.667 Td /F1 9.8 Tf [(., 2000; Mihm )] TJ ET
BT 498.277 743.667 Td /F5 9.8 Tf [(et al)] TJ ET
BT 516.704 743.667 Td /F1 9.8 Tf [(., 2003\) or to )] TJ ET
BT 26.250 731.762 Td /F1 9.8 Tf [(make it more cost effective \(Thomke 1998a, 1998b\).)] TJ ET
BT 26.250 712.357 Td /F1 9.8 Tf [(Verganti \(1999\) argues for a )] TJ ET
BT 150.894 712.357 Td /F5 9.8 Tf [(front-loading)] TJ ET
BT 205.084 712.357 Td /F1 9.8 Tf [( mechanism he calls “planned flexibility,” which deals with uncertainty through early )] TJ ET
BT 26.250 700.452 Td /F1 9.8 Tf [(identification of specific critical areas of a given project and early planning for reaction measures, leading to improved time to )] TJ ET
BT 26.250 688.548 Td /F1 9.8 Tf [(market and product quality. Also, Thomke and Fujimoto’s \(2000\) case study at Toyota gives field support for front loading as an )] TJ ET
BT 26.250 676.643 Td /F1 9.8 Tf [(important methodology to accelerate product development. “Enlightened” experimenters use front-loaded development, )] TJ ET
BT 26.250 664.738 Td /F1 9.8 Tf [(exploiting early information to spot and solve problems as far upstream as possible \(Thomke, 2001\). Survey-based research of )] TJ ET
BT 26.250 652.833 Td /F1 9.8 Tf [(29 Internet software development projects \(MacCormack & Verganti, 2003\) also provides support for the front-loading benefits )] TJ ET
BT 26.250 640.929 Td /F1 9.8 Tf [(argument. Here, for projects facing greater uncertainty, early technical and market feedback had a stronger association with )] TJ ET
BT 26.250 629.024 Td /F1 9.8 Tf [(performance. Finally, in the late 1990s pharmaceutical and biotech companies also discovered the benefits of finding potential )] TJ ET
BT 26.250 617.119 Td /F1 9.8 Tf [(failure modes as early as possible in the development process. In a case study of Millennium Pharmaceuticals, it was shown )] TJ ET
BT 26.250 605.214 Td /F1 9.8 Tf [(how new technologies for experimentation can form the basis for fundamentally rethinking the innovation process by shifting )] TJ ET
BT 26.250 593.310 Td /F1 9.8 Tf [(failures to earlier phases \(Thomke, 2003\).)] TJ ET
BT 26.250 573.905 Td /F5 9.8 Tf [(Parallelism)] TJ ET
BT 73.918 573.905 Td /F1 9.8 Tf [( or the number of alternative approaches explored to solve a problem is known to improve the quality of the solution. )] TJ ET
BT 26.250 562.000 Td /F1 9.8 Tf [(Abernathy and Rosenbloom \(1968\) showed that it is common in technological development to explore several approaches so )] TJ ET
BT 26.250 550.095 Td /F1 9.8 Tf [(that the best approach can be chosen. More recently, problem-solving efficiency in complex and novel environments is )] TJ ET
BT 26.250 538.191 Td /F1 9.8 Tf [(associated with a broad exploration and framing of the solution space, reaching across multiple knowledge domains \(Schrader )] TJ ET
BT 26.250 526.286 Td /F5 9.8 Tf [(et al)] TJ ET
BT 44.677 526.286 Td /F1 9.8 Tf [(., 1992; Iansiti, 1998; Golder & Tellis, 2004\). Toyota tries not to converge too quickly on a “best guess” solution for its new )] TJ ET
BT 26.250 514.381 Td /F1 9.8 Tf [(designs. Instead, the company has a high regard for learning on multiple ideas in parallel, using a “set-based” approach to )] TJ ET
BT 26.250 502.476 Td /F1 9.8 Tf [(product development \(Sobek II )] TJ ET
BT 161.736 502.476 Td /F5 9.8 Tf [(et al)] TJ ET
BT 180.163 502.476 Td /F1 9.8 Tf [(., 1999\), improving the quality of the commercialized product. More recent research )] TJ ET
BT 26.250 490.572 Td /F1 9.8 Tf [(concludes that set-based coordination should be emphasized if either starvation costs or the cost of carrying multiple design )] TJ ET
BT 26.250 478.667 Td /F1 9.8 Tf [(options in parallel are low \(Terwiesch )] TJ ET
BT 189.338 478.667 Td /F5 9.8 Tf [(et al)] TJ ET
BT 207.766 478.667 Td /F1 9.8 Tf [(., 2002\). Loch )] TJ ET
BT 270.097 478.667 Td /F5 9.8 Tf [(et al)] TJ ET
BT 288.525 478.667 Td /F1 9.8 Tf [(.’s \(2001\) analysis of experimentation policies shows that the )] TJ ET
BT 26.250 466.762 Td /F1 9.8 Tf [(optimal mix of parallel and sequential testing depends on the ratio of the financial cost and the cost of testing. A recent analysis )] TJ ET
BT 26.250 454.857 Td /F1 9.8 Tf [(of NPD decision making based on a pharmaceutical product development case developed closed-form solutions for the optimal )] TJ ET
BT 26.250 442.953 Td /F1 9.8 Tf [(number of concept tests to be conducted under profit uncertainty \(Dahan & Mendelson, 2001\). Their key finding was that the )] TJ ET
BT 26.250 431.048 Td /F1 9.8 Tf [(optimal number of concept tests depends not only on the cost of testing and the scale of uncertainty, but also on the distribution )] TJ ET
BT 26.250 419.143 Td /F1 9.8 Tf [(shape of that uncertainty.)] TJ ET
BT 26.250 399.738 Td /F1 9.8 Tf [(Finally, Mena )] TJ ET
BT 86.934 399.738 Td /F5 9.8 Tf [(et al)] TJ ET
BT 105.361 399.738 Td /F1 9.8 Tf [(. \(2001\) use a genetic algorithm-based simulation model to understand how the concepts of evolution and )] TJ ET
BT 26.250 387.834 Td /F1 9.8 Tf [(learning can be used to improve the product development process. They found that diversity of designs explored and process )] TJ ET
BT 26.250 375.929 Td /F1 9.8 Tf [(duration are by far the most important parameters in finding a satisfactory solution, while other factors were found to be )] TJ ET
BT 26.250 364.024 Td /F1 9.8 Tf [(negligible.)] TJ ET
BT 26.250 344.619 Td /F1 9.8 Tf [(From the above as well as a recent literature review and meta-analysis on relationships between integrated product )] TJ ET
BT 26.250 332.715 Td /F1 9.8 Tf [(development \(IPD\) characteristics and project performance \(Gerwin & Barrowman, 2002\), we conclude that present )] TJ ET
BT 26.250 320.810 Td /F1 9.8 Tf [(experimentation behavior research strongly emphasizes the study of the impact of various experimentation strategies on )] TJ ET
BT 26.250 308.905 Td /F1 9.8 Tf [(development lead time and efficiency, performance variables that are typical for product development and innovation research. )] TJ ET
BT 26.250 297.000 Td /F1 9.8 Tf [(No study was found to contribute to the exploration of )] TJ ET
BT 259.294 297.000 Td /F5 9.8 Tf [(predictive)] TJ ET
BT 301.015 297.000 Td /F1 9.8 Tf [( performance of experimentation strategies.)] TJ ET
BT 26.250 277.596 Td /F1 9.8 Tf [(However, the need to improve the predictability of the experimentation process has recently been advocated by the )] TJ ET
BT 26.250 265.691 Td /F1 9.8 Tf [(pharmaceutical R&D community. Lesko )] TJ ET
BT 199.634 265.691 Td /F5 9.8 Tf [(et al)] TJ ET
BT 218.062 265.691 Td /F1 9.8 Tf [(. \(2000\) conclude that to get to better therapeutic agents with lower development )] TJ ET
BT 26.250 253.786 Td /F1 9.8 Tf [(risks, the pharmaceutical R&D process needs to move from an essentially empirical mode to a more mechanistic and predictive )] TJ ET
BT 26.250 241.881 Td /F1 9.8 Tf [(one. The general goal should be to integrate early knowledge gained during discovery into the drug development decision-)] TJ ET
BT 26.250 229.977 Td /F1 9.8 Tf [(making process. This approach would find failures faster, resulting in more economical and informative development programs. )] TJ ET
BT 26.250 218.072 Td /F1 9.8 Tf [(Or, more recently confirmed by Duyck in an influential paper, “The current lack of predictability not only represents a deficit in )] TJ ET
BT 26.250 206.167 Td /F1 9.8 Tf [(our knowledge base, but results in substantial opportunity cost, increased financial cost for therapeutic development, and limits )] TJ ET
BT 26.250 194.262 Td /F1 9.8 Tf [(on the potential impact of our basic research enterprise on public health” \(Duyck, 2003\).)] TJ ET
BT 26.250 174.858 Td /F1 9.8 Tf [(Therefore, in the following section, experimentation strategy in pharmaceutical discovery will be explored in one of the top-10 )] TJ ET
BT 26.250 162.953 Td /F1 9.8 Tf [(pharmaceutical R&D operations that we will further refer to as PharmaCo. This will form the empirical base for the simulation-)] TJ ET
BT 26.250 151.048 Td /F1 9.8 Tf [(based study.)] TJ ET
BT 26.250 114.446 Td /F4 12.0 Tf [(Exploring experimentation strategy in pharmaceutical discovery)] TJ ET
BT 26.250 77.294 Td /F4 12.0 Tf [(The pharmaceutical discovery process)] TJ ET
BT 26.250 57.339 Td /F1 9.8 Tf [(Before exploring the predictive performance of experimentation strategies in pharmaceutical discovery, the process needs to be )] TJ ET
BT 26.250 45.435 Td /F1 9.8 Tf [(understood. Figure 1 provides an overview of a typical drug discovery process as found in a major R&D operation[1]. Once a )] TJ ET
Q
q
15.000 31.149 577.500 745.851 re W n
0.271 0.267 0.267 rg
BT 26.250 767.476 Td /F1 9.8 Tf [(However, current experimentation research focuses mainly on strategies aimed at accelerating product development lead-time )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(and enhancing efficiency. Thus, strategies are proposed to compress the experimentation process using concurrent engineering )] TJ ET
BT 26.250 743.667 Td /F1 9.8 Tf [(\(Krishnan )] TJ ET
BT 70.681 743.667 Td /F5 9.8 Tf [(et al)] TJ ET
BT 89.108 743.667 Td /F1 9.8 Tf [(., 1997; Smith & Eppinger, 1997a, 1997b; Loch & Terwiesch, 1998; Roemer )] TJ ET
BT 418.073 743.667 Td /F5 9.8 Tf [(et al)] TJ ET
BT 436.501 743.667 Td /F1 9.8 Tf [(., 2000; Mihm )] TJ ET
BT 498.277 743.667 Td /F5 9.8 Tf [(et al)] TJ ET
BT 516.704 743.667 Td /F1 9.8 Tf [(., 2003\) or to )] TJ ET
BT 26.250 731.762 Td /F1 9.8 Tf [(make it more cost effective \(Thomke 1998a, 1998b\).)] TJ ET
BT 26.250 712.357 Td /F1 9.8 Tf [(Verganti \(1999\) argues for a )] TJ ET
BT 150.894 712.357 Td /F5 9.8 Tf [(front-loading)] TJ ET
BT 205.084 712.357 Td /F1 9.8 Tf [( mechanism he calls “planned flexibility,” which deals with uncertainty through early )] TJ ET
BT 26.250 700.452 Td /F1 9.8 Tf [(identification of specific critical areas of a given project and early planning for reaction measures, leading to improved time to )] TJ ET
BT 26.250 688.548 Td /F1 9.8 Tf [(market and product quality. Also, Thomke and Fujimoto’s \(2000\) case study at Toyota gives field support for front loading as an )] TJ ET
BT 26.250 676.643 Td /F1 9.8 Tf [(important methodology to accelerate product development. “Enlightened” experimenters use front-loaded development, )] TJ ET
BT 26.250 664.738 Td /F1 9.8 Tf [(exploiting early information to spot and solve problems as far upstream as possible \(Thomke, 2001\). Survey-based research of )] TJ ET
BT 26.250 652.833 Td /F1 9.8 Tf [(29 Internet software development projects \(MacCormack & Verganti, 2003\) also provides support for the front-loading benefits )] TJ ET
BT 26.250 640.929 Td /F1 9.8 Tf [(argument. Here, for projects facing greater uncertainty, early technical and market feedback had a stronger association with )] TJ ET
BT 26.250 629.024 Td /F1 9.8 Tf [(performance. Finally, in the late 1990s pharmaceutical and biotech companies also discovered the benefits of finding potential )] TJ ET
BT 26.250 617.119 Td /F1 9.8 Tf [(failure modes as early as possible in the development process. In a case study of Millennium Pharmaceuticals, it was shown )] TJ ET
BT 26.250 605.214 Td /F1 9.8 Tf [(how new technologies for experimentation can form the basis for fundamentally rethinking the innovation process by shifting )] TJ ET
BT 26.250 593.310 Td /F1 9.8 Tf [(failures to earlier phases \(Thomke, 2003\).)] TJ ET
BT 26.250 573.905 Td /F5 9.8 Tf [(Parallelism)] TJ ET
BT 73.918 573.905 Td /F1 9.8 Tf [( or the number of alternative approaches explored to solve a problem is known to improve the quality of the solution. )] TJ ET
BT 26.250 562.000 Td /F1 9.8 Tf [(Abernathy and Rosenbloom \(1968\) showed that it is common in technological development to explore several approaches so )] TJ ET
BT 26.250 550.095 Td /F1 9.8 Tf [(that the best approach can be chosen. More recently, problem-solving efficiency in complex and novel environments is )] TJ ET
BT 26.250 538.191 Td /F1 9.8 Tf [(associated with a broad exploration and framing of the solution space, reaching across multiple knowledge domains \(Schrader )] TJ ET
BT 26.250 526.286 Td /F5 9.8 Tf [(et al)] TJ ET
BT 44.677 526.286 Td /F1 9.8 Tf [(., 1992; Iansiti, 1998; Golder & Tellis, 2004\). Toyota tries not to converge too quickly on a “best guess” solution for its new )] TJ ET
BT 26.250 514.381 Td /F1 9.8 Tf [(designs. Instead, the company has a high regard for learning on multiple ideas in parallel, using a “set-based” approach to )] TJ ET
BT 26.250 502.476 Td /F1 9.8 Tf [(product development \(Sobek II )] TJ ET
BT 161.736 502.476 Td /F5 9.8 Tf [(et al)] TJ ET
BT 180.163 502.476 Td /F1 9.8 Tf [(., 1999\), improving the quality of the commercialized product. More recent research )] TJ ET
BT 26.250 490.572 Td /F1 9.8 Tf [(concludes that set-based coordination should be emphasized if either starvation costs or the cost of carrying multiple design )] TJ ET
BT 26.250 478.667 Td /F1 9.8 Tf [(options in parallel are low \(Terwiesch )] TJ ET
BT 189.338 478.667 Td /F5 9.8 Tf [(et al)] TJ ET
BT 207.766 478.667 Td /F1 9.8 Tf [(., 2002\). Loch )] TJ ET
BT 270.097 478.667 Td /F5 9.8 Tf [(et al)] TJ ET
BT 288.525 478.667 Td /F1 9.8 Tf [(.’s \(2001\) analysis of experimentation policies shows that the )] TJ ET
BT 26.250 466.762 Td /F1 9.8 Tf [(optimal mix of parallel and sequential testing depends on the ratio of the financial cost and the cost of testing. A recent analysis )] TJ ET
BT 26.250 454.857 Td /F1 9.8 Tf [(of NPD decision making based on a pharmaceutical product development case developed closed-form solutions for the optimal )] TJ ET
BT 26.250 442.953 Td /F1 9.8 Tf [(number of concept tests to be conducted under profit uncertainty \(Dahan & Mendelson, 2001\). Their key finding was that the )] TJ ET
BT 26.250 431.048 Td /F1 9.8 Tf [(optimal number of concept tests depends not only on the cost of testing and the scale of uncertainty, but also on the distribution )] TJ ET
BT 26.250 419.143 Td /F1 9.8 Tf [(shape of that uncertainty.)] TJ ET
BT 26.250 399.738 Td /F1 9.8 Tf [(Finally, Mena )] TJ ET
BT 86.934 399.738 Td /F5 9.8 Tf [(et al)] TJ ET
BT 105.361 399.738 Td /F1 9.8 Tf [(. \(2001\) use a genetic algorithm-based simulation model to understand how the concepts of evolution and )] TJ ET
BT 26.250 387.834 Td /F1 9.8 Tf [(learning can be used to improve the product development process. They found that diversity of designs explored and process )] TJ ET
BT 26.250 375.929 Td /F1 9.8 Tf [(duration are by far the most important parameters in finding a satisfactory solution, while other factors were found to be )] TJ ET
BT 26.250 364.024 Td /F1 9.8 Tf [(negligible.)] TJ ET
BT 26.250 344.619 Td /F1 9.8 Tf [(From the above as well as a recent literature review and meta-analysis on relationships between integrated product )] TJ ET
BT 26.250 332.715 Td /F1 9.8 Tf [(development \(IPD\) characteristics and project performance \(Gerwin & Barrowman, 2002\), we conclude that present )] TJ ET
BT 26.250 320.810 Td /F1 9.8 Tf [(experimentation behavior research strongly emphasizes the study of the impact of various experimentation strategies on )] TJ ET
BT 26.250 308.905 Td /F1 9.8 Tf [(development lead time and efficiency, performance variables that are typical for product development and innovation research. )] TJ ET
BT 26.250 297.000 Td /F1 9.8 Tf [(No study was found to contribute to the exploration of )] TJ ET
BT 259.294 297.000 Td /F5 9.8 Tf [(predictive)] TJ ET
BT 301.015 297.000 Td /F1 9.8 Tf [( performance of experimentation strategies.)] TJ ET
BT 26.250 277.596 Td /F1 9.8 Tf [(However, the need to improve the predictability of the experimentation process has recently been advocated by the )] TJ ET
BT 26.250 265.691 Td /F1 9.8 Tf [(pharmaceutical R&D community. Lesko )] TJ ET
BT 199.634 265.691 Td /F5 9.8 Tf [(et al)] TJ ET
BT 218.062 265.691 Td /F1 9.8 Tf [(. \(2000\) conclude that to get to better therapeutic agents with lower development )] TJ ET
BT 26.250 253.786 Td /F1 9.8 Tf [(risks, the pharmaceutical R&D process needs to move from an essentially empirical mode to a more mechanistic and predictive )] TJ ET
BT 26.250 241.881 Td /F1 9.8 Tf [(one. The general goal should be to integrate early knowledge gained during discovery into the drug development decision-)] TJ ET
BT 26.250 229.977 Td /F1 9.8 Tf [(making process. This approach would find failures faster, resulting in more economical and informative development programs. )] TJ ET
BT 26.250 218.072 Td /F1 9.8 Tf [(Or, more recently confirmed by Duyck in an influential paper, “The current lack of predictability not only represents a deficit in )] TJ ET
BT 26.250 206.167 Td /F1 9.8 Tf [(our knowledge base, but results in substantial opportunity cost, increased financial cost for therapeutic development, and limits )] TJ ET
BT 26.250 194.262 Td /F1 9.8 Tf [(on the potential impact of our basic research enterprise on public health” \(Duyck, 2003\).)] TJ ET
BT 26.250 174.858 Td /F1 9.8 Tf [(Therefore, in the following section, experimentation strategy in pharmaceutical discovery will be explored in one of the top-10 )] TJ ET
BT 26.250 162.953 Td /F1 9.8 Tf [(pharmaceutical R&D operations that we will further refer to as PharmaCo. This will form the empirical base for the simulation-)] TJ ET
BT 26.250 151.048 Td /F1 9.8 Tf [(based study.)] TJ ET
BT 26.250 114.446 Td /F4 12.0 Tf [(Exploring experimentation strategy in pharmaceutical discovery)] TJ ET
BT 26.250 77.294 Td /F4 12.0 Tf [(The pharmaceutical discovery process)] TJ ET
BT 26.250 57.339 Td /F1 9.8 Tf [(Before exploring the predictive performance of experimentation strategies in pharmaceutical discovery, the process needs to be )] TJ ET
BT 26.250 45.435 Td /F1 9.8 Tf [(understood. Figure 1 provides an overview of a typical drug discovery process as found in a major R&D operation[1]. Once a )] TJ ET
Q
q
15.000 31.149 577.500 745.851 re W n
0.271 0.267 0.267 rg
BT 26.250 767.476 Td /F1 9.8 Tf [(However, current experimentation research focuses mainly on strategies aimed at accelerating product development lead-time )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(and enhancing efficiency. Thus, strategies are proposed to compress the experimentation process using concurrent engineering )] TJ ET
BT 26.250 743.667 Td /F1 9.8 Tf [(\(Krishnan )] TJ ET
BT 70.681 743.667 Td /F5 9.8 Tf [(et al)] TJ ET
BT 89.108 743.667 Td /F1 9.8 Tf [(., 1997; Smith & Eppinger, 1997a, 1997b; Loch & Terwiesch, 1998; Roemer )] TJ ET
BT 418.073 743.667 Td /F5 9.8 Tf [(et al)] TJ ET
BT 436.501 743.667 Td /F1 9.8 Tf [(., 2000; Mihm )] TJ ET
BT 498.277 743.667 Td /F5 9.8 Tf [(et al)] TJ ET
BT 516.704 743.667 Td /F1 9.8 Tf [(., 2003\) or to )] TJ ET
BT 26.250 731.762 Td /F1 9.8 Tf [(make it more cost effective \(Thomke 1998a, 1998b\).)] TJ ET
BT 26.250 712.357 Td /F1 9.8 Tf [(Verganti \(1999\) argues for a )] TJ ET
BT 150.894 712.357 Td /F5 9.8 Tf [(front-loading)] TJ ET
BT 205.084 712.357 Td /F1 9.8 Tf [( mechanism he calls “planned flexibility,” which deals with uncertainty through early )] TJ ET
BT 26.250 700.452 Td /F1 9.8 Tf [(identification of specific critical areas of a given project and early planning for reaction measures, leading to improved time to )] TJ ET
BT 26.250 688.548 Td /F1 9.8 Tf [(market and product quality. Also, Thomke and Fujimoto’s \(2000\) case study at Toyota gives field support for front loading as an )] TJ ET
BT 26.250 676.643 Td /F1 9.8 Tf [(important methodology to accelerate product development. “Enlightened” experimenters use front-loaded development, )] TJ ET
BT 26.250 664.738 Td /F1 9.8 Tf [(exploiting early information to spot and solve problems as far upstream as possible \(Thomke, 2001\). Survey-based research of )] TJ ET
BT 26.250 652.833 Td /F1 9.8 Tf [(29 Internet software development projects \(MacCormack & Verganti, 2003\) also provides support for the front-loading benefits )] TJ ET
BT 26.250 640.929 Td /F1 9.8 Tf [(argument. Here, for projects facing greater uncertainty, early technical and market feedback had a stronger association with )] TJ ET
BT 26.250 629.024 Td /F1 9.8 Tf [(performance. Finally, in the late 1990s pharmaceutical and biotech companies also discovered the benefits of finding potential )] TJ ET
BT 26.250 617.119 Td /F1 9.8 Tf [(failure modes as early as possible in the development process. In a case study of Millennium Pharmaceuticals, it was shown )] TJ ET
BT 26.250 605.214 Td /F1 9.8 Tf [(how new technologies for experimentation can form the basis for fundamentally rethinking the innovation process by shifting )] TJ ET
BT 26.250 593.310 Td /F1 9.8 Tf [(failures to earlier phases \(Thomke, 2003\).)] TJ ET
BT 26.250 573.905 Td /F5 9.8 Tf [(Parallelism)] TJ ET
BT 73.918 573.905 Td /F1 9.8 Tf [( or the number of alternative approaches explored to solve a problem is known to improve the quality of the solution. )] TJ ET
BT 26.250 562.000 Td /F1 9.8 Tf [(Abernathy and Rosenbloom \(1968\) showed that it is common in technological development to explore several approaches so )] TJ ET
BT 26.250 550.095 Td /F1 9.8 Tf [(that the best approach can be chosen. More recently, problem-solving efficiency in complex and novel environments is )] TJ ET
BT 26.250 538.191 Td /F1 9.8 Tf [(associated with a broad exploration and framing of the solution space, reaching across multiple knowledge domains \(Schrader )] TJ ET
BT 26.250 526.286 Td /F5 9.8 Tf [(et al)] TJ ET
BT 44.677 526.286 Td /F1 9.8 Tf [(., 1992; Iansiti, 1998; Golder & Tellis, 2004\). Toyota tries not to converge too quickly on a “best guess” solution for its new )] TJ ET
BT 26.250 514.381 Td /F1 9.8 Tf [(designs. Instead, the company has a high regard for learning on multiple ideas in parallel, using a “set-based” approach to )] TJ ET
BT 26.250 502.476 Td /F1 9.8 Tf [(product development \(Sobek II )] TJ ET
BT 161.736 502.476 Td /F5 9.8 Tf [(et al)] TJ ET
BT 180.163 502.476 Td /F1 9.8 Tf [(., 1999\), improving the quality of the commercialized product. More recent research )] TJ ET
BT 26.250 490.572 Td /F1 9.8 Tf [(concludes that set-based coordination should be emphasized if either starvation costs or the cost of carrying multiple design )] TJ ET
BT 26.250 478.667 Td /F1 9.8 Tf [(options in parallel are low \(Terwiesch )] TJ ET
BT 189.338 478.667 Td /F5 9.8 Tf [(et al)] TJ ET
BT 207.766 478.667 Td /F1 9.8 Tf [(., 2002\). Loch )] TJ ET
BT 270.097 478.667 Td /F5 9.8 Tf [(et al)] TJ ET
BT 288.525 478.667 Td /F1 9.8 Tf [(.’s \(2001\) analysis of experimentation policies shows that the )] TJ ET
BT 26.250 466.762 Td /F1 9.8 Tf [(optimal mix of parallel and sequential testing depends on the ratio of the financial cost and the cost of testing. A recent analysis )] TJ ET
BT 26.250 454.857 Td /F1 9.8 Tf [(of NPD decision making based on a pharmaceutical product development case developed closed-form solutions for the optimal )] TJ ET
BT 26.250 442.953 Td /F1 9.8 Tf [(number of concept tests to be conducted under profit uncertainty \(Dahan & Mendelson, 2001\). Their key finding was that the )] TJ ET
BT 26.250 431.048 Td /F1 9.8 Tf [(optimal number of concept tests depends not only on the cost of testing and the scale of uncertainty, but also on the distribution )] TJ ET
BT 26.250 419.143 Td /F1 9.8 Tf [(shape of that uncertainty.)] TJ ET
BT 26.250 399.738 Td /F1 9.8 Tf [(Finally, Mena )] TJ ET
BT 86.934 399.738 Td /F5 9.8 Tf [(et al)] TJ ET
BT 105.361 399.738 Td /F1 9.8 Tf [(. \(2001\) use a genetic algorithm-based simulation model to understand how the concepts of evolution and )] TJ ET
BT 26.250 387.834 Td /F1 9.8 Tf [(learning can be used to improve the product development process. They found that diversity of designs explored and process )] TJ ET
BT 26.250 375.929 Td /F1 9.8 Tf [(duration are by far the most important parameters in finding a satisfactory solution, while other factors were found to be )] TJ ET
BT 26.250 364.024 Td /F1 9.8 Tf [(negligible.)] TJ ET
BT 26.250 344.619 Td /F1 9.8 Tf [(From the above as well as a recent literature review and meta-analysis on relationships between integrated product )] TJ ET
BT 26.250 332.715 Td /F1 9.8 Tf [(development \(IPD\) characteristics and project performance \(Gerwin & Barrowman, 2002\), we conclude that present )] TJ ET
BT 26.250 320.810 Td /F1 9.8 Tf [(experimentation behavior research strongly emphasizes the study of the impact of various experimentation strategies on )] TJ ET
BT 26.250 308.905 Td /F1 9.8 Tf [(development lead time and efficiency, performance variables that are typical for product development and innovation research. )] TJ ET
BT 26.250 297.000 Td /F1 9.8 Tf [(No study was found to contribute to the exploration of )] TJ ET
BT 259.294 297.000 Td /F5 9.8 Tf [(predictive)] TJ ET
BT 301.015 297.000 Td /F1 9.8 Tf [( performance of experimentation strategies.)] TJ ET
BT 26.250 277.596 Td /F1 9.8 Tf [(However, the need to improve the predictability of the experimentation process has recently been advocated by the )] TJ ET
BT 26.250 265.691 Td /F1 9.8 Tf [(pharmaceutical R&D community. Lesko )] TJ ET
BT 199.634 265.691 Td /F5 9.8 Tf [(et al)] TJ ET
BT 218.062 265.691 Td /F1 9.8 Tf [(. \(2000\) conclude that to get to better therapeutic agents with lower development )] TJ ET
BT 26.250 253.786 Td /F1 9.8 Tf [(risks, the pharmaceutical R&D process needs to move from an essentially empirical mode to a more mechanistic and predictive )] TJ ET
BT 26.250 241.881 Td /F1 9.8 Tf [(one. The general goal should be to integrate early knowledge gained during discovery into the drug development decision-)] TJ ET
BT 26.250 229.977 Td /F1 9.8 Tf [(making process. This approach would find failures faster, resulting in more economical and informative development programs. )] TJ ET
BT 26.250 218.072 Td /F1 9.8 Tf [(Or, more recently confirmed by Duyck in an influential paper, “The current lack of predictability not only represents a deficit in )] TJ ET
BT 26.250 206.167 Td /F1 9.8 Tf [(our knowledge base, but results in substantial opportunity cost, increased financial cost for therapeutic development, and limits )] TJ ET
BT 26.250 194.262 Td /F1 9.8 Tf [(on the potential impact of our basic research enterprise on public health” \(Duyck, 2003\).)] TJ ET
BT 26.250 174.858 Td /F1 9.8 Tf [(Therefore, in the following section, experimentation strategy in pharmaceutical discovery will be explored in one of the top-10 )] TJ ET
BT 26.250 162.953 Td /F1 9.8 Tf [(pharmaceutical R&D operations that we will further refer to as PharmaCo. This will form the empirical base for the simulation-)] TJ ET
BT 26.250 151.048 Td /F1 9.8 Tf [(based study.)] TJ ET
BT 26.250 114.446 Td /F4 12.0 Tf [(Exploring experimentation strategy in pharmaceutical discovery)] TJ ET
BT 26.250 77.294 Td /F4 12.0 Tf [(The pharmaceutical discovery process)] TJ ET
BT 26.250 57.339 Td /F1 9.8 Tf [(Before exploring the predictive performance of experimentation strategies in pharmaceutical discovery, the process needs to be )] TJ ET
BT 26.250 45.435 Td /F1 9.8 Tf [(understood. Figure 1 provides an overview of a typical drug discovery process as found in a major R&D operation[1]. Once a )] TJ ET
Q
q
0.000 0.000 0.000 rg
BT 291.710 19.825 Td /F1 11.0 Tf [(2)] TJ ET
BT 25.000 19.825 Td /F1 11.0 Tf [(Emergence: Complexity and Organization)] TJ ET
Q
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0.271 0.267 0.267 rg
BT 26.250 767.476 Td /F1 9.8 Tf [(biological target is validated during “Target Identification & Validation,” the pharmaceutical drug discovery process aims to find a )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(therapeutic agent with positive effect on this scientifically and commercially interesting target. It proceeds in essentially two )] TJ ET
BT 26.250 743.667 Td /F1 9.8 Tf [(stages: screening and optimization.)] TJ ET
BT 26.250 724.262 Td /F1 9.8 Tf [(First, during “High Throughput Screening” \(HTS\) a lead molecule is selected from a diverse compound collection, constituting a )] TJ ET
BT 26.250 712.357 Td /F1 9.8 Tf [(chemical library. A typical major drug company will have hundreds of thousands to millions of compounds in its collection, )] TJ ET
BT 26.250 700.452 Td /F1 9.8 Tf [(typically valued at $50—140 million or more \(Young )] TJ ET
BT 251.670 700.452 Td /F5 9.8 Tf [(et al)] TJ ET
BT 270.097 700.452 Td /F1 9.8 Tf [(., 1997\). Alternatively, combinatorial chemistry has come into recent )] TJ ET
BT 26.250 688.548 Td /F1 9.8 Tf [(use to create large collections of candidate compounds )] TJ ET
BT 266.870 688.548 Td /F5 9.8 Tf [(screened)] TJ ET
BT 306.972 688.548 Td /F1 9.8 Tf [( for their effect on the biological target \(Thomke )] TJ ET
BT 513.984 688.548 Td /F5 9.8 Tf [(et al)] TJ ET
BT 532.411 688.548 Td /F1 9.8 Tf [(., 1998\). )] TJ ET
BT 26.250 676.643 Td /F1 9.8 Tf [(The candidate compound or “Hit” coming out of this screening process will be modestly potent. Therefore, in a second stage the )] TJ ET
BT 26.250 664.738 Td /F1 9.8 Tf [(candidate compound will be )] TJ ET
BT 149.266 664.738 Td /F5 9.8 Tf [(optimized)] TJ ET
BT 190.986 664.738 Td /F1 9.8 Tf [( by synthetically adding or removing parts using medicinal chemistry. This proceeds in )] TJ ET
BT 26.250 652.833 Td /F1 9.8 Tf [(two steps: lab-based — in-vitro — characterization or Hit-to-Lead \(H2L\), and animal-based — in-vivo — Lead Optimization \(LO\). )] TJ ET
BT 26.250 640.929 Td /F1 9.8 Tf [(This results in a candidate compound becoming more and more complex in structure as a result of the discovery process. )] TJ ET
BT 26.250 629.024 Td /F1 9.8 Tf [(Essentially, “Hit” structures serve as initial starting points to be optimized into “drug-like” leads \(Oprea et al., 2001\). The final )] TJ ET
BT 26.250 617.119 Td /F1 9.8 Tf [(product is a new molecular entity \(NME\) to be transferred into clinical development for further testing in humans.)] TJ ET
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BT 35.250 588.469 Td /F1 9.8 Tf [(f5bb5bc4-c61a-517c-db67-088dd4ce7e95)] TJ ET
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BT 35.250 562.088 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 552.088 Td /F1 8.0 Tf [(https://journal.emergentpublications.com/wp-content/uploads/2015/11/f5bb5bc4-c61a-517c-db67-088dd4ce7e95-300x202.png)] TJ ET
q
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BT 35.250 533.964 Td /F4 9.8 Tf [(Fig. 1: Alternative discovery experimentation strategies considered)] TJ ET
Q
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BT 35.250 495.565 Td /F1 9.8 Tf [(f53c1336-d9bd-175b-ba58-32fd8004596d)] TJ ET
0.500 0.500 0.500 rg
BT 35.250 469.183 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 459.183 Td /F1 8.0 Tf [(https://journal.emergentpublications.com/wp-content/uploads/2015/11/f53c1336-d9bd-175b-ba58-32fd8004596d-300x141.png)] TJ ET
q
35.250 432.679 537.000 17.905 re W n
0.271 0.267 0.267 rg
BT 35.250 441.060 Td /F4 9.8 Tf [(Fig. 2: Emulating the bio-availability surrogate marker chain)] TJ ET
Q
BT 26.250 411.905 Td /F1 9.8 Tf [(The practitioner literature \(Macdonald & Smith, 2001; Oprea, 2002; DeWitte, 2002\) develops a scientific viewpoint on a number )] TJ ET
BT 26.250 400.000 Td /F1 9.8 Tf [(of experimental designs available to run this screening and optimization drug discovery process. It can be summarized with )] TJ ET
BT 26.250 388.095 Td /F1 9.8 Tf [(regard to the level of )] TJ ET
BT 117.841 388.095 Td /F5 9.8 Tf [(front loading)] TJ ET
BT 171.496 388.095 Td /F1 9.8 Tf [( and )] TJ ET
BT 193.180 388.095 Td /F5 9.8 Tf [(parallelization)] TJ ET
BT 252.781 388.095 Td /F1 9.8 Tf [( that is used in the discovery process \(Figure 1\), the latter indicating the )] TJ ET
BT 26.250 376.191 Td /F1 9.8 Tf [(shape of the discovery funnel.)] TJ ET
BT 26.250 356.786 Td /F1 9.8 Tf [(The level of parallelism can be measured by the number of candidate classes[2] retained for a drug target during the various )] TJ ET
BT 26.250 344.881 Td /F1 9.8 Tf [(stages of the discovery process, which reflects the shape of the discovery funnel. Typically in PharmaCo one to five classes )] TJ ET
BT 26.250 332.976 Td /F1 9.8 Tf [(showing activity against a target are selected in HTS screening, and one to three classes are further analyzed in H2L and )] TJ ET
BT 26.250 321.072 Td /F1 9.8 Tf [(carried through to LO. A narrow funnel, then, would be characterized by the couple \(HTS, H2L/LO\) = \(1, 1\). Conversely, a broad )] TJ ET
BT 26.250 309.167 Td /F1 9.8 Tf [(funnel would be characterized by the couple \(HTS, H2L/LO\) = \(5, 3\). The main cost driver is the number of classes carried )] TJ ET
BT 26.250 297.262 Td /F1 9.8 Tf [(through to LO.)] TJ ET
BT 26.250 277.857 Td /F1 9.8 Tf [(The level of front loading can be measured by the number of variables — P and/or B[3] — considered at differing stages during )] TJ ET
BT 26.250 265.953 Td /F1 9.8 Tf [(Discovery; the most front-loaded strategy uses P and B from HTS on, the least front-loaded strategy only uses P during the )] TJ ET
BT 26.250 254.048 Td /F1 9.8 Tf [(entire process, disregarding B.)] TJ ET
0.965 0.965 0.965 rg
26.250 158.762 555.000 85.405 re f
0.267 0.267 0.267 rg
0.267 0.267 0.267 RG
26.250 244.167 m 581.250 244.167 l 581.250 243.417 l 26.250 243.417 l f
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BT 35.250 225.398 Td /F1 9.8 Tf [(de5c690a-47ae-f8fb-dfd7-7fd010fce0d6)] TJ ET
0.500 0.500 0.500 rg
BT 35.250 199.017 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 189.017 Td /F1 8.0 Tf [(https://journal.emergentpublications.com/wp-content/uploads/2015/11/de5c690a-47ae-f8fb-dfd7-7fd010fce0d6-300x140.png)] TJ ET
q
35.250 162.512 537.000 17.905 re W n
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BT 35.250 170.893 Td /F4 9.8 Tf [(Fig. 3: Alternative front-loaded strategies for discovery research experimentation)] TJ ET
Q
BT 26.250 141.738 Td /F1 9.8 Tf [(Finally, exploratory case-study analysis showed that along the discovery process scientists use surrogate measures for the )] TJ ET
BT 26.250 129.834 Td /F1 9.8 Tf [(variable they are ultimately interested in.)] TJ ET
BT 26.250 110.429 Td /F1 9.8 Tf [(This is because scientific methods or surrogate markers for an ultimate variable of interest become more accurate as one )] TJ ET
BT 26.250 98.524 Td /F1 9.8 Tf [(proceeds in the discovery phases, which explains the funnel shape above. As an example, bio-availability in humans — b in )] TJ ET
BT 26.250 86.619 Td /F1 9.8 Tf [(Figure 2 — is first measured in HTS using a rough decision rule set, to get an initial idea about this critical compound property )] TJ ET
BT 26.250 74.715 Td /F1 9.8 Tf [(based on virtual bioinformatics data only. Then, in H2L a more refined in-vitro method is used, and the final decision in LO is )] TJ ET
BT 26.250 62.810 Td /F1 9.8 Tf [(made using animal tests.)] TJ ET
BT 26.250 43.405 Td /F1 9.8 Tf [(Now, for this study we define the )] TJ ET
BT 169.858 43.405 Td /F5 9.8 Tf [(predictive power)] TJ ET
BT 240.838 43.405 Td /F1 9.8 Tf [( of this surrogate marker chain, given by the correlation of the successive )] TJ ET
Q
q
15.000 29.120 577.500 747.880 re W n
0.271 0.267 0.267 rg
BT 26.250 767.476 Td /F1 9.8 Tf [(biological target is validated during “Target Identification & Validation,” the pharmaceutical drug discovery process aims to find a )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(therapeutic agent with positive effect on this scientifically and commercially interesting target. It proceeds in essentially two )] TJ ET
BT 26.250 743.667 Td /F1 9.8 Tf [(stages: screening and optimization.)] TJ ET
BT 26.250 724.262 Td /F1 9.8 Tf [(First, during “High Throughput Screening” \(HTS\) a lead molecule is selected from a diverse compound collection, constituting a )] TJ ET
BT 26.250 712.357 Td /F1 9.8 Tf [(chemical library. A typical major drug company will have hundreds of thousands to millions of compounds in its collection, )] TJ ET
BT 26.250 700.452 Td /F1 9.8 Tf [(typically valued at $50—140 million or more \(Young )] TJ ET
BT 251.670 700.452 Td /F5 9.8 Tf [(et al)] TJ ET
BT 270.097 700.452 Td /F1 9.8 Tf [(., 1997\). Alternatively, combinatorial chemistry has come into recent )] TJ ET
BT 26.250 688.548 Td /F1 9.8 Tf [(use to create large collections of candidate compounds )] TJ ET
BT 266.870 688.548 Td /F5 9.8 Tf [(screened)] TJ ET
BT 306.972 688.548 Td /F1 9.8 Tf [( for their effect on the biological target \(Thomke )] TJ ET
BT 513.984 688.548 Td /F5 9.8 Tf [(et al)] TJ ET
BT 532.411 688.548 Td /F1 9.8 Tf [(., 1998\). )] TJ ET
BT 26.250 676.643 Td /F1 9.8 Tf [(The candidate compound or “Hit” coming out of this screening process will be modestly potent. Therefore, in a second stage the )] TJ ET
BT 26.250 664.738 Td /F1 9.8 Tf [(candidate compound will be )] TJ ET
BT 149.266 664.738 Td /F5 9.8 Tf [(optimized)] TJ ET
BT 190.986 664.738 Td /F1 9.8 Tf [( by synthetically adding or removing parts using medicinal chemistry. This proceeds in )] TJ ET
BT 26.250 652.833 Td /F1 9.8 Tf [(two steps: lab-based — in-vitro — characterization or Hit-to-Lead \(H2L\), and animal-based — in-vivo — Lead Optimization \(LO\). )] TJ ET
BT 26.250 640.929 Td /F1 9.8 Tf [(This results in a candidate compound becoming more and more complex in structure as a result of the discovery process. )] TJ ET
BT 26.250 629.024 Td /F1 9.8 Tf [(Essentially, “Hit” structures serve as initial starting points to be optimized into “drug-like” leads \(Oprea et al., 2001\). The final )] TJ ET
BT 26.250 617.119 Td /F1 9.8 Tf [(product is a new molecular entity \(NME\) to be transferred into clinical development for further testing in humans.)] TJ ET
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BT 35.250 552.088 Td /F1 8.0 Tf [(https://journal.emergentpublications.com/wp-content/uploads/2015/11/f5bb5bc4-c61a-517c-db67-088dd4ce7e95-300x202.png)] TJ ET
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BT 35.250 533.964 Td /F4 9.8 Tf [(Fig. 1: Alternative discovery experimentation strategies considered)] TJ ET
Q
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BT 35.250 441.060 Td /F4 9.8 Tf [(Fig. 2: Emulating the bio-availability surrogate marker chain)] TJ ET
Q
BT 26.250 411.905 Td /F1 9.8 Tf [(The practitioner literature \(Macdonald & Smith, 2001; Oprea, 2002; DeWitte, 2002\) develops a scientific viewpoint on a number )] TJ ET
BT 26.250 400.000 Td /F1 9.8 Tf [(of experimental designs available to run this screening and optimization drug discovery process. It can be summarized with )] TJ ET
BT 26.250 388.095 Td /F1 9.8 Tf [(regard to the level of )] TJ ET
BT 117.841 388.095 Td /F5 9.8 Tf [(front loading)] TJ ET
BT 171.496 388.095 Td /F1 9.8 Tf [( and )] TJ ET
BT 193.180 388.095 Td /F5 9.8 Tf [(parallelization)] TJ ET
BT 252.781 388.095 Td /F1 9.8 Tf [( that is used in the discovery process \(Figure 1\), the latter indicating the )] TJ ET
BT 26.250 376.191 Td /F1 9.8 Tf [(shape of the discovery funnel.)] TJ ET
BT 26.250 356.786 Td /F1 9.8 Tf [(The level of parallelism can be measured by the number of candidate classes[2] retained for a drug target during the various )] TJ ET
BT 26.250 344.881 Td /F1 9.8 Tf [(stages of the discovery process, which reflects the shape of the discovery funnel. Typically in PharmaCo one to five classes )] TJ ET
BT 26.250 332.976 Td /F1 9.8 Tf [(showing activity against a target are selected in HTS screening, and one to three classes are further analyzed in H2L and )] TJ ET
BT 26.250 321.072 Td /F1 9.8 Tf [(carried through to LO. A narrow funnel, then, would be characterized by the couple \(HTS, H2L/LO\) = \(1, 1\). Conversely, a broad )] TJ ET
BT 26.250 309.167 Td /F1 9.8 Tf [(funnel would be characterized by the couple \(HTS, H2L/LO\) = \(5, 3\). The main cost driver is the number of classes carried )] TJ ET
BT 26.250 297.262 Td /F1 9.8 Tf [(through to LO.)] TJ ET
BT 26.250 277.857 Td /F1 9.8 Tf [(The level of front loading can be measured by the number of variables — P and/or B[3] — considered at differing stages during )] TJ ET
BT 26.250 265.953 Td /F1 9.8 Tf [(Discovery; the most front-loaded strategy uses P and B from HTS on, the least front-loaded strategy only uses P during the )] TJ ET
BT 26.250 254.048 Td /F1 9.8 Tf [(entire process, disregarding B.)] TJ ET
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BT 35.250 189.017 Td /F1 8.0 Tf [(https://journal.emergentpublications.com/wp-content/uploads/2015/11/de5c690a-47ae-f8fb-dfd7-7fd010fce0d6-300x140.png)] TJ ET
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BT 35.250 170.893 Td /F4 9.8 Tf [(Fig. 3: Alternative front-loaded strategies for discovery research experimentation)] TJ ET
Q
BT 26.250 141.738 Td /F1 9.8 Tf [(Finally, exploratory case-study analysis showed that along the discovery process scientists use surrogate measures for the )] TJ ET
BT 26.250 129.834 Td /F1 9.8 Tf [(variable they are ultimately interested in.)] TJ ET
BT 26.250 110.429 Td /F1 9.8 Tf [(This is because scientific methods or surrogate markers for an ultimate variable of interest become more accurate as one )] TJ ET
BT 26.250 98.524 Td /F1 9.8 Tf [(proceeds in the discovery phases, which explains the funnel shape above. As an example, bio-availability in humans — b in )] TJ ET
BT 26.250 86.619 Td /F1 9.8 Tf [(Figure 2 — is first measured in HTS using a rough decision rule set, to get an initial idea about this critical compound property )] TJ ET
BT 26.250 74.715 Td /F1 9.8 Tf [(based on virtual bioinformatics data only. Then, in H2L a more refined in-vitro method is used, and the final decision in LO is )] TJ ET
BT 26.250 62.810 Td /F1 9.8 Tf [(made using animal tests.)] TJ ET
BT 26.250 43.405 Td /F1 9.8 Tf [(Now, for this study we define the )] TJ ET
BT 169.858 43.405 Td /F5 9.8 Tf [(predictive power)] TJ ET
BT 240.838 43.405 Td /F1 9.8 Tf [( of this surrogate marker chain, given by the correlation of the successive )] TJ ET
Q
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BT 26.250 767.476 Td /F1 9.8 Tf [(biological target is validated during “Target Identification & Validation,” the pharmaceutical drug discovery process aims to find a )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(therapeutic agent with positive effect on this scientifically and commercially interesting target. It proceeds in essentially two )] TJ ET
BT 26.250 743.667 Td /F1 9.8 Tf [(stages: screening and optimization.)] TJ ET
BT 26.250 724.262 Td /F1 9.8 Tf [(First, during “High Throughput Screening” \(HTS\) a lead molecule is selected from a diverse compound collection, constituting a )] TJ ET
BT 26.250 712.357 Td /F1 9.8 Tf [(chemical library. A typical major drug company will have hundreds of thousands to millions of compounds in its collection, )] TJ ET
BT 26.250 700.452 Td /F1 9.8 Tf [(typically valued at $50—140 million or more \(Young )] TJ ET
BT 251.670 700.452 Td /F5 9.8 Tf [(et al)] TJ ET
BT 270.097 700.452 Td /F1 9.8 Tf [(., 1997\). Alternatively, combinatorial chemistry has come into recent )] TJ ET
BT 26.250 688.548 Td /F1 9.8 Tf [(use to create large collections of candidate compounds )] TJ ET
BT 266.870 688.548 Td /F5 9.8 Tf [(screened)] TJ ET
BT 306.972 688.548 Td /F1 9.8 Tf [( for their effect on the biological target \(Thomke )] TJ ET
BT 513.984 688.548 Td /F5 9.8 Tf [(et al)] TJ ET
BT 532.411 688.548 Td /F1 9.8 Tf [(., 1998\). )] TJ ET
BT 26.250 676.643 Td /F1 9.8 Tf [(The candidate compound or “Hit” coming out of this screening process will be modestly potent. Therefore, in a second stage the )] TJ ET
BT 26.250 664.738 Td /F1 9.8 Tf [(candidate compound will be )] TJ ET
BT 149.266 664.738 Td /F5 9.8 Tf [(optimized)] TJ ET
BT 190.986 664.738 Td /F1 9.8 Tf [( by synthetically adding or removing parts using medicinal chemistry. This proceeds in )] TJ ET
BT 26.250 652.833 Td /F1 9.8 Tf [(two steps: lab-based — in-vitro — characterization or Hit-to-Lead \(H2L\), and animal-based — in-vivo — Lead Optimization \(LO\). )] TJ ET
BT 26.250 640.929 Td /F1 9.8 Tf [(This results in a candidate compound becoming more and more complex in structure as a result of the discovery process. )] TJ ET
BT 26.250 629.024 Td /F1 9.8 Tf [(Essentially, “Hit” structures serve as initial starting points to be optimized into “drug-like” leads \(Oprea et al., 2001\). The final )] TJ ET
BT 26.250 617.119 Td /F1 9.8 Tf [(product is a new molecular entity \(NME\) to be transferred into clinical development for further testing in humans.)] TJ ET
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BT 35.250 533.964 Td /F4 9.8 Tf [(Fig. 1: Alternative discovery experimentation strategies considered)] TJ ET
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BT 35.250 441.060 Td /F4 9.8 Tf [(Fig. 2: Emulating the bio-availability surrogate marker chain)] TJ ET
Q
BT 26.250 411.905 Td /F1 9.8 Tf [(The practitioner literature \(Macdonald & Smith, 2001; Oprea, 2002; DeWitte, 2002\) develops a scientific viewpoint on a number )] TJ ET
BT 26.250 400.000 Td /F1 9.8 Tf [(of experimental designs available to run this screening and optimization drug discovery process. It can be summarized with )] TJ ET
BT 26.250 388.095 Td /F1 9.8 Tf [(regard to the level of )] TJ ET
BT 117.841 388.095 Td /F5 9.8 Tf [(front loading)] TJ ET
BT 171.496 388.095 Td /F1 9.8 Tf [( and )] TJ ET
BT 193.180 388.095 Td /F5 9.8 Tf [(parallelization)] TJ ET
BT 252.781 388.095 Td /F1 9.8 Tf [( that is used in the discovery process \(Figure 1\), the latter indicating the )] TJ ET
BT 26.250 376.191 Td /F1 9.8 Tf [(shape of the discovery funnel.)] TJ ET
BT 26.250 356.786 Td /F1 9.8 Tf [(The level of parallelism can be measured by the number of candidate classes[2] retained for a drug target during the various )] TJ ET
BT 26.250 344.881 Td /F1 9.8 Tf [(stages of the discovery process, which reflects the shape of the discovery funnel. Typically in PharmaCo one to five classes )] TJ ET
BT 26.250 332.976 Td /F1 9.8 Tf [(showing activity against a target are selected in HTS screening, and one to three classes are further analyzed in H2L and )] TJ ET
BT 26.250 321.072 Td /F1 9.8 Tf [(carried through to LO. A narrow funnel, then, would be characterized by the couple \(HTS, H2L/LO\) = \(1, 1\). Conversely, a broad )] TJ ET
BT 26.250 309.167 Td /F1 9.8 Tf [(funnel would be characterized by the couple \(HTS, H2L/LO\) = \(5, 3\). The main cost driver is the number of classes carried )] TJ ET
BT 26.250 297.262 Td /F1 9.8 Tf [(through to LO.)] TJ ET
BT 26.250 277.857 Td /F1 9.8 Tf [(The level of front loading can be measured by the number of variables — P and/or B[3] — considered at differing stages during )] TJ ET
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BT 26.250 254.048 Td /F1 9.8 Tf [(entire process, disregarding B.)] TJ ET
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BT 35.250 170.893 Td /F4 9.8 Tf [(Fig. 3: Alternative front-loaded strategies for discovery research experimentation)] TJ ET
Q
BT 26.250 141.738 Td /F1 9.8 Tf [(Finally, exploratory case-study analysis showed that along the discovery process scientists use surrogate measures for the )] TJ ET
BT 26.250 129.834 Td /F1 9.8 Tf [(variable they are ultimately interested in.)] TJ ET
BT 26.250 110.429 Td /F1 9.8 Tf [(This is because scientific methods or surrogate markers for an ultimate variable of interest become more accurate as one )] TJ ET
BT 26.250 98.524 Td /F1 9.8 Tf [(proceeds in the discovery phases, which explains the funnel shape above. As an example, bio-availability in humans — b in )] TJ ET
BT 26.250 86.619 Td /F1 9.8 Tf [(Figure 2 — is first measured in HTS using a rough decision rule set, to get an initial idea about this critical compound property )] TJ ET
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BT 26.250 43.405 Td /F1 9.8 Tf [(Now, for this study we define the )] TJ ET
BT 169.858 43.405 Td /F5 9.8 Tf [(predictive power)] TJ ET
BT 240.838 43.405 Td /F1 9.8 Tf [( of this surrogate marker chain, given by the correlation of the successive )] TJ ET
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BT 26.250 767.476 Td /F1 9.8 Tf [(measurement methods. The higher the correlation between the various methods in the chain, the tighter the chain, and the )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(better its predictive power will be. Within PharmaCo, a tightness level of 70% for all variables studied can be assumed.)] TJ ET
BT 26.250 718.969 Td /F4 12.0 Tf [(Discovery experimentation strategies)] TJ ET
BT 26.250 699.015 Td /F1 9.8 Tf [(The three front-loaded experimental strategies for discovery research considered for subsequent comparative analysis are )] TJ ET
BT 26.250 687.110 Td /F1 9.8 Tf [(summarized in Figure 3. The arrows indicate which property classes are used in the prototype compound optimization cycles to )] TJ ET
BT 26.250 675.205 Td /F1 9.8 Tf [(converge to an NME. A vertical arrow indicates that only biological activity \(P\) or potency and selectivity is optimized, an arrow )] TJ ET
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BT 26.250 651.396 Td /F1 9.8 Tf [(factorial optimization cycles.)] TJ ET
BT 26.250 631.991 Td /F1 9.8 Tf [(In the old )] TJ ET
BT 69.072 631.991 Td /F5 9.8 Tf [(paradigm)] TJ ET
BT 109.710 631.991 Td /F1 9.8 Tf [(, applied in the industry some time ago, discovery research was only concerned about biological activity or )] TJ ET
BT 26.250 620.086 Td /F1 9.8 Tf [(potency and selectivity of a candidate compound. Drug-likeness properties were only taken into account from pre-clinical )] TJ ET
BT 26.250 608.181 Td /F1 9.8 Tf [(research on, which is a time- and effort-consuming process \(Oprea, 2002\). This strategy was not entirely successful, since )] TJ ET
BT 26.250 596.277 Td /F1 9.8 Tf [(within the industry only one in ten candidate drugs succeeded through clinical trials to reach the market \(Drews, 1998\).)] TJ ET
BT 26.250 576.872 Td /F1 9.8 Tf [(In the )] TJ ET
BT 53.355 576.872 Td /F5 9.8 Tf [(front-loaded paradigm)] TJ ET
BT 148.730 576.872 Td /F1 9.8 Tf [(, both biological activity and pharmacokinetic properties are optimized simultaneously from the Hit-)] TJ ET
BT 26.250 564.967 Td /F1 9.8 Tf [(to-Lead \(H2L\) stage on. Here, confirmed hits series from HTS are prioritized and analyzed for problems. Progressive )] TJ ET
BT 26.250 553.062 Td /F1 9.8 Tf [(increments in biological activity )] TJ ET
BT 162.789 553.062 Td /F5 9.8 Tf [(and)] TJ ET
BT 179.052 553.062 Td /F1 9.8 Tf [( bio-availability are obtained by addressing the appropriate molecular determinants that )] TJ ET
BT 26.250 541.158 Td /F1 9.8 Tf [(define the desired compound characteristics.)] TJ ET
BT 26.250 521.753 Td /F1 9.8 Tf [(Also, this paradigm is more generous in promoting selected candidate compounds through the H2L and LO process. This )] TJ ET
BT 26.250 509.848 Td /F1 9.8 Tf [(argues against a too narrow idea funnel and means that more candidates will reach LO status. Although this is an expensive )] TJ ET
BT 26.250 497.943 Td /F1 9.8 Tf [(recommendation, it does prevent candidate eliminations due to tests that mimic reality with statistical inaccuracy, the latter )] TJ ET
BT 26.250 486.039 Td /F1 9.8 Tf [(compounded to the number of tests conducted \(DeWitte, 2002\). The idea, then, is to build up knowledge through to in-vivo of a )] TJ ET
BT 26.250 474.134 Td /F5 9.8 Tf [(series)] TJ ET
BT 52.253 474.134 Td /F1 9.8 Tf [( of candidates instead of going for a fast attrition based on “in-silico” or in-vitro tests with low predictive power, and )] TJ ET
BT 26.250 462.229 Td /F1 9.8 Tf [(running LO with a small subset of “winning” candidate series of compounds.)] TJ ET
BT 26.250 442.824 Td /F1 9.8 Tf [(Finally, )] TJ ET
BT 59.839 442.824 Td /F5 9.8 Tf [(early front loading)] TJ ET
BT 137.332 442.824 Td /F1 9.8 Tf [( takes the front-loaded paradigm even further, selecting the most promising classes based on the )] TJ ET
BT 26.250 430.920 Td /F1 9.8 Tf [(fullest multi-factorial picture available using virtual “in-silico” screening technology during HTS \(Oprea, 2002\).)] TJ ET
BT 26.250 411.515 Td /F1 9.8 Tf [(Generalizing, our theory-building effort leads us to define )] TJ ET
BT 273.910 411.515 Td /F5 9.8 Tf [(residual ambiguity)] TJ ET
BT 352.475 411.515 Td /F1 9.8 Tf [( as all non-characterized solution variables of a )] TJ ET
BT 26.250 399.610 Td /F1 9.8 Tf [(candidate solution concept, their causal relationships and/or related problem-solving mechanisms at a point in time during the )] TJ ET
BT 26.250 387.705 Td /F1 9.8 Tf [(emergence of the solution to an innovation problem. From the above, it will be clear that front-loaded experimentation strategies )] TJ ET
BT 26.250 375.801 Td /F1 9.8 Tf [(aim for more extensive candidate characterization or lower levels of residual ambiguity than old-paradigm strategies at the end )] TJ ET
BT 26.250 363.896 Td /F1 9.8 Tf [(of discovery before transferring the therapeutic candidate into clinical development.)] TJ ET
BT 26.250 344.491 Td /F1 9.8 Tf [(Now, to quantitatively evaluate predictive performance of discovery experimentation strategies, an analytical framework needs )] TJ ET
BT 26.250 332.586 Td /F1 9.8 Tf [(to be developed. This is the subject of the next section.)] TJ ET
BT 26.250 295.984 Td /F4 12.0 Tf [(Modeling experimentation strategies’ performance)] TJ ET
BT 26.250 258.832 Td /F4 12.0 Tf [(Predictive performance)] TJ ET
BT 26.250 238.878 Td /F1 9.8 Tf [(Predictive performance can be intuitively defined as the accuracy with which compounds are promoted to therapeutic candidate )] TJ ET
BT 26.250 226.973 Td /F1 9.8 Tf [(or NME[4] status, as confirmed by later clinical development. To build a quantitative model of experimentation strategy variables )] TJ ET
BT 26.250 215.068 Td /F1 9.8 Tf [(— levels of front loading and parallelism- influencing predictive performance — a more formal notation is required.)] TJ ET
BT 26.250 195.663 Td /F1 9.8 Tf [(Therefore, we distinguish between )] TJ ET
BT 177.443 195.663 Td /F5 9.8 Tf [(H)] TJ ET
BT 184.483 199.552 Td /F5 8.7 Tf [(+)] TJ ET
BT 189.544 195.663 Td /F1 9.8 Tf [( and )] TJ ET
BT 211.228 195.663 Td /F5 9.8 Tf [(H)] TJ ET
BT 218.268 199.552 Td /F5 8.7 Tf [(—)] TJ ET
BT 226.934 195.663 Td /F1 9.8 Tf [(, being numbers of compounds declared respectively as active or inactive by the )] TJ ET
BT 26.250 154.882 Td /F1 9.8 Tf [(discovery experimentation strategy. The former is a set of therapeutic candidates )] TJ ET
q
48.000 0 0 48.000 377.942 145.282 cm /I1 Do
Q
0.500 0.500 0.500 rg
BT 377.942 185.882 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 377.942 175.882 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5BH_i%5E%20%2B%20%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 425.942 154.882 Td /F1 9.8 Tf [( that tested positive throughout the )] TJ ET
BT 26.250 106.882 Td /F1 9.8 Tf [(optimization process. During this process, a – virtually infinite – number of candidates )] TJ ET
q
48.000 0 0 48.000 396.896 97.282 cm /I1 Do
Q
0.500 0.500 0.500 rg
BT 396.896 137.882 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 396.896 127.882 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5BH_j%5E%20-%20%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 444.896 106.882 Td /F1 9.8 Tf [( were eliminated from further )] TJ ET
BT 26.250 87.759 Td /F1 9.8 Tf [(optimization.)] TJ ET
BT 26.250 39.478 Td /F1 9.8 Tf [(Then, active candidates )] TJ ET
Q
q
15.000 29.878 577.500 747.122 re W n
0.271 0.267 0.267 rg
BT 26.250 767.476 Td /F1 9.8 Tf [(measurement methods. The higher the correlation between the various methods in the chain, the tighter the chain, and the )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(better its predictive power will be. Within PharmaCo, a tightness level of 70% for all variables studied can be assumed.)] TJ ET
BT 26.250 718.969 Td /F4 12.0 Tf [(Discovery experimentation strategies)] TJ ET
BT 26.250 699.015 Td /F1 9.8 Tf [(The three front-loaded experimental strategies for discovery research considered for subsequent comparative analysis are )] TJ ET
BT 26.250 687.110 Td /F1 9.8 Tf [(summarized in Figure 3. The arrows indicate which property classes are used in the prototype compound optimization cycles to )] TJ ET
BT 26.250 675.205 Td /F1 9.8 Tf [(converge to an NME. A vertical arrow indicates that only biological activity \(P\) or potency and selectivity is optimized, an arrow )] TJ ET
BT 26.250 663.300 Td /F1 9.8 Tf [(along the diagonal indicates that both biological activity and drug likeness or bioavailability properties \(B\) are used in multi-)] TJ ET
BT 26.250 651.396 Td /F1 9.8 Tf [(factorial optimization cycles.)] TJ ET
BT 26.250 631.991 Td /F1 9.8 Tf [(In the old )] TJ ET
BT 69.072 631.991 Td /F5 9.8 Tf [(paradigm)] TJ ET
BT 109.710 631.991 Td /F1 9.8 Tf [(, applied in the industry some time ago, discovery research was only concerned about biological activity or )] TJ ET
BT 26.250 620.086 Td /F1 9.8 Tf [(potency and selectivity of a candidate compound. Drug-likeness properties were only taken into account from pre-clinical )] TJ ET
BT 26.250 608.181 Td /F1 9.8 Tf [(research on, which is a time- and effort-consuming process \(Oprea, 2002\). This strategy was not entirely successful, since )] TJ ET
BT 26.250 596.277 Td /F1 9.8 Tf [(within the industry only one in ten candidate drugs succeeded through clinical trials to reach the market \(Drews, 1998\).)] TJ ET
BT 26.250 576.872 Td /F1 9.8 Tf [(In the )] TJ ET
BT 53.355 576.872 Td /F5 9.8 Tf [(front-loaded paradigm)] TJ ET
BT 148.730 576.872 Td /F1 9.8 Tf [(, both biological activity and pharmacokinetic properties are optimized simultaneously from the Hit-)] TJ ET
BT 26.250 564.967 Td /F1 9.8 Tf [(to-Lead \(H2L\) stage on. Here, confirmed hits series from HTS are prioritized and analyzed for problems. Progressive )] TJ ET
BT 26.250 553.062 Td /F1 9.8 Tf [(increments in biological activity )] TJ ET
BT 162.789 553.062 Td /F5 9.8 Tf [(and)] TJ ET
BT 179.052 553.062 Td /F1 9.8 Tf [( bio-availability are obtained by addressing the appropriate molecular determinants that )] TJ ET
BT 26.250 541.158 Td /F1 9.8 Tf [(define the desired compound characteristics.)] TJ ET
BT 26.250 521.753 Td /F1 9.8 Tf [(Also, this paradigm is more generous in promoting selected candidate compounds through the H2L and LO process. This )] TJ ET
BT 26.250 509.848 Td /F1 9.8 Tf [(argues against a too narrow idea funnel and means that more candidates will reach LO status. Although this is an expensive )] TJ ET
BT 26.250 497.943 Td /F1 9.8 Tf [(recommendation, it does prevent candidate eliminations due to tests that mimic reality with statistical inaccuracy, the latter )] TJ ET
BT 26.250 486.039 Td /F1 9.8 Tf [(compounded to the number of tests conducted \(DeWitte, 2002\). The idea, then, is to build up knowledge through to in-vivo of a )] TJ ET
BT 26.250 474.134 Td /F5 9.8 Tf [(series)] TJ ET
BT 52.253 474.134 Td /F1 9.8 Tf [( of candidates instead of going for a fast attrition based on “in-silico” or in-vitro tests with low predictive power, and )] TJ ET
BT 26.250 462.229 Td /F1 9.8 Tf [(running LO with a small subset of “winning” candidate series of compounds.)] TJ ET
BT 26.250 442.824 Td /F1 9.8 Tf [(Finally, )] TJ ET
BT 59.839 442.824 Td /F5 9.8 Tf [(early front loading)] TJ ET
BT 137.332 442.824 Td /F1 9.8 Tf [( takes the front-loaded paradigm even further, selecting the most promising classes based on the )] TJ ET
BT 26.250 430.920 Td /F1 9.8 Tf [(fullest multi-factorial picture available using virtual “in-silico” screening technology during HTS \(Oprea, 2002\).)] TJ ET
BT 26.250 411.515 Td /F1 9.8 Tf [(Generalizing, our theory-building effort leads us to define )] TJ ET
BT 273.910 411.515 Td /F5 9.8 Tf [(residual ambiguity)] TJ ET
BT 352.475 411.515 Td /F1 9.8 Tf [( as all non-characterized solution variables of a )] TJ ET
BT 26.250 399.610 Td /F1 9.8 Tf [(candidate solution concept, their causal relationships and/or related problem-solving mechanisms at a point in time during the )] TJ ET
BT 26.250 387.705 Td /F1 9.8 Tf [(emergence of the solution to an innovation problem. From the above, it will be clear that front-loaded experimentation strategies )] TJ ET
BT 26.250 375.801 Td /F1 9.8 Tf [(aim for more extensive candidate characterization or lower levels of residual ambiguity than old-paradigm strategies at the end )] TJ ET
BT 26.250 363.896 Td /F1 9.8 Tf [(of discovery before transferring the therapeutic candidate into clinical development.)] TJ ET
BT 26.250 344.491 Td /F1 9.8 Tf [(Now, to quantitatively evaluate predictive performance of discovery experimentation strategies, an analytical framework needs )] TJ ET
BT 26.250 332.586 Td /F1 9.8 Tf [(to be developed. This is the subject of the next section.)] TJ ET
BT 26.250 295.984 Td /F4 12.0 Tf [(Modeling experimentation strategies’ performance)] TJ ET
BT 26.250 258.832 Td /F4 12.0 Tf [(Predictive performance)] TJ ET
BT 26.250 238.878 Td /F1 9.8 Tf [(Predictive performance can be intuitively defined as the accuracy with which compounds are promoted to therapeutic candidate )] TJ ET
BT 26.250 226.973 Td /F1 9.8 Tf [(or NME[4] status, as confirmed by later clinical development. To build a quantitative model of experimentation strategy variables )] TJ ET
BT 26.250 215.068 Td /F1 9.8 Tf [(— levels of front loading and parallelism- influencing predictive performance — a more formal notation is required.)] TJ ET
BT 26.250 195.663 Td /F1 9.8 Tf [(Therefore, we distinguish between )] TJ ET
BT 177.443 195.663 Td /F5 9.8 Tf [(H)] TJ ET
BT 184.483 199.552 Td /F5 8.7 Tf [(+)] TJ ET
BT 189.544 195.663 Td /F1 9.8 Tf [( and )] TJ ET
BT 211.228 195.663 Td /F5 9.8 Tf [(H)] TJ ET
BT 218.268 199.552 Td /F5 8.7 Tf [(—)] TJ ET
BT 226.934 195.663 Td /F1 9.8 Tf [(, being numbers of compounds declared respectively as active or inactive by the )] TJ ET
BT 26.250 154.882 Td /F1 9.8 Tf [(discovery experimentation strategy. The former is a set of therapeutic candidates )] TJ ET
q
48.000 0 0 48.000 377.942 145.282 cm /I1 Do
Q
0.500 0.500 0.500 rg
BT 377.942 185.882 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 377.942 175.882 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5BH_i%5E%20%2B%20%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 425.942 154.882 Td /F1 9.8 Tf [( that tested positive throughout the )] TJ ET
BT 26.250 106.882 Td /F1 9.8 Tf [(optimization process. During this process, a – virtually infinite – number of candidates )] TJ ET
q
48.000 0 0 48.000 396.896 97.282 cm /I1 Do
Q
0.500 0.500 0.500 rg
BT 396.896 137.882 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 396.896 127.882 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5BH_j%5E%20-%20%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 444.896 106.882 Td /F1 9.8 Tf [( were eliminated from further )] TJ ET
BT 26.250 87.759 Td /F1 9.8 Tf [(optimization.)] TJ ET
BT 26.250 39.478 Td /F1 9.8 Tf [(Then, active candidates )] TJ ET
Q
q
15.000 29.878 577.500 747.122 re W n
0.271 0.267 0.267 rg
BT 26.250 767.476 Td /F1 9.8 Tf [(measurement methods. The higher the correlation between the various methods in the chain, the tighter the chain, and the )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(better its predictive power will be. Within PharmaCo, a tightness level of 70% for all variables studied can be assumed.)] TJ ET
BT 26.250 718.969 Td /F4 12.0 Tf [(Discovery experimentation strategies)] TJ ET
BT 26.250 699.015 Td /F1 9.8 Tf [(The three front-loaded experimental strategies for discovery research considered for subsequent comparative analysis are )] TJ ET
BT 26.250 687.110 Td /F1 9.8 Tf [(summarized in Figure 3. The arrows indicate which property classes are used in the prototype compound optimization cycles to )] TJ ET
BT 26.250 675.205 Td /F1 9.8 Tf [(converge to an NME. A vertical arrow indicates that only biological activity \(P\) or potency and selectivity is optimized, an arrow )] TJ ET
BT 26.250 663.300 Td /F1 9.8 Tf [(along the diagonal indicates that both biological activity and drug likeness or bioavailability properties \(B\) are used in multi-)] TJ ET
BT 26.250 651.396 Td /F1 9.8 Tf [(factorial optimization cycles.)] TJ ET
BT 26.250 631.991 Td /F1 9.8 Tf [(In the old )] TJ ET
BT 69.072 631.991 Td /F5 9.8 Tf [(paradigm)] TJ ET
BT 109.710 631.991 Td /F1 9.8 Tf [(, applied in the industry some time ago, discovery research was only concerned about biological activity or )] TJ ET
BT 26.250 620.086 Td /F1 9.8 Tf [(potency and selectivity of a candidate compound. Drug-likeness properties were only taken into account from pre-clinical )] TJ ET
BT 26.250 608.181 Td /F1 9.8 Tf [(research on, which is a time- and effort-consuming process \(Oprea, 2002\). This strategy was not entirely successful, since )] TJ ET
BT 26.250 596.277 Td /F1 9.8 Tf [(within the industry only one in ten candidate drugs succeeded through clinical trials to reach the market \(Drews, 1998\).)] TJ ET
BT 26.250 576.872 Td /F1 9.8 Tf [(In the )] TJ ET
BT 53.355 576.872 Td /F5 9.8 Tf [(front-loaded paradigm)] TJ ET
BT 148.730 576.872 Td /F1 9.8 Tf [(, both biological activity and pharmacokinetic properties are optimized simultaneously from the Hit-)] TJ ET
BT 26.250 564.967 Td /F1 9.8 Tf [(to-Lead \(H2L\) stage on. Here, confirmed hits series from HTS are prioritized and analyzed for problems. Progressive )] TJ ET
BT 26.250 553.062 Td /F1 9.8 Tf [(increments in biological activity )] TJ ET
BT 162.789 553.062 Td /F5 9.8 Tf [(and)] TJ ET
BT 179.052 553.062 Td /F1 9.8 Tf [( bio-availability are obtained by addressing the appropriate molecular determinants that )] TJ ET
BT 26.250 541.158 Td /F1 9.8 Tf [(define the desired compound characteristics.)] TJ ET
BT 26.250 521.753 Td /F1 9.8 Tf [(Also, this paradigm is more generous in promoting selected candidate compounds through the H2L and LO process. This )] TJ ET
BT 26.250 509.848 Td /F1 9.8 Tf [(argues against a too narrow idea funnel and means that more candidates will reach LO status. Although this is an expensive )] TJ ET
BT 26.250 497.943 Td /F1 9.8 Tf [(recommendation, it does prevent candidate eliminations due to tests that mimic reality with statistical inaccuracy, the latter )] TJ ET
BT 26.250 486.039 Td /F1 9.8 Tf [(compounded to the number of tests conducted \(DeWitte, 2002\). The idea, then, is to build up knowledge through to in-vivo of a )] TJ ET
BT 26.250 474.134 Td /F5 9.8 Tf [(series)] TJ ET
BT 52.253 474.134 Td /F1 9.8 Tf [( of candidates instead of going for a fast attrition based on “in-silico” or in-vitro tests with low predictive power, and )] TJ ET
BT 26.250 462.229 Td /F1 9.8 Tf [(running LO with a small subset of “winning” candidate series of compounds.)] TJ ET
BT 26.250 442.824 Td /F1 9.8 Tf [(Finally, )] TJ ET
BT 59.839 442.824 Td /F5 9.8 Tf [(early front loading)] TJ ET
BT 137.332 442.824 Td /F1 9.8 Tf [( takes the front-loaded paradigm even further, selecting the most promising classes based on the )] TJ ET
BT 26.250 430.920 Td /F1 9.8 Tf [(fullest multi-factorial picture available using virtual “in-silico” screening technology during HTS \(Oprea, 2002\).)] TJ ET
BT 26.250 411.515 Td /F1 9.8 Tf [(Generalizing, our theory-building effort leads us to define )] TJ ET
BT 273.910 411.515 Td /F5 9.8 Tf [(residual ambiguity)] TJ ET
BT 352.475 411.515 Td /F1 9.8 Tf [( as all non-characterized solution variables of a )] TJ ET
BT 26.250 399.610 Td /F1 9.8 Tf [(candidate solution concept, their causal relationships and/or related problem-solving mechanisms at a point in time during the )] TJ ET
BT 26.250 387.705 Td /F1 9.8 Tf [(emergence of the solution to an innovation problem. From the above, it will be clear that front-loaded experimentation strategies )] TJ ET
BT 26.250 375.801 Td /F1 9.8 Tf [(aim for more extensive candidate characterization or lower levels of residual ambiguity than old-paradigm strategies at the end )] TJ ET
BT 26.250 363.896 Td /F1 9.8 Tf [(of discovery before transferring the therapeutic candidate into clinical development.)] TJ ET
BT 26.250 344.491 Td /F1 9.8 Tf [(Now, to quantitatively evaluate predictive performance of discovery experimentation strategies, an analytical framework needs )] TJ ET
BT 26.250 332.586 Td /F1 9.8 Tf [(to be developed. This is the subject of the next section.)] TJ ET
BT 26.250 295.984 Td /F4 12.0 Tf [(Modeling experimentation strategies’ performance)] TJ ET
BT 26.250 258.832 Td /F4 12.0 Tf [(Predictive performance)] TJ ET
BT 26.250 238.878 Td /F1 9.8 Tf [(Predictive performance can be intuitively defined as the accuracy with which compounds are promoted to therapeutic candidate )] TJ ET
BT 26.250 226.973 Td /F1 9.8 Tf [(or NME[4] status, as confirmed by later clinical development. To build a quantitative model of experimentation strategy variables )] TJ ET
BT 26.250 215.068 Td /F1 9.8 Tf [(— levels of front loading and parallelism- influencing predictive performance — a more formal notation is required.)] TJ ET
BT 26.250 195.663 Td /F1 9.8 Tf [(Therefore, we distinguish between )] TJ ET
BT 177.443 195.663 Td /F5 9.8 Tf [(H)] TJ ET
BT 184.483 199.552 Td /F5 8.7 Tf [(+)] TJ ET
BT 189.544 195.663 Td /F1 9.8 Tf [( and )] TJ ET
BT 211.228 195.663 Td /F5 9.8 Tf [(H)] TJ ET
BT 218.268 199.552 Td /F5 8.7 Tf [(—)] TJ ET
BT 226.934 195.663 Td /F1 9.8 Tf [(, being numbers of compounds declared respectively as active or inactive by the )] TJ ET
BT 26.250 154.882 Td /F1 9.8 Tf [(discovery experimentation strategy. The former is a set of therapeutic candidates )] TJ ET
q
48.000 0 0 48.000 377.942 145.282 cm /I1 Do
Q
0.500 0.500 0.500 rg
BT 377.942 185.882 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 377.942 175.882 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5BH_i%5E%20%2B%20%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 425.942 154.882 Td /F1 9.8 Tf [( that tested positive throughout the )] TJ ET
BT 26.250 106.882 Td /F1 9.8 Tf [(optimization process. During this process, a – virtually infinite – number of candidates )] TJ ET
q
48.000 0 0 48.000 396.896 97.282 cm /I1 Do
Q
0.500 0.500 0.500 rg
BT 396.896 137.882 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 396.896 127.882 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5BH_j%5E%20-%20%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 444.896 106.882 Td /F1 9.8 Tf [( were eliminated from further )] TJ ET
BT 26.250 87.759 Td /F1 9.8 Tf [(optimization.)] TJ ET
BT 26.250 39.478 Td /F1 9.8 Tf [(Then, active candidates )] TJ ET
Q
q
0.000 0.000 0.000 rg
BT 291.710 19.825 Td /F1 11.0 Tf [(4)] TJ ET
BT 25.000 19.825 Td /F1 11.0 Tf [(Emergence: Complexity and Organization)] TJ ET
Q
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q
48.000 0 0 48.000 26.250 729.000 cm /I1 Do
Q
0.500 0.500 0.500 rg
BT 26.250 769.600 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 26.250 759.600 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5BH_i%5E%20%2B%20%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 74.250 738.600 Td /F1 9.8 Tf [( declared NMEs by the discovery experimentation strategy will subsequently be confirmed to be positive or negative )] TJ ET
BT 26.250 719.476 Td /F1 9.8 Tf [(in clinical development. We need then to distinguish between )] TJ ET
BT 292.337 719.476 Td /F5 9.8 Tf [(C)] TJ ET
BT 299.377 723.364 Td /F5 8.7 Tf [(+)] TJ ET
BT 304.438 719.476 Td /F1 9.8 Tf [( and )] TJ ET
BT 326.122 719.476 Td /F5 9.8 Tf [(C)] TJ ET
BT 333.162 723.364 Td /F5 8.7 Tf [(—)] TJ ET
BT 341.828 719.476 Td /F1 9.8 Tf [( as the number of NMEs respectively passing the latter )] TJ ET
BT 26.250 707.571 Td /F1 9.8 Tf [(development phases or not.)] TJ ET
BT 26.250 688.167 Td /F1 9.8 Tf [(To evaluate the predictive performance of a discovery experimentation strategy, the following question must be answered: )] TJ ET
BT 26.250 647.386 Td /F1 9.8 Tf [(“What is the probability that a candidate )] TJ ET
q
48.000 0 0 48.000 200.209 637.786 cm /I1 Do
Q
0.500 0.500 0.500 rg
BT 200.209 678.386 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 200.209 668.386 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5BH_i%5E%20%2B%20%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 248.209 647.386 Td /F1 9.8 Tf [( is )] TJ ET
BT 260.670 647.386 Td /F5 9.8 Tf [(really)] TJ ET
BT 283.963 647.386 Td /F1 9.8 Tf [( active, denoted as )] TJ ET
q
48.000 0 0 48.000 368.515 637.786 cm /I1 Do
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0.500 0.500 0.500 rg
BT 368.515 678.386 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 368.515 668.386 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5BC_j%5E%20%2B%20%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 416.515 647.386 Td /F1 9.8 Tf [(?” To answer this question, we must )] TJ ET
BT 26.250 628.262 Td /F1 9.8 Tf [(start from the universe of potential therapeutic agents H and examine how the experimentation strategy improves the odds of )] TJ ET
BT 26.250 616.357 Td /F1 9.8 Tf [(finding an active compound. This representation of uncertainty about parameters using conditional probabilities is called )] TJ ET
BT 26.250 604.452 Td /F1 9.8 Tf [(Bayesian inference. It models the experimentation strategy as a learning process that modifies one’s initial probability statement )] TJ ET
BT 26.250 592.548 Td /F1 9.8 Tf [(about the prevalence )] TJ ET
BT 120.016 592.548 Td /F5 9.8 Tf [(p)] TJ ET
BT 125.437 592.548 Td /F1 9.8 Tf [( or )] TJ ET
BT 139.526 592.548 Td /F5 9.8 Tf [(p \(C)] TJ ET
BT 157.943 596.436 Td /F5 8.7 Tf [(+)] TJ ET
BT 163.005 592.548 Td /F5 9.8 Tf [(\))] TJ ET
BT 166.251 592.548 Td /F1 9.8 Tf [( prior to observing the data during experimentation to updated or posterior knowledge )] TJ ET
BT 26.250 580.643 Td /F1 9.8 Tf [(incorporating both prior knowledge and the data at hand \(Congdon, 2001\).)] TJ ET
BT 26.250 561.238 Td /F1 9.8 Tf [(The )] TJ ET
BT 45.760 561.238 Td /F5 9.8 Tf [(positive predictive value)] TJ ET
BT 149.256 561.238 Td /F1 9.8 Tf [(, then, denoted )] TJ ET
BT 217.019 561.238 Td /F5 9.8 Tf [(p \(C)] TJ ET
BT 235.436 565.126 Td /F5 8.7 Tf [(+)] TJ ET
BT 240.498 561.238 Td /F5 9.8 Tf [(|H)] TJ ET
BT 250.072 565.126 Td /F5 8.7 Tf [(+)] TJ ET
BT 255.133 561.238 Td /F5 9.8 Tf [(\))] TJ ET
BT 258.380 561.238 Td /F1 9.8 Tf [( or )] TJ ET
BT 275.179 561.238 Td /F5 9.8 Tf [(p)] TJ ET
BT 280.600 565.126 Td /F5 8.7 Tf [(+)] TJ ET
BT 285.662 561.238 Td /F1 9.8 Tf [(, is read as the probability that a compound will actually pass )] TJ ET
BT 26.250 549.333 Td /F1 9.8 Tf [(clinical development )] TJ ET
BT 116.740 549.333 Td /F5 9.8 Tf [(p \(C)] TJ ET
BT 135.157 553.222 Td /F5 8.7 Tf [(+)] TJ ET
BT 140.219 549.333 Td /F5 9.8 Tf [(\))] TJ ET
BT 143.466 549.333 Td /F1 9.8 Tf [( given that it has been declared active )] TJ ET
BT 309.849 549.333 Td /F5 9.8 Tf [(p \(H)] TJ ET
BT 328.267 553.222 Td /F5 8.7 Tf [(+)] TJ ET
BT 333.328 549.333 Td /F5 9.8 Tf [(\))] TJ ET
BT 336.575 549.333 Td /F1 9.8 Tf [( by the experimentation strategy. Similarly, the )] TJ ET
BT 26.250 537.429 Td /F1 9.8 Tf [(probability that a compound will not pass clinical development )] TJ ET
BT 295.028 537.429 Td /F5 9.8 Tf [(p \(C)] TJ ET
BT 313.446 541.317 Td /F5 8.7 Tf [(–)] TJ ET
BT 318.265 537.429 Td /F5 9.8 Tf [(\))] TJ ET
BT 321.511 537.429 Td /F1 9.8 Tf [(, given it has been declared inactive )] TJ ET
BT 479.218 537.429 Td /F5 9.8 Tf [(p \(H)] TJ ET
BT 497.635 541.317 Td /F5 8.7 Tf [(–)] TJ ET
BT 502.454 537.429 Td /F5 9.8 Tf [(\))] TJ ET
BT 505.701 537.429 Td /F1 9.8 Tf [( by the )] TJ ET
BT 26.250 525.524 Td /F1 9.8 Tf [(experimentation strategy, is called the )] TJ ET
BT 192.614 525.524 Td /F5 9.8 Tf [(negative predictive value)] TJ ET
BT 299.913 525.524 Td /F1 9.8 Tf [(, and denoted )] TJ ET
BT 362.255 525.524 Td /F5 9.8 Tf [(p \(C)] TJ ET
BT 380.672 529.412 Td /F5 8.7 Tf [(—)] TJ ET
BT 389.339 525.524 Td /F5 9.8 Tf [(|H)] TJ ET
BT 398.913 529.412 Td /F5 8.7 Tf [(–)] TJ ET
BT 403.732 525.524 Td /F5 9.8 Tf [(\))] TJ ET
BT 406.979 525.524 Td /F1 9.8 Tf [(. The fraction of active compounds that )] TJ ET
BT 26.250 513.619 Td /F1 9.8 Tf [(were mistakenly declared inactive by the experimentation strategy is called )] TJ ET
BT 351.373 513.619 Td /F5 9.8 Tf [(p)] TJ ET
BT 356.794 517.507 Td /F5 8.7 Tf [(—)] TJ ET
BT 365.461 513.619 Td /F1 9.8 Tf [( or )] TJ ET
BT 379.550 513.619 Td /F5 9.8 Tf [(p \(C)] TJ ET
BT 397.968 517.507 Td /F5 8.7 Tf [(+)] TJ ET
BT 403.029 513.619 Td /F5 9.8 Tf [(|H)] TJ ET
BT 412.604 517.507 Td /F5 8.7 Tf [(–)] TJ ET
BT 417.422 513.619 Td /F5 9.8 Tf [(\))] TJ ET
BT 420.669 513.619 Td /F1 9.8 Tf [(; the negative predictive value is )] TJ ET
BT 26.250 501.714 Td /F1 9.8 Tf [(denoted as 1 — )] TJ ET
BT 97.796 501.714 Td /F5 9.8 Tf [(p)] TJ ET
BT 103.217 505.603 Td /F5 8.7 Tf [(—)] TJ ET
BT 111.883 501.714 Td /F1 9.8 Tf [( \(Parmigiani, 2002\). Hence, )] TJ ET
BT 233.261 501.714 Td /F5 9.8 Tf [(predictive performance)] TJ ET
BT 332.418 501.714 Td /F1 9.8 Tf [( of an experimentation strategy can be measured using )] TJ ET
BT 26.250 489.810 Td /F1 9.8 Tf [(two criteria )] TJ ET
BT 76.092 489.810 Td /F5 9.8 Tf [(?)] TJ ET
BT 81.513 489.810 Td /F1 9.8 Tf [(:)] TJ ET
0.965 0.965 0.965 rg
26.250 412.429 555.000 67.500 re f
0.267 0.267 0.267 rg
0.267 0.267 0.267 RG
26.250 479.929 m 581.250 479.929 l 581.250 479.179 l 26.250 479.179 l f
26.250 412.429 m 581.250 412.429 l 581.250 413.179 l 26.250 413.179 l f
q
48.000 0 0 48.000 35.250 422.179 cm /I1 Do
Q
0.500 0.500 0.500 rg
BT 35.250 462.779 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 452.779 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5B%7Bc_1%7D%3A%7B%5Cpi%20%5E%20%2B%20%7D%20%3D%20p\(%7BC%5E%20%2B%20%7D%7C%7BH%5E%20%2B%20%7D\)%20%3D%20%5Cfrac%7B%7Bp\(%7BC%5E%20%2B%20%7D%2C%7BH%5E%20%2B%20%7D\)%7D%7D%7B%7Bp\(%7BH%5E%20%2B%20%7D\)%7D%7D%5Cqquad%20%5Cqquad%20\(1\)%5C%5D)] TJ ET
0.965 0.965 0.965 rg
26.250 337.429 555.000 67.500 re f
0.267 0.267 0.267 rg
26.250 404.929 m 581.250 404.929 l 581.250 404.179 l 26.250 404.179 l f
26.250 337.429 m 581.250 337.429 l 581.250 338.179 l 26.250 338.179 l f
q
48.000 0 0 48.000 35.250 347.179 cm /I1 Do
Q
0.500 0.500 0.500 rg
BT 35.250 387.779 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 377.779 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5B%7Bc_1%7D%3A%7B%5Cpi%20%5E%20-%20%7D%20%3D%20p\(%7BC%5E%20%2B%20%7D%7C%7BH%5E%20-%20%7D\)%20%3D%20%5Cfrac%7B%7Bp\(%7BC%5E%20%2B%20%7D%2C%7BH%5E%20-%20%7D\)%7D%7D%7B%7Bp\(%7BH%5E%20-%20%7D\)%7D%7D%5Cqquad%20%5Cqquad%20\(2\)%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 26.250 320.405 Td /F1 9.8 Tf [(An experimentation strategy featuring high positive and negative predictive performance, then, has a high value for )] TJ ET
BT 523.207 320.405 Td /F5 9.8 Tf [(p)] TJ ET
BT 528.629 324.293 Td /F1 8.7 Tf [(+)] TJ ET
BT 533.690 320.405 Td /F1 9.8 Tf [( and a low )] TJ ET
BT 26.250 308.500 Td /F5 9.8 Tf [(p)] TJ ET
BT 31.671 312.388 Td /F1 8.7 Tf [(–)] TJ ET
BT 36.490 308.500 Td /F1 9.8 Tf [(. High values for )] TJ ET
BT 109.644 308.500 Td /F5 9.8 Tf [(p)] TJ ET
BT 115.065 312.388 Td /F1 8.7 Tf [(+)] TJ ET
BT 120.126 308.500 Td /F1 9.8 Tf [( indicate high levels of clinical confirmation of the earlier discovery decision. Low levels of p— indicate a )] TJ ET
BT 26.250 296.595 Td /F1 9.8 Tf [(low level of missed opportunities for commercialization[5]. The transition from p to )] TJ ET
BT 380.623 296.595 Td /F5 9.8 Tf [(p)] TJ ET
BT 386.044 300.484 Td /F1 8.7 Tf [(+)] TJ ET
BT 391.106 296.595 Td /F1 9.8 Tf [( and )] TJ ET
BT 412.790 296.595 Td /F5 9.8 Tf [(p)] TJ ET
BT 418.211 300.484 Td /F1 8.7 Tf [(–)] TJ ET
BT 423.030 296.595 Td /F1 9.8 Tf [( models the learning about the true )] TJ ET
BT 26.250 284.691 Td /F1 9.8 Tf [(status of the universe of potential compounds )] TJ ET
BT 225.150 284.691 Td /F5 9.8 Tf [(H)] TJ ET
BT 232.190 284.691 Td /F1 9.8 Tf [(. Using the Bayesian logic set out above, it quantifies how inferences about the )] TJ ET
BT 26.250 272.786 Td /F1 9.8 Tf [(universe of potential compounds )] TJ ET
BT 169.321 272.786 Td /F5 9.8 Tf [(H)] TJ ET
BT 176.361 272.786 Td /F1 9.8 Tf [( are updated in the light of new evidence, provided by the experimentation strategy.)] TJ ET
BT 26.250 236.183 Td /F4 12.0 Tf [(Business performance)] TJ ET
BT 26.250 216.229 Td /F1 9.8 Tf [(To formally state the problem of an R&D manager having to choose between various experimentation strategies, the optimal )] TJ ET
BT 26.250 204.324 Td /F1 9.8 Tf [(business decision for a strategy )] TJ ET
BT 165.519 204.324 Td /F5 9.8 Tf [(d)] TJ ET
BT 170.940 208.213 Td /F5 8.7 Tf [(*)] TJ ET
BT 174.311 204.324 Td /F1 9.8 Tf [( can be modelled as follows \(Müller, 1999: 99; Parmigiani, 2002\):)] TJ ET
0.965 0.965 0.965 rg
26.250 126.944 555.000 67.500 re f
0.267 0.267 0.267 rg
26.250 194.444 m 581.250 194.444 l 581.250 193.694 l 26.250 193.694 l f
26.250 126.944 m 581.250 126.944 l 581.250 127.694 l 26.250 127.694 l f
q
48.000 0 0 48.000 35.250 136.694 cm /I1 Do
Q
0.500 0.500 0.500 rg
BT 35.250 177.294 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 167.294 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5B%7Bd%5E*%7D%20%3D%20%5Carg%20%7B%5Cmax%20_%7Bd%20%5Cin%20D%7D%7DU\(d\)%5Cqquad%20%5Cqquad%20\(3\)%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 26.250 109.920 Td /F1 9.8 Tf [(where)] TJ ET
0.965 0.965 0.965 rg
26.250 36.289 555.000 63.750 re f
0.267 0.267 0.267 rg
26.250 100.039 m 581.250 100.039 l 581.250 99.289 l 26.250 99.289 l f
Q
q
15.000 36.289 577.500 740.711 re W n
q
48.000 0 0 48.000 26.250 729.000 cm /I1 Do
Q
0.500 0.500 0.500 rg
BT 26.250 769.600 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 26.250 759.600 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5BH_i%5E%20%2B%20%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 74.250 738.600 Td /F1 9.8 Tf [( declared NMEs by the discovery experimentation strategy will subsequently be confirmed to be positive or negative )] TJ ET
BT 26.250 719.476 Td /F1 9.8 Tf [(in clinical development. We need then to distinguish between )] TJ ET
BT 292.337 719.476 Td /F5 9.8 Tf [(C)] TJ ET
BT 299.377 723.364 Td /F5 8.7 Tf [(+)] TJ ET
BT 304.438 719.476 Td /F1 9.8 Tf [( and )] TJ ET
BT 326.122 719.476 Td /F5 9.8 Tf [(C)] TJ ET
BT 333.162 723.364 Td /F5 8.7 Tf [(—)] TJ ET
BT 341.828 719.476 Td /F1 9.8 Tf [( as the number of NMEs respectively passing the latter )] TJ ET
BT 26.250 707.571 Td /F1 9.8 Tf [(development phases or not.)] TJ ET
BT 26.250 688.167 Td /F1 9.8 Tf [(To evaluate the predictive performance of a discovery experimentation strategy, the following question must be answered: )] TJ ET
BT 26.250 647.386 Td /F1 9.8 Tf [(“What is the probability that a candidate )] TJ ET
q
48.000 0 0 48.000 200.209 637.786 cm /I1 Do
Q
0.500 0.500 0.500 rg
BT 200.209 678.386 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 200.209 668.386 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5BH_i%5E%20%2B%20%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 248.209 647.386 Td /F1 9.8 Tf [( is )] TJ ET
BT 260.670 647.386 Td /F5 9.8 Tf [(really)] TJ ET
BT 283.963 647.386 Td /F1 9.8 Tf [( active, denoted as )] TJ ET
q
48.000 0 0 48.000 368.515 637.786 cm /I1 Do
Q
0.500 0.500 0.500 rg
BT 368.515 678.386 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 368.515 668.386 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5BC_j%5E%20%2B%20%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 416.515 647.386 Td /F1 9.8 Tf [(?” To answer this question, we must )] TJ ET
BT 26.250 628.262 Td /F1 9.8 Tf [(start from the universe of potential therapeutic agents H and examine how the experimentation strategy improves the odds of )] TJ ET
BT 26.250 616.357 Td /F1 9.8 Tf [(finding an active compound. This representation of uncertainty about parameters using conditional probabilities is called )] TJ ET
BT 26.250 604.452 Td /F1 9.8 Tf [(Bayesian inference. It models the experimentation strategy as a learning process that modifies one’s initial probability statement )] TJ ET
BT 26.250 592.548 Td /F1 9.8 Tf [(about the prevalence )] TJ ET
BT 120.016 592.548 Td /F5 9.8 Tf [(p)] TJ ET
BT 125.437 592.548 Td /F1 9.8 Tf [( or )] TJ ET
BT 139.526 592.548 Td /F5 9.8 Tf [(p \(C)] TJ ET
BT 157.943 596.436 Td /F5 8.7 Tf [(+)] TJ ET
BT 163.005 592.548 Td /F5 9.8 Tf [(\))] TJ ET
BT 166.251 592.548 Td /F1 9.8 Tf [( prior to observing the data during experimentation to updated or posterior knowledge )] TJ ET
BT 26.250 580.643 Td /F1 9.8 Tf [(incorporating both prior knowledge and the data at hand \(Congdon, 2001\).)] TJ ET
BT 26.250 561.238 Td /F1 9.8 Tf [(The )] TJ ET
BT 45.760 561.238 Td /F5 9.8 Tf [(positive predictive value)] TJ ET
BT 149.256 561.238 Td /F1 9.8 Tf [(, then, denoted )] TJ ET
BT 217.019 561.238 Td /F5 9.8 Tf [(p \(C)] TJ ET
BT 235.436 565.126 Td /F5 8.7 Tf [(+)] TJ ET
BT 240.498 561.238 Td /F5 9.8 Tf [(|H)] TJ ET
BT 250.072 565.126 Td /F5 8.7 Tf [(+)] TJ ET
BT 255.133 561.238 Td /F5 9.8 Tf [(\))] TJ ET
BT 258.380 561.238 Td /F1 9.8 Tf [( or )] TJ ET
BT 275.179 561.238 Td /F5 9.8 Tf [(p)] TJ ET
BT 280.600 565.126 Td /F5 8.7 Tf [(+)] TJ ET
BT 285.662 561.238 Td /F1 9.8 Tf [(, is read as the probability that a compound will actually pass )] TJ ET
BT 26.250 549.333 Td /F1 9.8 Tf [(clinical development )] TJ ET
BT 116.740 549.333 Td /F5 9.8 Tf [(p \(C)] TJ ET
BT 135.157 553.222 Td /F5 8.7 Tf [(+)] TJ ET
BT 140.219 549.333 Td /F5 9.8 Tf [(\))] TJ ET
BT 143.466 549.333 Td /F1 9.8 Tf [( given that it has been declared active )] TJ ET
BT 309.849 549.333 Td /F5 9.8 Tf [(p \(H)] TJ ET
BT 328.267 553.222 Td /F5 8.7 Tf [(+)] TJ ET
BT 333.328 549.333 Td /F5 9.8 Tf [(\))] TJ ET
BT 336.575 549.333 Td /F1 9.8 Tf [( by the experimentation strategy. Similarly, the )] TJ ET
BT 26.250 537.429 Td /F1 9.8 Tf [(probability that a compound will not pass clinical development )] TJ ET
BT 295.028 537.429 Td /F5 9.8 Tf [(p \(C)] TJ ET
BT 313.446 541.317 Td /F5 8.7 Tf [(–)] TJ ET
BT 318.265 537.429 Td /F5 9.8 Tf [(\))] TJ ET
BT 321.511 537.429 Td /F1 9.8 Tf [(, given it has been declared inactive )] TJ ET
BT 479.218 537.429 Td /F5 9.8 Tf [(p \(H)] TJ ET
BT 497.635 541.317 Td /F5 8.7 Tf [(–)] TJ ET
BT 502.454 537.429 Td /F5 9.8 Tf [(\))] TJ ET
BT 505.701 537.429 Td /F1 9.8 Tf [( by the )] TJ ET
BT 26.250 525.524 Td /F1 9.8 Tf [(experimentation strategy, is called the )] TJ ET
BT 192.614 525.524 Td /F5 9.8 Tf [(negative predictive value)] TJ ET
BT 299.913 525.524 Td /F1 9.8 Tf [(, and denoted )] TJ ET
BT 362.255 525.524 Td /F5 9.8 Tf [(p \(C)] TJ ET
BT 380.672 529.412 Td /F5 8.7 Tf [(—)] TJ ET
BT 389.339 525.524 Td /F5 9.8 Tf [(|H)] TJ ET
BT 398.913 529.412 Td /F5 8.7 Tf [(–)] TJ ET
BT 403.732 525.524 Td /F5 9.8 Tf [(\))] TJ ET
BT 406.979 525.524 Td /F1 9.8 Tf [(. The fraction of active compounds that )] TJ ET
BT 26.250 513.619 Td /F1 9.8 Tf [(were mistakenly declared inactive by the experimentation strategy is called )] TJ ET
BT 351.373 513.619 Td /F5 9.8 Tf [(p)] TJ ET
BT 356.794 517.507 Td /F5 8.7 Tf [(—)] TJ ET
BT 365.461 513.619 Td /F1 9.8 Tf [( or )] TJ ET
BT 379.550 513.619 Td /F5 9.8 Tf [(p \(C)] TJ ET
BT 397.968 517.507 Td /F5 8.7 Tf [(+)] TJ ET
BT 403.029 513.619 Td /F5 9.8 Tf [(|H)] TJ ET
BT 412.604 517.507 Td /F5 8.7 Tf [(–)] TJ ET
BT 417.422 513.619 Td /F5 9.8 Tf [(\))] TJ ET
BT 420.669 513.619 Td /F1 9.8 Tf [(; the negative predictive value is )] TJ ET
BT 26.250 501.714 Td /F1 9.8 Tf [(denoted as 1 — )] TJ ET
BT 97.796 501.714 Td /F5 9.8 Tf [(p)] TJ ET
BT 103.217 505.603 Td /F5 8.7 Tf [(—)] TJ ET
BT 111.883 501.714 Td /F1 9.8 Tf [( \(Parmigiani, 2002\). Hence, )] TJ ET
BT 233.261 501.714 Td /F5 9.8 Tf [(predictive performance)] TJ ET
BT 332.418 501.714 Td /F1 9.8 Tf [( of an experimentation strategy can be measured using )] TJ ET
BT 26.250 489.810 Td /F1 9.8 Tf [(two criteria )] TJ ET
BT 76.092 489.810 Td /F5 9.8 Tf [(?)] TJ ET
BT 81.513 489.810 Td /F1 9.8 Tf [(:)] TJ ET
0.965 0.965 0.965 rg
26.250 412.429 555.000 67.500 re f
0.267 0.267 0.267 rg
0.267 0.267 0.267 RG
26.250 479.929 m 581.250 479.929 l 581.250 479.179 l 26.250 479.179 l f
26.250 412.429 m 581.250 412.429 l 581.250 413.179 l 26.250 413.179 l f
q
48.000 0 0 48.000 35.250 422.179 cm /I1 Do
Q
0.500 0.500 0.500 rg
BT 35.250 462.779 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 452.779 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5B%7Bc_1%7D%3A%7B%5Cpi%20%5E%20%2B%20%7D%20%3D%20p\(%7BC%5E%20%2B%20%7D%7C%7BH%5E%20%2B%20%7D\)%20%3D%20%5Cfrac%7B%7Bp\(%7BC%5E%20%2B%20%7D%2C%7BH%5E%20%2B%20%7D\)%7D%7D%7B%7Bp\(%7BH%5E%20%2B%20%7D\)%7D%7D%5Cqquad%20%5Cqquad%20\(1\)%5C%5D)] TJ ET
0.965 0.965 0.965 rg
26.250 337.429 555.000 67.500 re f
0.267 0.267 0.267 rg
26.250 404.929 m 581.250 404.929 l 581.250 404.179 l 26.250 404.179 l f
26.250 337.429 m 581.250 337.429 l 581.250 338.179 l 26.250 338.179 l f
q
48.000 0 0 48.000 35.250 347.179 cm /I1 Do
Q
0.500 0.500 0.500 rg
BT 35.250 387.779 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 377.779 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5B%7Bc_1%7D%3A%7B%5Cpi%20%5E%20-%20%7D%20%3D%20p\(%7BC%5E%20%2B%20%7D%7C%7BH%5E%20-%20%7D\)%20%3D%20%5Cfrac%7B%7Bp\(%7BC%5E%20%2B%20%7D%2C%7BH%5E%20-%20%7D\)%7D%7D%7B%7Bp\(%7BH%5E%20-%20%7D\)%7D%7D%5Cqquad%20%5Cqquad%20\(2\)%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 26.250 320.405 Td /F1 9.8 Tf [(An experimentation strategy featuring high positive and negative predictive performance, then, has a high value for )] TJ ET
BT 523.207 320.405 Td /F5 9.8 Tf [(p)] TJ ET
BT 528.629 324.293 Td /F1 8.7 Tf [(+)] TJ ET
BT 533.690 320.405 Td /F1 9.8 Tf [( and a low )] TJ ET
BT 26.250 308.500 Td /F5 9.8 Tf [(p)] TJ ET
BT 31.671 312.388 Td /F1 8.7 Tf [(–)] TJ ET
BT 36.490 308.500 Td /F1 9.8 Tf [(. High values for )] TJ ET
BT 109.644 308.500 Td /F5 9.8 Tf [(p)] TJ ET
BT 115.065 312.388 Td /F1 8.7 Tf [(+)] TJ ET
BT 120.126 308.500 Td /F1 9.8 Tf [( indicate high levels of clinical confirmation of the earlier discovery decision. Low levels of p— indicate a )] TJ ET
BT 26.250 296.595 Td /F1 9.8 Tf [(low level of missed opportunities for commercialization[5]. The transition from p to )] TJ ET
BT 380.623 296.595 Td /F5 9.8 Tf [(p)] TJ ET
BT 386.044 300.484 Td /F1 8.7 Tf [(+)] TJ ET
BT 391.106 296.595 Td /F1 9.8 Tf [( and )] TJ ET
BT 412.790 296.595 Td /F5 9.8 Tf [(p)] TJ ET
BT 418.211 300.484 Td /F1 8.7 Tf [(–)] TJ ET
BT 423.030 296.595 Td /F1 9.8 Tf [( models the learning about the true )] TJ ET
BT 26.250 284.691 Td /F1 9.8 Tf [(status of the universe of potential compounds )] TJ ET
BT 225.150 284.691 Td /F5 9.8 Tf [(H)] TJ ET
BT 232.190 284.691 Td /F1 9.8 Tf [(. Using the Bayesian logic set out above, it quantifies how inferences about the )] TJ ET
BT 26.250 272.786 Td /F1 9.8 Tf [(universe of potential compounds )] TJ ET
BT 169.321 272.786 Td /F5 9.8 Tf [(H)] TJ ET
BT 176.361 272.786 Td /F1 9.8 Tf [( are updated in the light of new evidence, provided by the experimentation strategy.)] TJ ET
BT 26.250 236.183 Td /F4 12.0 Tf [(Business performance)] TJ ET
BT 26.250 216.229 Td /F1 9.8 Tf [(To formally state the problem of an R&D manager having to choose between various experimentation strategies, the optimal )] TJ ET
BT 26.250 204.324 Td /F1 9.8 Tf [(business decision for a strategy )] TJ ET
BT 165.519 204.324 Td /F5 9.8 Tf [(d)] TJ ET
BT 170.940 208.213 Td /F5 8.7 Tf [(*)] TJ ET
BT 174.311 204.324 Td /F1 9.8 Tf [( can be modelled as follows \(Müller, 1999: 99; Parmigiani, 2002\):)] TJ ET
0.965 0.965 0.965 rg
26.250 126.944 555.000 67.500 re f
0.267 0.267 0.267 rg
26.250 194.444 m 581.250 194.444 l 581.250 193.694 l 26.250 193.694 l f
26.250 126.944 m 581.250 126.944 l 581.250 127.694 l 26.250 127.694 l f
q
48.000 0 0 48.000 35.250 136.694 cm /I1 Do
Q
0.500 0.500 0.500 rg
BT 35.250 177.294 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 167.294 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5B%7Bd%5E*%7D%20%3D%20%5Carg%20%7B%5Cmax%20_%7Bd%20%5Cin%20D%7D%7DU\(d\)%5Cqquad%20%5Cqquad%20\(3\)%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 26.250 109.920 Td /F1 9.8 Tf [(where)] TJ ET
0.965 0.965 0.965 rg
26.250 36.289 555.000 63.750 re f
0.267 0.267 0.267 rg
26.250 100.039 m 581.250 100.039 l 581.250 99.289 l 26.250 99.289 l f
Q
q
15.000 36.289 577.500 740.711 re W n
q
48.000 0 0 48.000 26.250 729.000 cm /I1 Do
Q
0.500 0.500 0.500 rg
BT 26.250 769.600 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 26.250 759.600 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5BH_i%5E%20%2B%20%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 74.250 738.600 Td /F1 9.8 Tf [( declared NMEs by the discovery experimentation strategy will subsequently be confirmed to be positive or negative )] TJ ET
BT 26.250 719.476 Td /F1 9.8 Tf [(in clinical development. We need then to distinguish between )] TJ ET
BT 292.337 719.476 Td /F5 9.8 Tf [(C)] TJ ET
BT 299.377 723.364 Td /F5 8.7 Tf [(+)] TJ ET
BT 304.438 719.476 Td /F1 9.8 Tf [( and )] TJ ET
BT 326.122 719.476 Td /F5 9.8 Tf [(C)] TJ ET
BT 333.162 723.364 Td /F5 8.7 Tf [(—)] TJ ET
BT 341.828 719.476 Td /F1 9.8 Tf [( as the number of NMEs respectively passing the latter )] TJ ET
BT 26.250 707.571 Td /F1 9.8 Tf [(development phases or not.)] TJ ET
BT 26.250 688.167 Td /F1 9.8 Tf [(To evaluate the predictive performance of a discovery experimentation strategy, the following question must be answered: )] TJ ET
BT 26.250 647.386 Td /F1 9.8 Tf [(“What is the probability that a candidate )] TJ ET
q
48.000 0 0 48.000 200.209 637.786 cm /I1 Do
Q
0.500 0.500 0.500 rg
BT 200.209 678.386 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 200.209 668.386 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5BH_i%5E%20%2B%20%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 248.209 647.386 Td /F1 9.8 Tf [( is )] TJ ET
BT 260.670 647.386 Td /F5 9.8 Tf [(really)] TJ ET
BT 283.963 647.386 Td /F1 9.8 Tf [( active, denoted as )] TJ ET
q
48.000 0 0 48.000 368.515 637.786 cm /I1 Do
Q
0.500 0.500 0.500 rg
BT 368.515 678.386 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 368.515 668.386 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5BC_j%5E%20%2B%20%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 416.515 647.386 Td /F1 9.8 Tf [(?” To answer this question, we must )] TJ ET
BT 26.250 628.262 Td /F1 9.8 Tf [(start from the universe of potential therapeutic agents H and examine how the experimentation strategy improves the odds of )] TJ ET
BT 26.250 616.357 Td /F1 9.8 Tf [(finding an active compound. This representation of uncertainty about parameters using conditional probabilities is called )] TJ ET
BT 26.250 604.452 Td /F1 9.8 Tf [(Bayesian inference. It models the experimentation strategy as a learning process that modifies one’s initial probability statement )] TJ ET
BT 26.250 592.548 Td /F1 9.8 Tf [(about the prevalence )] TJ ET
BT 120.016 592.548 Td /F5 9.8 Tf [(p)] TJ ET
BT 125.437 592.548 Td /F1 9.8 Tf [( or )] TJ ET
BT 139.526 592.548 Td /F5 9.8 Tf [(p \(C)] TJ ET
BT 157.943 596.436 Td /F5 8.7 Tf [(+)] TJ ET
BT 163.005 592.548 Td /F5 9.8 Tf [(\))] TJ ET
BT 166.251 592.548 Td /F1 9.8 Tf [( prior to observing the data during experimentation to updated or posterior knowledge )] TJ ET
BT 26.250 580.643 Td /F1 9.8 Tf [(incorporating both prior knowledge and the data at hand \(Congdon, 2001\).)] TJ ET
BT 26.250 561.238 Td /F1 9.8 Tf [(The )] TJ ET
BT 45.760 561.238 Td /F5 9.8 Tf [(positive predictive value)] TJ ET
BT 149.256 561.238 Td /F1 9.8 Tf [(, then, denoted )] TJ ET
BT 217.019 561.238 Td /F5 9.8 Tf [(p \(C)] TJ ET
BT 235.436 565.126 Td /F5 8.7 Tf [(+)] TJ ET
BT 240.498 561.238 Td /F5 9.8 Tf [(|H)] TJ ET
BT 250.072 565.126 Td /F5 8.7 Tf [(+)] TJ ET
BT 255.133 561.238 Td /F5 9.8 Tf [(\))] TJ ET
BT 258.380 561.238 Td /F1 9.8 Tf [( or )] TJ ET
BT 275.179 561.238 Td /F5 9.8 Tf [(p)] TJ ET
BT 280.600 565.126 Td /F5 8.7 Tf [(+)] TJ ET
BT 285.662 561.238 Td /F1 9.8 Tf [(, is read as the probability that a compound will actually pass )] TJ ET
BT 26.250 549.333 Td /F1 9.8 Tf [(clinical development )] TJ ET
BT 116.740 549.333 Td /F5 9.8 Tf [(p \(C)] TJ ET
BT 135.157 553.222 Td /F5 8.7 Tf [(+)] TJ ET
BT 140.219 549.333 Td /F5 9.8 Tf [(\))] TJ ET
BT 143.466 549.333 Td /F1 9.8 Tf [( given that it has been declared active )] TJ ET
BT 309.849 549.333 Td /F5 9.8 Tf [(p \(H)] TJ ET
BT 328.267 553.222 Td /F5 8.7 Tf [(+)] TJ ET
BT 333.328 549.333 Td /F5 9.8 Tf [(\))] TJ ET
BT 336.575 549.333 Td /F1 9.8 Tf [( by the experimentation strategy. Similarly, the )] TJ ET
BT 26.250 537.429 Td /F1 9.8 Tf [(probability that a compound will not pass clinical development )] TJ ET
BT 295.028 537.429 Td /F5 9.8 Tf [(p \(C)] TJ ET
BT 313.446 541.317 Td /F5 8.7 Tf [(–)] TJ ET
BT 318.265 537.429 Td /F5 9.8 Tf [(\))] TJ ET
BT 321.511 537.429 Td /F1 9.8 Tf [(, given it has been declared inactive )] TJ ET
BT 479.218 537.429 Td /F5 9.8 Tf [(p \(H)] TJ ET
BT 497.635 541.317 Td /F5 8.7 Tf [(–)] TJ ET
BT 502.454 537.429 Td /F5 9.8 Tf [(\))] TJ ET
BT 505.701 537.429 Td /F1 9.8 Tf [( by the )] TJ ET
BT 26.250 525.524 Td /F1 9.8 Tf [(experimentation strategy, is called the )] TJ ET
BT 192.614 525.524 Td /F5 9.8 Tf [(negative predictive value)] TJ ET
BT 299.913 525.524 Td /F1 9.8 Tf [(, and denoted )] TJ ET
BT 362.255 525.524 Td /F5 9.8 Tf [(p \(C)] TJ ET
BT 380.672 529.412 Td /F5 8.7 Tf [(—)] TJ ET
BT 389.339 525.524 Td /F5 9.8 Tf [(|H)] TJ ET
BT 398.913 529.412 Td /F5 8.7 Tf [(–)] TJ ET
BT 403.732 525.524 Td /F5 9.8 Tf [(\))] TJ ET
BT 406.979 525.524 Td /F1 9.8 Tf [(. The fraction of active compounds that )] TJ ET
BT 26.250 513.619 Td /F1 9.8 Tf [(were mistakenly declared inactive by the experimentation strategy is called )] TJ ET
BT 351.373 513.619 Td /F5 9.8 Tf [(p)] TJ ET
BT 356.794 517.507 Td /F5 8.7 Tf [(—)] TJ ET
BT 365.461 513.619 Td /F1 9.8 Tf [( or )] TJ ET
BT 379.550 513.619 Td /F5 9.8 Tf [(p \(C)] TJ ET
BT 397.968 517.507 Td /F5 8.7 Tf [(+)] TJ ET
BT 403.029 513.619 Td /F5 9.8 Tf [(|H)] TJ ET
BT 412.604 517.507 Td /F5 8.7 Tf [(–)] TJ ET
BT 417.422 513.619 Td /F5 9.8 Tf [(\))] TJ ET
BT 420.669 513.619 Td /F1 9.8 Tf [(; the negative predictive value is )] TJ ET
BT 26.250 501.714 Td /F1 9.8 Tf [(denoted as 1 — )] TJ ET
BT 97.796 501.714 Td /F5 9.8 Tf [(p)] TJ ET
BT 103.217 505.603 Td /F5 8.7 Tf [(—)] TJ ET
BT 111.883 501.714 Td /F1 9.8 Tf [( \(Parmigiani, 2002\). Hence, )] TJ ET
BT 233.261 501.714 Td /F5 9.8 Tf [(predictive performance)] TJ ET
BT 332.418 501.714 Td /F1 9.8 Tf [( of an experimentation strategy can be measured using )] TJ ET
BT 26.250 489.810 Td /F1 9.8 Tf [(two criteria )] TJ ET
BT 76.092 489.810 Td /F5 9.8 Tf [(?)] TJ ET
BT 81.513 489.810 Td /F1 9.8 Tf [(:)] TJ ET
0.965 0.965 0.965 rg
26.250 412.429 555.000 67.500 re f
0.267 0.267 0.267 rg
0.267 0.267 0.267 RG
26.250 479.929 m 581.250 479.929 l 581.250 479.179 l 26.250 479.179 l f
26.250 412.429 m 581.250 412.429 l 581.250 413.179 l 26.250 413.179 l f
q
48.000 0 0 48.000 35.250 422.179 cm /I1 Do
Q
0.500 0.500 0.500 rg
BT 35.250 462.779 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 452.779 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5B%7Bc_1%7D%3A%7B%5Cpi%20%5E%20%2B%20%7D%20%3D%20p\(%7BC%5E%20%2B%20%7D%7C%7BH%5E%20%2B%20%7D\)%20%3D%20%5Cfrac%7B%7Bp\(%7BC%5E%20%2B%20%7D%2C%7BH%5E%20%2B%20%7D\)%7D%7D%7B%7Bp\(%7BH%5E%20%2B%20%7D\)%7D%7D%5Cqquad%20%5Cqquad%20\(1\)%5C%5D)] TJ ET
0.965 0.965 0.965 rg
26.250 337.429 555.000 67.500 re f
0.267 0.267 0.267 rg
26.250 404.929 m 581.250 404.929 l 581.250 404.179 l 26.250 404.179 l f
26.250 337.429 m 581.250 337.429 l 581.250 338.179 l 26.250 338.179 l f
q
48.000 0 0 48.000 35.250 347.179 cm /I1 Do
Q
0.500 0.500 0.500 rg
BT 35.250 387.779 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 377.779 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5B%7Bc_1%7D%3A%7B%5Cpi%20%5E%20-%20%7D%20%3D%20p\(%7BC%5E%20%2B%20%7D%7C%7BH%5E%20-%20%7D\)%20%3D%20%5Cfrac%7B%7Bp\(%7BC%5E%20%2B%20%7D%2C%7BH%5E%20-%20%7D\)%7D%7D%7B%7Bp\(%7BH%5E%20-%20%7D\)%7D%7D%5Cqquad%20%5Cqquad%20\(2\)%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 26.250 320.405 Td /F1 9.8 Tf [(An experimentation strategy featuring high positive and negative predictive performance, then, has a high value for )] TJ ET
BT 523.207 320.405 Td /F5 9.8 Tf [(p)] TJ ET
BT 528.629 324.293 Td /F1 8.7 Tf [(+)] TJ ET
BT 533.690 320.405 Td /F1 9.8 Tf [( and a low )] TJ ET
BT 26.250 308.500 Td /F5 9.8 Tf [(p)] TJ ET
BT 31.671 312.388 Td /F1 8.7 Tf [(–)] TJ ET
BT 36.490 308.500 Td /F1 9.8 Tf [(. High values for )] TJ ET
BT 109.644 308.500 Td /F5 9.8 Tf [(p)] TJ ET
BT 115.065 312.388 Td /F1 8.7 Tf [(+)] TJ ET
BT 120.126 308.500 Td /F1 9.8 Tf [( indicate high levels of clinical confirmation of the earlier discovery decision. Low levels of p— indicate a )] TJ ET
BT 26.250 296.595 Td /F1 9.8 Tf [(low level of missed opportunities for commercialization[5]. The transition from p to )] TJ ET
BT 380.623 296.595 Td /F5 9.8 Tf [(p)] TJ ET
BT 386.044 300.484 Td /F1 8.7 Tf [(+)] TJ ET
BT 391.106 296.595 Td /F1 9.8 Tf [( and )] TJ ET
BT 412.790 296.595 Td /F5 9.8 Tf [(p)] TJ ET
BT 418.211 300.484 Td /F1 8.7 Tf [(–)] TJ ET
BT 423.030 296.595 Td /F1 9.8 Tf [( models the learning about the true )] TJ ET
BT 26.250 284.691 Td /F1 9.8 Tf [(status of the universe of potential compounds )] TJ ET
BT 225.150 284.691 Td /F5 9.8 Tf [(H)] TJ ET
BT 232.190 284.691 Td /F1 9.8 Tf [(. Using the Bayesian logic set out above, it quantifies how inferences about the )] TJ ET
BT 26.250 272.786 Td /F1 9.8 Tf [(universe of potential compounds )] TJ ET
BT 169.321 272.786 Td /F5 9.8 Tf [(H)] TJ ET
BT 176.361 272.786 Td /F1 9.8 Tf [( are updated in the light of new evidence, provided by the experimentation strategy.)] TJ ET
BT 26.250 236.183 Td /F4 12.0 Tf [(Business performance)] TJ ET
BT 26.250 216.229 Td /F1 9.8 Tf [(To formally state the problem of an R&D manager having to choose between various experimentation strategies, the optimal )] TJ ET
BT 26.250 204.324 Td /F1 9.8 Tf [(business decision for a strategy )] TJ ET
BT 165.519 204.324 Td /F5 9.8 Tf [(d)] TJ ET
BT 170.940 208.213 Td /F5 8.7 Tf [(*)] TJ ET
BT 174.311 204.324 Td /F1 9.8 Tf [( can be modelled as follows \(Müller, 1999: 99; Parmigiani, 2002\):)] TJ ET
0.965 0.965 0.965 rg
26.250 126.944 555.000 67.500 re f
0.267 0.267 0.267 rg
26.250 194.444 m 581.250 194.444 l 581.250 193.694 l 26.250 193.694 l f
26.250 126.944 m 581.250 126.944 l 581.250 127.694 l 26.250 127.694 l f
q
48.000 0 0 48.000 35.250 136.694 cm /I1 Do
Q
0.500 0.500 0.500 rg
BT 35.250 177.294 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 167.294 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5B%7Bd%5E*%7D%20%3D%20%5Carg%20%7B%5Cmax%20_%7Bd%20%5Cin%20D%7D%7DU\(d\)%5Cqquad%20%5Cqquad%20\(3\)%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 26.250 109.920 Td /F1 9.8 Tf [(where)] TJ ET
0.965 0.965 0.965 rg
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Q
0.500 0.500 0.500 rg
BT 35.250 462.779 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 452.779 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5B%7Bc_1%7D%3A%7B%5Cpi%20%5E%20%2B%20%7D%20%3D%20p\(%7BC%5E%20%2B%20%7D%7C%7BH%5E%20%2B%20%7D\)%20%3D%20%5Cfrac%7B%7Bp\(%7BC%5E%20%2B%20%7D%2C%7BH%5E%20%2B%20%7D\)%7D%7D%7B%7Bp\(%7BH%5E%20%2B%20%7D\)%7D%7D%5Cqquad%20%5Cqquad%20\(1\)%5C%5D)] TJ ET
q
48.000 0 0 48.000 35.250 347.179 cm /I1 Do
Q
BT 35.250 387.779 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 377.779 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5B%7Bc_1%7D%3A%7B%5Cpi%20%5E%20-%20%7D%20%3D%20p\(%7BC%5E%20%2B%20%7D%7C%7BH%5E%20-%20%7D\)%20%3D%20%5Cfrac%7B%7Bp\(%7BC%5E%20%2B%20%7D%2C%7BH%5E%20-%20%7D\)%7D%7D%7B%7Bp\(%7BH%5E%20-%20%7D\)%7D%7D%5Cqquad%20%5Cqquad%20\(2\)%5C%5D)] TJ ET
q
48.000 0 0 48.000 35.250 136.694 cm /I1 Do
Q
BT 35.250 177.294 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 167.294 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5B%7Bd%5E*%7D%20%3D%20%5Carg%20%7B%5Cmax%20_%7Bd%20%5Cin%20D%7D%7DU\(d\)%5Cqquad%20%5Cqquad%20\(3\)%5C%5D)] TJ ET
q
0.000 0.000 0.000 rg
BT 291.710 19.825 Td /F1 11.0 Tf [(5)] TJ ET
BT 25.000 19.825 Td /F1 11.0 Tf [(Emergence: Complexity and Organization)] TJ ET
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BT 35.250 769.600 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 759.600 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5BU\(d\)%20%3D%20%5Cint%20u%20\(d%2C%5Ctheta%20%2Cy\)p\(%5Ctheta%20\)%7Bp_d%7D\(y%7C%5Ctheta%20\)d%5Ctheta%20dy%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 26.250 702.226 Td /F5 9.8 Tf [(U\(d\))] TJ ET
BT 45.204 702.226 Td /F1 9.8 Tf [( is the expected utility of an experimentation strategy d, an element of the universe of possible experimentation strategies )] TJ ET
BT 569.793 702.226 Td /F5 9.8 Tf [(D)] TJ ET
BT 26.250 690.321 Td /F1 9.8 Tf [(. The utility function )] TJ ET
BT 112.957 690.321 Td /F5 9.8 Tf [(u\(d,?,y\))] TJ ET
BT 146.009 690.321 Td /F1 9.8 Tf [( is in our case specified by solving a decision tree of the outcomes y of the various \()] TJ ET
BT 506.379 690.321 Td /F5 9.8 Tf [(H, C)] TJ ET
BT 525.879 690.321 Td /F1 9.8 Tf [(\) )] TJ ET
BT 26.250 678.417 Td /F1 9.8 Tf [(combinations. Ordering these combinations in a decision tree, and based on cost assumptions for each branch of the tree, a )] TJ ET
BT 26.250 666.512 Td /F1 9.8 Tf [(financial outcome can be calculated and used for comparison to make the best decision )] TJ ET
BT 406.685 666.512 Td /F5 9.8 Tf [(d)] TJ ET
BT 412.106 670.400 Td /F5 8.7 Tf [(*)] TJ ET
BT 415.478 666.512 Td /F1 9.8 Tf [(.)] TJ ET
BT 26.250 647.107 Td /F1 9.8 Tf [(Figure 4 gives a decision tree representation of the best choice )] TJ ET
BT 300.469 647.107 Td /F5 9.8 Tf [(d)] TJ ET
BT 305.890 650.995 Td /F1 8.7 Tf [(*)] TJ ET
BT 309.261 647.107 Td /F1 9.8 Tf [( \(depicted as a rectangle\) to be made between the three )] TJ ET
BT 26.250 635.202 Td /F1 9.8 Tf [(discovery experimentation strategies, building on )] TJ ET
BT 239.756 635.202 Td /F5 9.8 Tf [(p)] TJ ET
BT 245.177 639.091 Td /F1 8.7 Tf [(+)] TJ ET
BT 250.238 635.202 Td /F1 9.8 Tf [( and )] TJ ET
BT 271.922 635.202 Td /F5 9.8 Tf [(p)] TJ ET
BT 277.343 639.091 Td /F1 8.7 Tf [(–)] TJ ET
BT 282.162 635.202 Td /F1 9.8 Tf [( derived above. Overall, the depicted tree[6] represents the two-step )] TJ ET
BT 26.250 623.298 Td /F1 9.8 Tf [(decision to take a therapeutic agent to market.)] TJ ET
BT 26.250 603.893 Td /F1 9.8 Tf [(The best choice at the decision node is the experimentation strategy option corresponding to the branch that leads to the )] TJ ET
BT 26.250 591.988 Td /F1 9.8 Tf [(maximum value \(see a.o. Jensen, 2001\). Solving a decision tree in Figure 4 leads to the following utility or business value )] TJ ET
BT 551.892 591.988 Td /F5 9.8 Tf [(U\(d\))] TJ ET
BT 26.250 580.083 Td /F1 9.8 Tf [(for an experimentation strategy )] TJ ET
BT 163.355 580.083 Td /F5 9.8 Tf [(d)] TJ ET
BT 168.775 580.083 Td /F1 9.8 Tf [(:)] TJ ET
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48.000 0 0 48.000 35.250 512.452 cm /I1 Do
Q
0.500 0.500 0.500 rg
BT 35.250 553.052 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 543.052 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5B%5Cchi_%7B3%7D%3AU\(d\)%3D%5B%5Cpi%5E%7B%2B%7D\(R-C_%7BD%7D%2BC_%7BClin%7D\)p\(H%5E%7B%2B%7D\)-%5Cpi%5E%7B-%7D\(R%2BC_%7BD%7D-C_%7BClin%7D\)p\(H%5E%7B-%7D\)%5D-%5B\(1-%5Cpi%5E%7B%2B%7D\)\(C_%7BD%7D%2BC_%7BClin%7D\)p\(H%5E%7B%2B%7D\)%2B\(1-%5Cpi%5E%7B-%7D\)C_%7BD%7Dp\(H%5E%7B-%7D\)%5D%5Cqquad%20%5Cqquad%20\(4\)%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 26.250 485.679 Td /F1 9.8 Tf [(Calculating )] TJ ET
BT 77.184 485.679 Td /F5 9.8 Tf [(U\(d\))] TJ ET
BT 96.138 485.679 Td /F1 9.8 Tf [(, then, leads to the best business choice between various experimentation strategies; the strategy delivering the )] TJ ET
BT 26.250 473.774 Td /F1 9.8 Tf [(maximum business value will be preferred.)] TJ ET
BT 26.250 437.171 Td /F4 12.0 Tf [(Simulation study rationale and methodology)] TJ ET
BT 26.250 400.019 Td /F4 12.0 Tf [(Monte Carlo simulation study rationale)] TJ ET
BT 26.250 380.065 Td /F1 9.8 Tf [(Various reasons led us to conclude that computer-based simulation is the preferred research methodology to study the impact )] TJ ET
BT 26.250 368.160 Td /F1 9.8 Tf [(of various experimentation strategies on predictive and business performance using the Bayesian framework developed above.)] TJ ET
BT 26.250 348.756 Td /F1 9.8 Tf [(First, the dynamics of problem-solving behavior are not analytically tractable while they have to be represented using )] TJ ET
BT 26.250 336.851 Td /F1 9.8 Tf [(discontinuous nonlinear systems, which are generally hard to describe in closed form \(Mihm )] TJ ET
BT 425.054 336.851 Td /F5 9.8 Tf [(et al)] TJ ET
BT 443.482 336.851 Td /F1 9.8 Tf [(., 2003\).)] TJ ET
BT 26.250 288.570 Td /F1 9.8 Tf [(Second, some of the variables like )] TJ ET
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BT 177.443 319.570 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 177.443 309.570 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5BH_i%5E%20-%20%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 225.443 288.570 Td /F1 9.8 Tf [( or their derived probabilities like )] TJ ET
BT 367.949 288.570 Td /F5 9.8 Tf [(p\(C)] TJ ET
BT 383.656 328.553 Td /F5 8.7 Tf [(–)] TJ ET
BT 388.475 288.570 Td /F5 9.8 Tf [(|H)] TJ ET
BT 398.050 328.553 Td /F5 8.7 Tf [(+)] TJ ET
BT 403.111 288.570 Td /F5 9.8 Tf [(\))] TJ ET
BT 406.358 288.570 Td /F1 9.8 Tf [( or )] TJ ET
BT 420.447 288.570 Td /F5 9.8 Tf [(p\(C)] TJ ET
BT 436.154 328.553 Td /F5 8.7 Tf [(+)] TJ ET
BT 441.215 288.570 Td /F5 9.8 Tf [(|H)] TJ ET
BT 450.790 328.553 Td /F5 8.7 Tf [(–)] TJ ET
BT 455.608 288.570 Td /F5 9.8 Tf [(\))] TJ ET
BT 458.855 288.570 Td /F1 9.8 Tf [( among others are )] TJ ET
BT 26.250 269.446 Td /F1 9.8 Tf [(unobservable in real life, thereby resisting real-life experiments and empirical research \(Masuch & Lapotin, 1989\).)] TJ ET
BT 26.250 250.041 Td /F1 9.8 Tf [(And finally, a Monte Carlo simulation-based research methodology allows us to vary underlying assumptions and to search )] TJ ET
BT 26.250 238.137 Td /F1 9.8 Tf [(virtually for optimal experimentation approaches in very high-dimensional spaces without disrupting practice \(Parmigiani, 2002\).)] TJ ET
BT 26.250 201.534 Td /F4 12.0 Tf [(Generating a virtual search space)] TJ ET
BT 26.250 181.580 Td /F1 9.8 Tf [(To run a simulation study, a virtual cohort of candidate therapeutic agents or chemical compounds needs to be generated and )] TJ ET
BT 26.250 169.675 Td /F1 9.8 Tf [(used to calculate the probability density functions cited above. More specifically, we chose to model a virtual compound having )] TJ ET
BT 26.250 157.770 Td /F1 9.8 Tf [(three fundamental virtual properties: Potency \(P\), Bioavailability \(B\), and Toxicity \(T\).)] TJ ET
BT 26.250 138.366 Td /F1 9.8 Tf [(Now, as depicted in Figure 5, the extant search space is the total number of virtual compounds it contains. It is generated in )] TJ ET
BT 26.250 126.461 Td /F1 9.8 Tf [(several steps. First, classes get assigned a value for \(P\), \(B\), and \(T\) through random sampling of the exponential distributions )] TJ ET
BT 26.250 114.556 Td /F1 9.8 Tf [(we defined for these properties. Then, around these class values, reference compounds are randomly generated using a normal )] TJ ET
BT 26.250 102.651 Td /F1 9.8 Tf [(distribution. This is done while, in reality, classes are investigated by scientists in HTS and H2L by using a set of reference )] TJ ET
BT 26.250 90.747 Td /F1 9.8 Tf [(compounds describing the classes. Finally, virtual compounds are randomly generated around reference compounds using a )] TJ ET
BT 26.250 78.842 Td /F1 9.8 Tf [(normal distribution.)] TJ ET
BT 26.250 59.437 Td /F1 9.8 Tf [(The simulation experiment, then, uses this virtual search space as input to the discovery screening and optimization )] TJ ET
BT 26.250 47.532 Td /F1 9.8 Tf [(experimentation process. Before analyzing predictive and business performance of the three experimentation strategies — old )] TJ ET
Q
q
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BT 35.250 769.600 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 759.600 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5BU\(d\)%20%3D%20%5Cint%20u%20\(d%2C%5Ctheta%20%2Cy\)p\(%5Ctheta%20\)%7Bp_d%7D\(y%7C%5Ctheta%20\)d%5Ctheta%20dy%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 26.250 702.226 Td /F5 9.8 Tf [(U\(d\))] TJ ET
BT 45.204 702.226 Td /F1 9.8 Tf [( is the expected utility of an experimentation strategy d, an element of the universe of possible experimentation strategies )] TJ ET
BT 569.793 702.226 Td /F5 9.8 Tf [(D)] TJ ET
BT 26.250 690.321 Td /F1 9.8 Tf [(. The utility function )] TJ ET
BT 112.957 690.321 Td /F5 9.8 Tf [(u\(d,?,y\))] TJ ET
BT 146.009 690.321 Td /F1 9.8 Tf [( is in our case specified by solving a decision tree of the outcomes y of the various \()] TJ ET
BT 506.379 690.321 Td /F5 9.8 Tf [(H, C)] TJ ET
BT 525.879 690.321 Td /F1 9.8 Tf [(\) )] TJ ET
BT 26.250 678.417 Td /F1 9.8 Tf [(combinations. Ordering these combinations in a decision tree, and based on cost assumptions for each branch of the tree, a )] TJ ET
BT 26.250 666.512 Td /F1 9.8 Tf [(financial outcome can be calculated and used for comparison to make the best decision )] TJ ET
BT 406.685 666.512 Td /F5 9.8 Tf [(d)] TJ ET
BT 412.106 670.400 Td /F5 8.7 Tf [(*)] TJ ET
BT 415.478 666.512 Td /F1 9.8 Tf [(.)] TJ ET
BT 26.250 647.107 Td /F1 9.8 Tf [(Figure 4 gives a decision tree representation of the best choice )] TJ ET
BT 300.469 647.107 Td /F5 9.8 Tf [(d)] TJ ET
BT 305.890 650.995 Td /F1 8.7 Tf [(*)] TJ ET
BT 309.261 647.107 Td /F1 9.8 Tf [( \(depicted as a rectangle\) to be made between the three )] TJ ET
BT 26.250 635.202 Td /F1 9.8 Tf [(discovery experimentation strategies, building on )] TJ ET
BT 239.756 635.202 Td /F5 9.8 Tf [(p)] TJ ET
BT 245.177 639.091 Td /F1 8.7 Tf [(+)] TJ ET
BT 250.238 635.202 Td /F1 9.8 Tf [( and )] TJ ET
BT 271.922 635.202 Td /F5 9.8 Tf [(p)] TJ ET
BT 277.343 639.091 Td /F1 8.7 Tf [(–)] TJ ET
BT 282.162 635.202 Td /F1 9.8 Tf [( derived above. Overall, the depicted tree[6] represents the two-step )] TJ ET
BT 26.250 623.298 Td /F1 9.8 Tf [(decision to take a therapeutic agent to market.)] TJ ET
BT 26.250 603.893 Td /F1 9.8 Tf [(The best choice at the decision node is the experimentation strategy option corresponding to the branch that leads to the )] TJ ET
BT 26.250 591.988 Td /F1 9.8 Tf [(maximum value \(see a.o. Jensen, 2001\). Solving a decision tree in Figure 4 leads to the following utility or business value )] TJ ET
BT 551.892 591.988 Td /F5 9.8 Tf [(U\(d\))] TJ ET
BT 26.250 580.083 Td /F1 9.8 Tf [(for an experimentation strategy )] TJ ET
BT 163.355 580.083 Td /F5 9.8 Tf [(d)] TJ ET
BT 168.775 580.083 Td /F1 9.8 Tf [(:)] TJ ET
0.965 0.965 0.965 rg
26.250 502.702 555.000 67.500 re f
0.267 0.267 0.267 rg
26.250 570.202 m 581.250 570.202 l 581.250 569.452 l 26.250 569.452 l f
26.250 502.702 m 581.250 502.702 l 581.250 503.452 l 26.250 503.452 l f
q
48.000 0 0 48.000 35.250 512.452 cm /I1 Do
Q
0.500 0.500 0.500 rg
BT 35.250 553.052 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 543.052 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5B%5Cchi_%7B3%7D%3AU\(d\)%3D%5B%5Cpi%5E%7B%2B%7D\(R-C_%7BD%7D%2BC_%7BClin%7D\)p\(H%5E%7B%2B%7D\)-%5Cpi%5E%7B-%7D\(R%2BC_%7BD%7D-C_%7BClin%7D\)p\(H%5E%7B-%7D\)%5D-%5B\(1-%5Cpi%5E%7B%2B%7D\)\(C_%7BD%7D%2BC_%7BClin%7D\)p\(H%5E%7B%2B%7D\)%2B\(1-%5Cpi%5E%7B-%7D\)C_%7BD%7Dp\(H%5E%7B-%7D\)%5D%5Cqquad%20%5Cqquad%20\(4\)%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 26.250 485.679 Td /F1 9.8 Tf [(Calculating )] TJ ET
BT 77.184 485.679 Td /F5 9.8 Tf [(U\(d\))] TJ ET
BT 96.138 485.679 Td /F1 9.8 Tf [(, then, leads to the best business choice between various experimentation strategies; the strategy delivering the )] TJ ET
BT 26.250 473.774 Td /F1 9.8 Tf [(maximum business value will be preferred.)] TJ ET
BT 26.250 437.171 Td /F4 12.0 Tf [(Simulation study rationale and methodology)] TJ ET
BT 26.250 400.019 Td /F4 12.0 Tf [(Monte Carlo simulation study rationale)] TJ ET
BT 26.250 380.065 Td /F1 9.8 Tf [(Various reasons led us to conclude that computer-based simulation is the preferred research methodology to study the impact )] TJ ET
BT 26.250 368.160 Td /F1 9.8 Tf [(of various experimentation strategies on predictive and business performance using the Bayesian framework developed above.)] TJ ET
BT 26.250 348.756 Td /F1 9.8 Tf [(First, the dynamics of problem-solving behavior are not analytically tractable while they have to be represented using )] TJ ET
BT 26.250 336.851 Td /F1 9.8 Tf [(discontinuous nonlinear systems, which are generally hard to describe in closed form \(Mihm )] TJ ET
BT 425.054 336.851 Td /F5 9.8 Tf [(et al)] TJ ET
BT 443.482 336.851 Td /F1 9.8 Tf [(., 2003\).)] TJ ET
BT 26.250 288.570 Td /F1 9.8 Tf [(Second, some of the variables like )] TJ ET
q
48.000 0 0 48.000 177.443 278.970 cm /I1 Do
Q
0.500 0.500 0.500 rg
BT 177.443 319.570 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 177.443 309.570 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5BH_i%5E%20-%20%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 225.443 288.570 Td /F1 9.8 Tf [( or their derived probabilities like )] TJ ET
BT 367.949 288.570 Td /F5 9.8 Tf [(p\(C)] TJ ET
BT 383.656 328.553 Td /F5 8.7 Tf [(–)] TJ ET
BT 388.475 288.570 Td /F5 9.8 Tf [(|H)] TJ ET
BT 398.050 328.553 Td /F5 8.7 Tf [(+)] TJ ET
BT 403.111 288.570 Td /F5 9.8 Tf [(\))] TJ ET
BT 406.358 288.570 Td /F1 9.8 Tf [( or )] TJ ET
BT 420.447 288.570 Td /F5 9.8 Tf [(p\(C)] TJ ET
BT 436.154 328.553 Td /F5 8.7 Tf [(+)] TJ ET
BT 441.215 288.570 Td /F5 9.8 Tf [(|H)] TJ ET
BT 450.790 328.553 Td /F5 8.7 Tf [(–)] TJ ET
BT 455.608 288.570 Td /F5 9.8 Tf [(\))] TJ ET
BT 458.855 288.570 Td /F1 9.8 Tf [( among others are )] TJ ET
BT 26.250 269.446 Td /F1 9.8 Tf [(unobservable in real life, thereby resisting real-life experiments and empirical research \(Masuch & Lapotin, 1989\).)] TJ ET
BT 26.250 250.041 Td /F1 9.8 Tf [(And finally, a Monte Carlo simulation-based research methodology allows us to vary underlying assumptions and to search )] TJ ET
BT 26.250 238.137 Td /F1 9.8 Tf [(virtually for optimal experimentation approaches in very high-dimensional spaces without disrupting practice \(Parmigiani, 2002\).)] TJ ET
BT 26.250 201.534 Td /F4 12.0 Tf [(Generating a virtual search space)] TJ ET
BT 26.250 181.580 Td /F1 9.8 Tf [(To run a simulation study, a virtual cohort of candidate therapeutic agents or chemical compounds needs to be generated and )] TJ ET
BT 26.250 169.675 Td /F1 9.8 Tf [(used to calculate the probability density functions cited above. More specifically, we chose to model a virtual compound having )] TJ ET
BT 26.250 157.770 Td /F1 9.8 Tf [(three fundamental virtual properties: Potency \(P\), Bioavailability \(B\), and Toxicity \(T\).)] TJ ET
BT 26.250 138.366 Td /F1 9.8 Tf [(Now, as depicted in Figure 5, the extant search space is the total number of virtual compounds it contains. It is generated in )] TJ ET
BT 26.250 126.461 Td /F1 9.8 Tf [(several steps. First, classes get assigned a value for \(P\), \(B\), and \(T\) through random sampling of the exponential distributions )] TJ ET
BT 26.250 114.556 Td /F1 9.8 Tf [(we defined for these properties. Then, around these class values, reference compounds are randomly generated using a normal )] TJ ET
BT 26.250 102.651 Td /F1 9.8 Tf [(distribution. This is done while, in reality, classes are investigated by scientists in HTS and H2L by using a set of reference )] TJ ET
BT 26.250 90.747 Td /F1 9.8 Tf [(compounds describing the classes. Finally, virtual compounds are randomly generated around reference compounds using a )] TJ ET
BT 26.250 78.842 Td /F1 9.8 Tf [(normal distribution.)] TJ ET
BT 26.250 59.437 Td /F1 9.8 Tf [(The simulation experiment, then, uses this virtual search space as input to the discovery screening and optimization )] TJ ET
BT 26.250 47.532 Td /F1 9.8 Tf [(experimentation process. Before analyzing predictive and business performance of the three experimentation strategies — old )] TJ ET
Q
q
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0.965 0.965 0.965 rg
26.250 719.250 555.000 57.750 re f
0.267 0.267 0.267 rg
0.267 0.267 0.267 RG
26.250 719.250 m 581.250 719.250 l 581.250 720.000 l 26.250 720.000 l f
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Q
0.500 0.500 0.500 rg
BT 35.250 769.600 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 759.600 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5BU\(d\)%20%3D%20%5Cint%20u%20\(d%2C%5Ctheta%20%2Cy\)p\(%5Ctheta%20\)%7Bp_d%7D\(y%7C%5Ctheta%20\)d%5Ctheta%20dy%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 26.250 702.226 Td /F5 9.8 Tf [(U\(d\))] TJ ET
BT 45.204 702.226 Td /F1 9.8 Tf [( is the expected utility of an experimentation strategy d, an element of the universe of possible experimentation strategies )] TJ ET
BT 569.793 702.226 Td /F5 9.8 Tf [(D)] TJ ET
BT 26.250 690.321 Td /F1 9.8 Tf [(. The utility function )] TJ ET
BT 112.957 690.321 Td /F5 9.8 Tf [(u\(d,?,y\))] TJ ET
BT 146.009 690.321 Td /F1 9.8 Tf [( is in our case specified by solving a decision tree of the outcomes y of the various \()] TJ ET
BT 506.379 690.321 Td /F5 9.8 Tf [(H, C)] TJ ET
BT 525.879 690.321 Td /F1 9.8 Tf [(\) )] TJ ET
BT 26.250 678.417 Td /F1 9.8 Tf [(combinations. Ordering these combinations in a decision tree, and based on cost assumptions for each branch of the tree, a )] TJ ET
BT 26.250 666.512 Td /F1 9.8 Tf [(financial outcome can be calculated and used for comparison to make the best decision )] TJ ET
BT 406.685 666.512 Td /F5 9.8 Tf [(d)] TJ ET
BT 412.106 670.400 Td /F5 8.7 Tf [(*)] TJ ET
BT 415.478 666.512 Td /F1 9.8 Tf [(.)] TJ ET
BT 26.250 647.107 Td /F1 9.8 Tf [(Figure 4 gives a decision tree representation of the best choice )] TJ ET
BT 300.469 647.107 Td /F5 9.8 Tf [(d)] TJ ET
BT 305.890 650.995 Td /F1 8.7 Tf [(*)] TJ ET
BT 309.261 647.107 Td /F1 9.8 Tf [( \(depicted as a rectangle\) to be made between the three )] TJ ET
BT 26.250 635.202 Td /F1 9.8 Tf [(discovery experimentation strategies, building on )] TJ ET
BT 239.756 635.202 Td /F5 9.8 Tf [(p)] TJ ET
BT 245.177 639.091 Td /F1 8.7 Tf [(+)] TJ ET
BT 250.238 635.202 Td /F1 9.8 Tf [( and )] TJ ET
BT 271.922 635.202 Td /F5 9.8 Tf [(p)] TJ ET
BT 277.343 639.091 Td /F1 8.7 Tf [(–)] TJ ET
BT 282.162 635.202 Td /F1 9.8 Tf [( derived above. Overall, the depicted tree[6] represents the two-step )] TJ ET
BT 26.250 623.298 Td /F1 9.8 Tf [(decision to take a therapeutic agent to market.)] TJ ET
BT 26.250 603.893 Td /F1 9.8 Tf [(The best choice at the decision node is the experimentation strategy option corresponding to the branch that leads to the )] TJ ET
BT 26.250 591.988 Td /F1 9.8 Tf [(maximum value \(see a.o. Jensen, 2001\). Solving a decision tree in Figure 4 leads to the following utility or business value )] TJ ET
BT 551.892 591.988 Td /F5 9.8 Tf [(U\(d\))] TJ ET
BT 26.250 580.083 Td /F1 9.8 Tf [(for an experimentation strategy )] TJ ET
BT 163.355 580.083 Td /F5 9.8 Tf [(d)] TJ ET
BT 168.775 580.083 Td /F1 9.8 Tf [(:)] TJ ET
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Q
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BT 35.250 553.052 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 543.052 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5B%5Cchi_%7B3%7D%3AU\(d\)%3D%5B%5Cpi%5E%7B%2B%7D\(R-C_%7BD%7D%2BC_%7BClin%7D\)p\(H%5E%7B%2B%7D\)-%5Cpi%5E%7B-%7D\(R%2BC_%7BD%7D-C_%7BClin%7D\)p\(H%5E%7B-%7D\)%5D-%5B\(1-%5Cpi%5E%7B%2B%7D\)\(C_%7BD%7D%2BC_%7BClin%7D\)p\(H%5E%7B%2B%7D\)%2B\(1-%5Cpi%5E%7B-%7D\)C_%7BD%7Dp\(H%5E%7B-%7D\)%5D%5Cqquad%20%5Cqquad%20\(4\)%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 26.250 485.679 Td /F1 9.8 Tf [(Calculating )] TJ ET
BT 77.184 485.679 Td /F5 9.8 Tf [(U\(d\))] TJ ET
BT 96.138 485.679 Td /F1 9.8 Tf [(, then, leads to the best business choice between various experimentation strategies; the strategy delivering the )] TJ ET
BT 26.250 473.774 Td /F1 9.8 Tf [(maximum business value will be preferred.)] TJ ET
BT 26.250 437.171 Td /F4 12.0 Tf [(Simulation study rationale and methodology)] TJ ET
BT 26.250 400.019 Td /F4 12.0 Tf [(Monte Carlo simulation study rationale)] TJ ET
BT 26.250 380.065 Td /F1 9.8 Tf [(Various reasons led us to conclude that computer-based simulation is the preferred research methodology to study the impact )] TJ ET
BT 26.250 368.160 Td /F1 9.8 Tf [(of various experimentation strategies on predictive and business performance using the Bayesian framework developed above.)] TJ ET
BT 26.250 348.756 Td /F1 9.8 Tf [(First, the dynamics of problem-solving behavior are not analytically tractable while they have to be represented using )] TJ ET
BT 26.250 336.851 Td /F1 9.8 Tf [(discontinuous nonlinear systems, which are generally hard to describe in closed form \(Mihm )] TJ ET
BT 425.054 336.851 Td /F5 9.8 Tf [(et al)] TJ ET
BT 443.482 336.851 Td /F1 9.8 Tf [(., 2003\).)] TJ ET
BT 26.250 288.570 Td /F1 9.8 Tf [(Second, some of the variables like )] TJ ET
q
48.000 0 0 48.000 177.443 278.970 cm /I1 Do
Q
0.500 0.500 0.500 rg
BT 177.443 319.570 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 177.443 309.570 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5BH_i%5E%20-%20%5C%5D)] TJ ET
0.271 0.267 0.267 rg
BT 225.443 288.570 Td /F1 9.8 Tf [( or their derived probabilities like )] TJ ET
BT 367.949 288.570 Td /F5 9.8 Tf [(p\(C)] TJ ET
BT 383.656 328.553 Td /F5 8.7 Tf [(–)] TJ ET
BT 388.475 288.570 Td /F5 9.8 Tf [(|H)] TJ ET
BT 398.050 328.553 Td /F5 8.7 Tf [(+)] TJ ET
BT 403.111 288.570 Td /F5 9.8 Tf [(\))] TJ ET
BT 406.358 288.570 Td /F1 9.8 Tf [( or )] TJ ET
BT 420.447 288.570 Td /F5 9.8 Tf [(p\(C)] TJ ET
BT 436.154 328.553 Td /F5 8.7 Tf [(+)] TJ ET
BT 441.215 288.570 Td /F5 9.8 Tf [(|H)] TJ ET
BT 450.790 328.553 Td /F5 8.7 Tf [(–)] TJ ET
BT 455.608 288.570 Td /F5 9.8 Tf [(\))] TJ ET
BT 458.855 288.570 Td /F1 9.8 Tf [( among others are )] TJ ET
BT 26.250 269.446 Td /F1 9.8 Tf [(unobservable in real life, thereby resisting real-life experiments and empirical research \(Masuch & Lapotin, 1989\).)] TJ ET
BT 26.250 250.041 Td /F1 9.8 Tf [(And finally, a Monte Carlo simulation-based research methodology allows us to vary underlying assumptions and to search )] TJ ET
BT 26.250 238.137 Td /F1 9.8 Tf [(virtually for optimal experimentation approaches in very high-dimensional spaces without disrupting practice \(Parmigiani, 2002\).)] TJ ET
BT 26.250 201.534 Td /F4 12.0 Tf [(Generating a virtual search space)] TJ ET
BT 26.250 181.580 Td /F1 9.8 Tf [(To run a simulation study, a virtual cohort of candidate therapeutic agents or chemical compounds needs to be generated and )] TJ ET
BT 26.250 169.675 Td /F1 9.8 Tf [(used to calculate the probability density functions cited above. More specifically, we chose to model a virtual compound having )] TJ ET
BT 26.250 157.770 Td /F1 9.8 Tf [(three fundamental virtual properties: Potency \(P\), Bioavailability \(B\), and Toxicity \(T\).)] TJ ET
BT 26.250 138.366 Td /F1 9.8 Tf [(Now, as depicted in Figure 5, the extant search space is the total number of virtual compounds it contains. It is generated in )] TJ ET
BT 26.250 126.461 Td /F1 9.8 Tf [(several steps. First, classes get assigned a value for \(P\), \(B\), and \(T\) through random sampling of the exponential distributions )] TJ ET
BT 26.250 114.556 Td /F1 9.8 Tf [(we defined for these properties. Then, around these class values, reference compounds are randomly generated using a normal )] TJ ET
BT 26.250 102.651 Td /F1 9.8 Tf [(distribution. This is done while, in reality, classes are investigated by scientists in HTS and H2L by using a set of reference )] TJ ET
BT 26.250 90.747 Td /F1 9.8 Tf [(compounds describing the classes. Finally, virtual compounds are randomly generated around reference compounds using a )] TJ ET
BT 26.250 78.842 Td /F1 9.8 Tf [(normal distribution.)] TJ ET
BT 26.250 59.437 Td /F1 9.8 Tf [(The simulation experiment, then, uses this virtual search space as input to the discovery screening and optimization )] TJ ET
BT 26.250 47.532 Td /F1 9.8 Tf [(experimentation process. Before analyzing predictive and business performance of the three experimentation strategies — old )] TJ ET
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BT 35.250 769.600 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 759.600 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5BU\(d\)%20%3D%20%5Cint%20u%20\(d%2C%5Ctheta%20%2Cy\)p\(%5Ctheta%20\)%7Bp_d%7D\(y%7C%5Ctheta%20\)d%5Ctheta%20dy%5C%5D)] TJ ET
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48.000 0 0 48.000 35.250 512.452 cm /I1 Do
Q
BT 35.250 553.052 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 543.052 Td /F1 8.0 Tf [(https://chart.googleapis.com/chart?cht=tx&chf=bg,s,FFFFFF00&chco=000000&chl=%5C%5B%5Cchi_%7B3%7D%3AU\(d\)%3D%5B%5Cpi%5E%7B%2B%7D\(R-C_%7BD%7D%2BC_%7BClin%7D\)p\(H%5E%7B%2B%7D\)-%5Cpi%5E%7B-%7D\(R%2BC_%7BD%7D-C_%7BClin%7D\)p\(H%5E%7B-%7D\)%5D-%5B\(1-%5Cpi%5E%7B%2B%7D\)\(C_%7BD%7D%2BC_%7BClin%7D\)p\(H%5E%7B%2B%7D\)%2B\(1-%5Cpi%5E%7B-%7D\)C_%7BD%7Dp\(H%5E%7B-%7D\)%5D%5Cqquad%20%5Cqquad%20\(4\)%5C%5D)] TJ ET
q
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BT 291.710 19.825 Td /F1 11.0 Tf [(6)] TJ ET
BT 25.000 19.825 Td /F1 11.0 Tf [(Emergence: Complexity and Organization)] TJ ET
Q
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BT 26.250 767.476 Td /F1 9.8 Tf [(paradigm, front-loaded \(FL\) paradigm, Early front loading \(EFL\), we need to explain how the experimentation process was )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(emulated in the simulation.)] TJ ET
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BT 35.250 700.540 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 690.540 Td /F1 8.0 Tf [(https://journal.emergentpublications.com/wp-content/uploads/2015/11/e5bcf9c9-9061-64fe-9ca1-48d8d6a0d289-300x237.png)] TJ ET
q
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BT 35.250 672.417 Td /F4 9.8 Tf [(Fig. 4: Utility calculation and optimal strategy choice decision tree model)] TJ ET
Q
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BT 35.250 607.636 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 597.636 Td /F1 8.0 Tf [(https://journal.emergentpublications.com/wp-content/uploads/2015/11/c257e0da-3b5c-50cc-6de3-8476db32086e-300x285.png)] TJ ET
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BT 35.250 579.512 Td /F4 9.8 Tf [(Fig. 5: Virtual search space composition)] TJ ET
Q
BT 26.250 533.159 Td /F4 12.0 Tf [(Adaptive systems emulation of the discovery process)] TJ ET
BT 26.250 513.205 Td /F1 9.8 Tf [(Using a simulation methodology, a formal model of the discovery research process needs to be designed. Among garbage can )] TJ ET
BT 26.250 501.300 Td /F1 9.8 Tf [(models \(Cohen )] TJ ET
BT 95.066 501.300 Td /F5 9.8 Tf [(et al)] TJ ET
BT 113.493 501.300 Td /F1 9.8 Tf [(., 1972\) and physical symbol systems \(Newell & Simon, 1976; Masuch & Lapotin, 1989\) of organizational )] TJ ET
BT 26.250 489.396 Td /F1 9.8 Tf [(decision making, massively parallel connectionist \(Rumelhart )] TJ ET
BT 291.743 489.396 Td /F5 9.8 Tf [(et al)] TJ ET
BT 310.170 489.396 Td /F1 9.8 Tf [(., 1986\) and evolutionary computation models like adaptive )] TJ ET
BT 26.250 477.491 Td /F1 9.8 Tf [(systems \(Holland, 1992, 1998; Mitchell, 2001\), we argue that the latter adaptive system \(Holland, 1992\) is the best artificial )] TJ ET
BT 26.250 465.586 Td /F1 9.8 Tf [(reconstruction of the mental modeling process during pharmaceutical discovery research since it mimics best the “selection and )] TJ ET
BT 26.250 453.681 Td /F1 9.8 Tf [(optimization” search for an NME in a vast, discontinuous multi-factorial solution space.)] TJ ET
BT 26.250 434.277 Td /F1 9.8 Tf [(According to Mitchell \(2001\), this adaptive search method 1\) initially generates a set of candidate solutions; 2\) evaluates the )] TJ ET
BT 26.250 422.372 Td /F1 9.8 Tf [(candidate solutions according to some fitness criteria; 3\) decides on the basis of this evaluation which candidates will be kept )] TJ ET
BT 26.250 410.467 Td /F1 9.8 Tf [(and which will be discarded; and 4\) produces further variants by using some kind of operators on the surviving candidates.)] TJ ET
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BT 35.250 381.818 Td /F1 9.8 Tf [(eb87f9b7-d8ce-2abb-6bd7-c3637462ce98)] TJ ET
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BT 35.250 355.436 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 345.436 Td /F1 8.0 Tf [(https://journal.emergentpublications.com/wp-content/uploads/2015/11/eb87f9b7-d8ce-2abb-6bd7-c3637462ce98-300x119.png)] TJ ET
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BT 35.250 327.312 Td /F4 9.8 Tf [(Fig. 6: Adaptive systems model of the discovery experimentation process)] TJ ET
Q
BT 26.250 298.158 Td /F1 9.8 Tf [(Therefore, an annotated[7] adaptive systems model served as the theoretical paradigm underlying the simulation study, in which )] TJ ET
BT 26.250 286.253 Td /F1 9.8 Tf [(each experimentation strategy used in a pharmaceutical discovery context is modeled to execute an adaptive plan.)] TJ ET
BT 26.250 266.848 Td /F1 9.8 Tf [(Referring to Figure 6 and using Holland’s \(1992\) notation, this adaptive plan t starts from the universe of potential compounds )] TJ ET
BT 26.250 254.943 Td /F1 9.8 Tf [(representing the extant search space and initial uncertainty about the environment. Only an extremely small fraction of this )] TJ ET
BT 26.250 243.039 Td /F1 9.8 Tf [(universe is composed of candidate compounds with therapeutic effect. Through the execution of successive “noisy” selection )] TJ ET
BT 26.250 231.134 Td /F1 9.8 Tf [(and optimization loops, the total set of chemical structures attainable at moment )] TJ ET
BT 373.077 231.134 Td /F5 9.8 Tf [(t)] TJ ET
BT 375.788 231.134 Td /F1 9.8 Tf [(, )] TJ ET
BT 381.209 231.134 Td /F5 9.8 Tf [(?\(t\))] TJ ET
BT 395.834 231.134 Td /F1 9.8 Tf [(, is modified by a set of operators O\()] TJ ET
BT 552.984 231.134 Td /F5 9.8 Tf [(t\))] TJ ET
BT 26.250 219.229 Td /F1 9.8 Tf [(based on information I\()] TJ ET
BT 125.963 219.229 Td /F5 9.8 Tf [(t\))] TJ ET
BT 131.921 219.229 Td /F1 9.8 Tf [( received by t at time )] TJ ET
BT 224.584 219.229 Td /F5 9.8 Tf [(t)] TJ ET
BT 227.295 219.229 Td /F1 9.8 Tf [(. This way, the adaptive plan improves the fit of the solution with the target.)] TJ ET
BT 26.250 199.824 Td /F1 9.8 Tf [(The screening and optimization process on a validated target starts in HTS at the level of the chemical classes to end at )] TJ ET
BT 26.250 187.920 Td /F1 9.8 Tf [(candidate compound level. The top-performing classes and compounds are selected using payoff information on two or more )] TJ ET
BT 26.250 176.015 Td /F1 9.8 Tf [(properties. I\()] TJ ET
BT 80.977 176.015 Td /F5 9.8 Tf [(t\))] TJ ET
BT 86.934 176.015 Td /F1 9.8 Tf [( varies depending on the experimentation strategy used; or only biological activity information is used to find the )] TJ ET
BT 26.250 164.110 Td /F1 9.8 Tf [(best performer; or a multi-objective function combining payoff information on both biological activity and bio-availability )] TJ ET
BT 26.250 152.205 Td /F1 9.8 Tf [(dimensions is used to find the best-performing compounds in the search space. All experimentation strategies are modeled to )] TJ ET
BT 26.250 140.301 Td /F1 9.8 Tf [(only deal with chemical class-related performance in HTS and H2L, and with chemical compound-related performance in LO. )] TJ ET
BT 26.250 128.396 Td /F1 9.8 Tf [(Both front-loaded and early front-loaded strategies use multi-objective optimization, but start doing so at different moments in )] TJ ET
BT 26.250 116.491 Td /F1 9.8 Tf [(the process. The chosen multi-objective function conservatively takes the minimum of both \(P\) and \(B\) detector values as the )] TJ ET
BT 26.250 104.586 Td /F1 9.8 Tf [(calculated performance value for the chemical class or candidate compound, depending on the discovery research phase. At all )] TJ ET
BT 26.250 92.682 Td /F1 9.8 Tf [(stages a compound’s toxicology profile \(T\) needs a minimum level to pass.)] TJ ET
BT 26.250 73.277 Td /F1 9.8 Tf [(The conceptual model specified above formed the basis to build a computer simulation program in Excel Visual Basic )] TJ ET
BT 26.250 61.372 Td /F1 9.8 Tf [(Applications \(VBA\). Theory building results using simulation-based experiments will be discussed in the following paragraphs.)] TJ ET
Q
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BT 26.250 767.476 Td /F1 9.8 Tf [(paradigm, front-loaded \(FL\) paradigm, Early front loading \(EFL\), we need to explain how the experimentation process was )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(emulated in the simulation.)] TJ ET
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BT 35.250 726.922 Td /F1 9.8 Tf [(e5bcf9c9-9061-64fe-9ca1-48d8d6a0d289)] TJ ET
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BT 35.250 700.540 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 690.540 Td /F1 8.0 Tf [(https://journal.emergentpublications.com/wp-content/uploads/2015/11/e5bcf9c9-9061-64fe-9ca1-48d8d6a0d289-300x237.png)] TJ ET
q
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BT 35.250 672.417 Td /F4 9.8 Tf [(Fig. 4: Utility calculation and optimal strategy choice decision tree model)] TJ ET
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BT 35.250 607.636 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 597.636 Td /F1 8.0 Tf [(https://journal.emergentpublications.com/wp-content/uploads/2015/11/c257e0da-3b5c-50cc-6de3-8476db32086e-300x285.png)] TJ ET
q
35.250 571.131 537.000 17.905 re W n
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BT 35.250 579.512 Td /F4 9.8 Tf [(Fig. 5: Virtual search space composition)] TJ ET
Q
BT 26.250 533.159 Td /F4 12.0 Tf [(Adaptive systems emulation of the discovery process)] TJ ET
BT 26.250 513.205 Td /F1 9.8 Tf [(Using a simulation methodology, a formal model of the discovery research process needs to be designed. Among garbage can )] TJ ET
BT 26.250 501.300 Td /F1 9.8 Tf [(models \(Cohen )] TJ ET
BT 95.066 501.300 Td /F5 9.8 Tf [(et al)] TJ ET
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BT 291.743 489.396 Td /F5 9.8 Tf [(et al)] TJ ET
BT 310.170 489.396 Td /F1 9.8 Tf [(., 1986\) and evolutionary computation models like adaptive )] TJ ET
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BT 26.250 465.586 Td /F1 9.8 Tf [(reconstruction of the mental modeling process during pharmaceutical discovery research since it mimics best the “selection and )] TJ ET
BT 26.250 453.681 Td /F1 9.8 Tf [(optimization” search for an NME in a vast, discontinuous multi-factorial solution space.)] TJ ET
BT 26.250 434.277 Td /F1 9.8 Tf [(According to Mitchell \(2001\), this adaptive search method 1\) initially generates a set of candidate solutions; 2\) evaluates the )] TJ ET
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BT 26.250 410.467 Td /F1 9.8 Tf [(and which will be discarded; and 4\) produces further variants by using some kind of operators on the surviving candidates.)] TJ ET
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BT 35.250 327.312 Td /F4 9.8 Tf [(Fig. 6: Adaptive systems model of the discovery experimentation process)] TJ ET
Q
BT 26.250 298.158 Td /F1 9.8 Tf [(Therefore, an annotated[7] adaptive systems model served as the theoretical paradigm underlying the simulation study, in which )] TJ ET
BT 26.250 286.253 Td /F1 9.8 Tf [(each experimentation strategy used in a pharmaceutical discovery context is modeled to execute an adaptive plan.)] TJ ET
BT 26.250 266.848 Td /F1 9.8 Tf [(Referring to Figure 6 and using Holland’s \(1992\) notation, this adaptive plan t starts from the universe of potential compounds )] TJ ET
BT 26.250 254.943 Td /F1 9.8 Tf [(representing the extant search space and initial uncertainty about the environment. Only an extremely small fraction of this )] TJ ET
BT 26.250 243.039 Td /F1 9.8 Tf [(universe is composed of candidate compounds with therapeutic effect. Through the execution of successive “noisy” selection )] TJ ET
BT 26.250 231.134 Td /F1 9.8 Tf [(and optimization loops, the total set of chemical structures attainable at moment )] TJ ET
BT 373.077 231.134 Td /F5 9.8 Tf [(t)] TJ ET
BT 375.788 231.134 Td /F1 9.8 Tf [(, )] TJ ET
BT 381.209 231.134 Td /F5 9.8 Tf [(?\(t\))] TJ ET
BT 395.834 231.134 Td /F1 9.8 Tf [(, is modified by a set of operators O\()] TJ ET
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BT 26.250 219.229 Td /F1 9.8 Tf [(based on information I\()] TJ ET
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BT 131.921 219.229 Td /F1 9.8 Tf [( received by t at time )] TJ ET
BT 224.584 219.229 Td /F5 9.8 Tf [(t)] TJ ET
BT 227.295 219.229 Td /F1 9.8 Tf [(. This way, the adaptive plan improves the fit of the solution with the target.)] TJ ET
BT 26.250 199.824 Td /F1 9.8 Tf [(The screening and optimization process on a validated target starts in HTS at the level of the chemical classes to end at )] TJ ET
BT 26.250 187.920 Td /F1 9.8 Tf [(candidate compound level. The top-performing classes and compounds are selected using payoff information on two or more )] TJ ET
BT 26.250 176.015 Td /F1 9.8 Tf [(properties. I\()] TJ ET
BT 80.977 176.015 Td /F5 9.8 Tf [(t\))] TJ ET
BT 86.934 176.015 Td /F1 9.8 Tf [( varies depending on the experimentation strategy used; or only biological activity information is used to find the )] TJ ET
BT 26.250 164.110 Td /F1 9.8 Tf [(best performer; or a multi-objective function combining payoff information on both biological activity and bio-availability )] TJ ET
BT 26.250 152.205 Td /F1 9.8 Tf [(dimensions is used to find the best-performing compounds in the search space. All experimentation strategies are modeled to )] TJ ET
BT 26.250 140.301 Td /F1 9.8 Tf [(only deal with chemical class-related performance in HTS and H2L, and with chemical compound-related performance in LO. )] TJ ET
BT 26.250 128.396 Td /F1 9.8 Tf [(Both front-loaded and early front-loaded strategies use multi-objective optimization, but start doing so at different moments in )] TJ ET
BT 26.250 116.491 Td /F1 9.8 Tf [(the process. The chosen multi-objective function conservatively takes the minimum of both \(P\) and \(B\) detector values as the )] TJ ET
BT 26.250 104.586 Td /F1 9.8 Tf [(calculated performance value for the chemical class or candidate compound, depending on the discovery research phase. At all )] TJ ET
BT 26.250 92.682 Td /F1 9.8 Tf [(stages a compound’s toxicology profile \(T\) needs a minimum level to pass.)] TJ ET
BT 26.250 73.277 Td /F1 9.8 Tf [(The conceptual model specified above formed the basis to build a computer simulation program in Excel Visual Basic )] TJ ET
BT 26.250 61.372 Td /F1 9.8 Tf [(Applications \(VBA\). Theory building results using simulation-based experiments will be discussed in the following paragraphs.)] TJ ET
Q
q
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BT 26.250 755.571 Td /F1 9.8 Tf [(emulated in the simulation.)] TJ ET
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BT 35.250 672.417 Td /F4 9.8 Tf [(Fig. 4: Utility calculation and optimal strategy choice decision tree model)] TJ ET
Q
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BT 35.250 579.512 Td /F4 9.8 Tf [(Fig. 5: Virtual search space composition)] TJ ET
Q
BT 26.250 533.159 Td /F4 12.0 Tf [(Adaptive systems emulation of the discovery process)] TJ ET
BT 26.250 513.205 Td /F1 9.8 Tf [(Using a simulation methodology, a formal model of the discovery research process needs to be designed. Among garbage can )] TJ ET
BT 26.250 501.300 Td /F1 9.8 Tf [(models \(Cohen )] TJ ET
BT 95.066 501.300 Td /F5 9.8 Tf [(et al)] TJ ET
BT 113.493 501.300 Td /F1 9.8 Tf [(., 1972\) and physical symbol systems \(Newell & Simon, 1976; Masuch & Lapotin, 1989\) of organizational )] TJ ET
BT 26.250 489.396 Td /F1 9.8 Tf [(decision making, massively parallel connectionist \(Rumelhart )] TJ ET
BT 291.743 489.396 Td /F5 9.8 Tf [(et al)] TJ ET
BT 310.170 489.396 Td /F1 9.8 Tf [(., 1986\) and evolutionary computation models like adaptive )] TJ ET
BT 26.250 477.491 Td /F1 9.8 Tf [(systems \(Holland, 1992, 1998; Mitchell, 2001\), we argue that the latter adaptive system \(Holland, 1992\) is the best artificial )] TJ ET
BT 26.250 465.586 Td /F1 9.8 Tf [(reconstruction of the mental modeling process during pharmaceutical discovery research since it mimics best the “selection and )] TJ ET
BT 26.250 453.681 Td /F1 9.8 Tf [(optimization” search for an NME in a vast, discontinuous multi-factorial solution space.)] TJ ET
BT 26.250 434.277 Td /F1 9.8 Tf [(According to Mitchell \(2001\), this adaptive search method 1\) initially generates a set of candidate solutions; 2\) evaluates the )] TJ ET
BT 26.250 422.372 Td /F1 9.8 Tf [(candidate solutions according to some fitness criteria; 3\) decides on the basis of this evaluation which candidates will be kept )] TJ ET
BT 26.250 410.467 Td /F1 9.8 Tf [(and which will be discarded; and 4\) produces further variants by using some kind of operators on the surviving candidates.)] TJ ET
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BT 35.250 327.312 Td /F4 9.8 Tf [(Fig. 6: Adaptive systems model of the discovery experimentation process)] TJ ET
Q
BT 26.250 298.158 Td /F1 9.8 Tf [(Therefore, an annotated[7] adaptive systems model served as the theoretical paradigm underlying the simulation study, in which )] TJ ET
BT 26.250 286.253 Td /F1 9.8 Tf [(each experimentation strategy used in a pharmaceutical discovery context is modeled to execute an adaptive plan.)] TJ ET
BT 26.250 266.848 Td /F1 9.8 Tf [(Referring to Figure 6 and using Holland’s \(1992\) notation, this adaptive plan t starts from the universe of potential compounds )] TJ ET
BT 26.250 254.943 Td /F1 9.8 Tf [(representing the extant search space and initial uncertainty about the environment. Only an extremely small fraction of this )] TJ ET
BT 26.250 243.039 Td /F1 9.8 Tf [(universe is composed of candidate compounds with therapeutic effect. Through the execution of successive “noisy” selection )] TJ ET
BT 26.250 231.134 Td /F1 9.8 Tf [(and optimization loops, the total set of chemical structures attainable at moment )] TJ ET
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BT 375.788 231.134 Td /F1 9.8 Tf [(, )] TJ ET
BT 381.209 231.134 Td /F5 9.8 Tf [(?\(t\))] TJ ET
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BT 224.584 219.229 Td /F5 9.8 Tf [(t)] TJ ET
BT 227.295 219.229 Td /F1 9.8 Tf [(. This way, the adaptive plan improves the fit of the solution with the target.)] TJ ET
BT 26.250 199.824 Td /F1 9.8 Tf [(The screening and optimization process on a validated target starts in HTS at the level of the chemical classes to end at )] TJ ET
BT 26.250 187.920 Td /F1 9.8 Tf [(candidate compound level. The top-performing classes and compounds are selected using payoff information on two or more )] TJ ET
BT 26.250 176.015 Td /F1 9.8 Tf [(properties. I\()] TJ ET
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BT 86.934 176.015 Td /F1 9.8 Tf [( varies depending on the experimentation strategy used; or only biological activity information is used to find the )] TJ ET
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BT 26.250 140.301 Td /F1 9.8 Tf [(only deal with chemical class-related performance in HTS and H2L, and with chemical compound-related performance in LO. )] TJ ET
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BT 26.250 116.491 Td /F1 9.8 Tf [(the process. The chosen multi-objective function conservatively takes the minimum of both \(P\) and \(B\) detector values as the )] TJ ET
BT 26.250 104.586 Td /F1 9.8 Tf [(calculated performance value for the chemical class or candidate compound, depending on the discovery research phase. At all )] TJ ET
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BT 26.250 73.277 Td /F1 9.8 Tf [(The conceptual model specified above formed the basis to build a computer simulation program in Excel Visual Basic )] TJ ET
BT 26.250 61.372 Td /F1 9.8 Tf [(Applications \(VBA\). Theory building results using simulation-based experiments will be discussed in the following paragraphs.)] TJ ET
Q
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BT 26.250 713.126 Td /F4 12.0 Tf [(Simulation model behavior)] TJ ET
BT 26.250 693.172 Td /F1 9.8 Tf [(All experiments were executed within a Monte Carlo design. Two-way ANOVA screening experiments \()] TJ ET
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BT 35.250 566.713 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 556.713 Td /F1 8.0 Tf [(https://journal.emergentpublications.com/wp-content/uploads/2015/11/ac5a75c8-8b15-0ff2-0d79-d1b877d74bc7-300x151.png)] TJ ET
q
35.250 530.208 537.000 17.905 re W n
0.271 0.267 0.267 rg
BT 35.250 538.589 Td /F4 9.8 Tf [(Fig. 7: Predictive performance of experimentation strategies one-way ANOVA results \(n=200\))] TJ ET
Q
BT 26.250 509.434 Td /F1 9.8 Tf [(Then, selected simulation runs were repeated 200 times to obtain distributions of performance variables. One-way ANOVA was )] TJ ET
BT 26.250 497.529 Td /F1 9.8 Tf [(used on these performance data series to test for statistical significance of differences in predictive and business performance )] TJ ET
BT 26.250 485.625 Td /F1 9.8 Tf [(of experimentation strategies. Results of these Monte Carlo simulation experiments indicate that )] TJ ET
BT 442.965 485.625 Td /F5 9.8 Tf [(the level of front loading)] TJ ET
BT 546.481 485.625 Td /F1 9.8 Tf [( and )] TJ ET
BT 26.250 473.720 Td /F1 9.8 Tf [(the )] TJ ET
BT 42.513 473.720 Td /F5 9.8 Tf [(number of parallel solution concept explorations)] TJ ET
BT 248.979 473.720 Td /F1 9.8 Tf [( used significantly influence predictive and business performance.)] TJ ET
BT 26.250 454.315 Td /F1 9.8 Tf [(In the following, outputs of the simulation runs are discussed in greater detail.)] TJ ET
BT 26.250 417.713 Td /F4 12.0 Tf [(Influence of the level of front loading on predictive and business performance)] TJ ET
BT 26.250 397.758 Td /F1 9.8 Tf [(Simulation experiments probing for the influence of the level of front loading on predictive and business performance were )] TJ ET
BT 26.250 385.854 Td /F1 9.8 Tf [(conducted assuming a \(HTS, H2L\) = \(5, 2\) funnel shape and a surrogate marker chain tightness of 70%. Both conditions reflect )] TJ ET
BT 26.250 373.949 Td /F1 9.8 Tf [(respectively best current operational reality and scientific constraints.)] TJ ET
BT 26.250 354.544 Td /F1 9.8 Tf [(\()] TJ ET
BT 29.497 354.544 Td /F5 9.8 Tf [(1\) Predictive performance)] TJ ET
BT 141.115 354.544 Td /F1 9.8 Tf [(. Figure 7 indicates that positive predictive performance of old paradigm, front-loading, and early front )] TJ ET
BT 26.250 342.639 Td /F1 9.8 Tf [(loading differ significantly [F\(2, 596\)=13,79; )] TJ ET
BT 215.654 342.639 Td /F5 9.8 Tf [(p)] TJ ET
BT 221.075 342.639 Td /F1 9.8 Tf [(<.01], although the actual differences in mean scores are quite small. Also, this )] TJ ET
BT 26.250 330.735 Td /F1 9.8 Tf [(result needs to be moderated by the fact that the effect size, calculated using eta squared, was 0.044, indicating a small to )] TJ ET
BT 26.250 318.830 Td /F1 9.8 Tf [(medium impact of the independent variables on the outcome variable. Levene’s test showed non-homogeneity of variance )] TJ ET
BT 26.250 306.925 Td /F1 9.8 Tf [(across the three groups — which we consider to be normal considering the different experimentation strategy types — implying )] TJ ET
BT 26.250 295.020 Td /F1 9.8 Tf [(the need for studying differences between groups at the )] TJ ET
BT 269.600 295.020 Td /F5 9.8 Tf [(p)] TJ ET
BT 275.021 295.020 Td /F1 9.8 Tf [(<.01 level of significance. Both Tukey’s HSD and Scheffe’s post-hoc )] TJ ET
BT 26.250 283.116 Td /F1 9.8 Tf [(comparisons indicated that old paradigm \(M=0.974; SD=0.05\) is significantly outperformed by front-loaded paradigm \(M=0.992; )] TJ ET
BT 26.250 271.211 Td /F1 9.8 Tf [(SD=0.026\), and early front loading \(M=0.993; SD=0.036\) at the )] TJ ET
BT 301.298 271.211 Td /F5 9.8 Tf [(p)] TJ ET
BT 306.719 271.211 Td /F1 9.8 Tf [(<.01 level of significance for positive predictive performance. )] TJ ET
BT 26.250 259.306 Td /F1 9.8 Tf [(Front-loaded paradigm and early front loading did not differ significantly. Also, negative predictive performance did not differ )] TJ ET
BT 26.250 247.401 Td /F1 9.8 Tf [(significantly across discovery experimentation strategies.)] TJ ET
BT 26.250 227.997 Td /F1 9.8 Tf [(\()] TJ ET
BT 29.497 227.997 Td /F5 9.8 Tf [(2\) Business performance)] TJ ET
BT 137.868 227.997 Td /F1 9.8 Tf [(. Financial assumptions used to perform this analysis include the following: product revenue R was )] TJ ET
BT 26.250 216.092 Td /F1 9.8 Tf [(assumed to build up linearly to one billion dollars a year in a period of seven years, a conservative estimation of a typical )] TJ ET
BT 26.250 204.187 Td /F1 9.8 Tf [(industry average blockbuster product \(Duyck, 2003\). )] TJ ET
BT 254.927 204.187 Td /F5 9.8 Tf [(C)] TJ ET
BT 261.966 202.123 Td /F5 8.7 Tf [(D)] TJ ET
BT 268.223 204.187 Td /F1 9.8 Tf [( and )] TJ ET
BT 289.907 204.187 Td /F5 9.8 Tf [(C)] TJ ET
BT 296.947 202.123 Td /F5 8.7 Tf [(Clin)] TJ ET
BT 311.871 204.187 Td /F1 9.8 Tf [( amount to $20 and $400 million dollar respectively. These )] TJ ET
BT 26.250 192.282 Td /F1 9.8 Tf [(numbers represent typical project costs excluding the contribution for attrition, which is usually taken into account when )] TJ ET
BT 26.250 180.378 Td /F1 9.8 Tf [(specifying development costs \(Kennedy, 1997; Duyck, 2003\).)] TJ ET
BT 26.250 160.973 Td /F1 9.8 Tf [(There was a statistically significant difference at the )] TJ ET
BT 251.134 160.973 Td /F5 9.8 Tf [(p)] TJ ET
BT 256.555 160.973 Td /F1 9.8 Tf [(<.01 level for the different experimentation strategies. Referring to Figure )] TJ ET
BT 26.250 149.068 Td /F1 9.8 Tf [(8, old paradigm, front-loaded paradigm and early front loading differed significantly [F\(2, 596\)=12.9; )] TJ ET
BT 456.810 149.068 Td /F5 9.8 Tf [(p)] TJ ET
BT 462.231 149.068 Td /F1 9.8 Tf [(<.01] on business )] TJ ET
BT 26.250 137.163 Td /F1 9.8 Tf [(performance. This result needs to be moderated by the fact that the effect size, calculated using eta squared, was 0.041, )] TJ ET
BT 26.250 125.259 Td /F1 9.8 Tf [(indicating a small to medium impact of the independent variables on the outcome variable. Levene’s test showed non-)] TJ ET
BT 26.250 113.354 Td /F1 9.8 Tf [(homogeneity of variance across the three groups — which I consider to be normal considering the different experimentation )] TJ ET
BT 26.250 101.449 Td /F1 9.8 Tf [(strategy types — implying the need for studying differences between groups at the p<.01 level of significance.)] TJ ET
0.965 0.965 0.965 rg
26.250 27.818 555.000 63.750 re f
0.267 0.267 0.267 rg
0.267 0.267 0.267 RG
26.250 91.568 m 581.250 91.568 l 581.250 90.818 l 26.250 90.818 l f
0.271 0.267 0.267 rg
BT 35.250 72.799 Td /F1 9.8 Tf [(f941d249-ff07-ac30-bd06-cdf400e8874e)] TJ ET
0.500 0.500 0.500 rg
BT 35.250 46.418 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 36.418 Td /F1 8.0 Tf [(https://journal.emergentpublications.com/wp-content/uploads/2015/11/f941d249-ff07-ac30-bd06-cdf400e8874e-300x303.png)] TJ ET
q
35.250 27.818 537.000 0.000 re W n
Q
Q
q
15.000 27.818 577.500 749.182 re W n
0.271 0.267 0.267 rg
BT 26.250 750.278 Td /F4 12.0 Tf [(Monte Carlo simulation results)] TJ ET
BT 26.250 713.126 Td /F4 12.0 Tf [(Simulation model behavior)] TJ ET
BT 26.250 693.172 Td /F1 9.8 Tf [(All experiments were executed within a Monte Carlo design. Two-way ANOVA screening experiments \()] TJ ET
BT 470.021 693.172 Td /F5 9.8 Tf [(n)] TJ ET
BT 475.442 693.172 Td /F1 9.8 Tf [(=100\) showed that the )] TJ ET
BT 26.250 681.267 Td /F1 9.8 Tf [(simulation method used only provides meaningful results within an operating window determined by 1\) the ruggedness of the )] TJ ET
BT 26.250 669.363 Td /F1 9.8 Tf [(solution landscape; and 2\) the number of compounds selected in LO; \()] TJ ET
BT 329.738 669.363 Td /F5 9.8 Tf [(p\))] TJ ET
BT 338.406 669.363 Td /F1 9.8 Tf [(. As to ruggedness, to get meaningful results standard )] TJ ET
BT 26.250 657.458 Td /F1 9.8 Tf [(deviations were not allowed to move beyond SD=1. Also, when \()] TJ ET
BT 304.525 657.458 Td /F5 9.8 Tf [(p\))] TJ ET
BT 313.192 657.458 Td /F1 9.8 Tf [( was allowed to move below a certain threshold, results again )] TJ ET
BT 26.250 645.553 Td /F1 9.8 Tf [(became meaningless. Therefore, simulation experiments were conducted at )] TJ ET
BT 356.258 645.553 Td /F5 9.8 Tf [(p)] TJ ET
BT 361.679 645.553 Td /F1 9.8 Tf [(=15 out of 24,000 initial virtual compounds )] TJ ET
BT 26.250 633.648 Td /F1 9.8 Tf [(leading to a )] TJ ET
BT 79.368 633.648 Td /F5 9.8 Tf [(p\(H)] TJ ET
BT 95.075 637.537 Td /F5 8.7 Tf [(+)] TJ ET
BT 100.137 633.648 Td /F5 9.8 Tf [(\))] TJ ET
BT 103.383 633.648 Td /F1 9.8 Tf [( = 0.000625. This was the lowest possible value, which fits an industry average of about five compounds )] TJ ET
BT 26.250 621.744 Td /F1 9.8 Tf [(being advanced to clinical development out of 10,000 synthesized compounds \(Furness, 2003\).)] TJ ET
0.965 0.965 0.965 rg
26.250 526.458 555.000 85.405 re f
0.267 0.267 0.267 rg
0.267 0.267 0.267 RG
26.250 611.863 m 581.250 611.863 l 581.250 611.113 l 26.250 611.113 l f
26.250 526.458 m 581.250 526.458 l 581.250 527.208 l 26.250 527.208 l f
0.271 0.267 0.267 rg
BT 35.250 593.094 Td /F1 9.8 Tf [(ac5a75c8-8b15-0ff2-0d79-d1b877d74bc7)] TJ ET
0.500 0.500 0.500 rg
BT 35.250 566.713 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 556.713 Td /F1 8.0 Tf [(https://journal.emergentpublications.com/wp-content/uploads/2015/11/ac5a75c8-8b15-0ff2-0d79-d1b877d74bc7-300x151.png)] TJ ET
q
35.250 530.208 537.000 17.905 re W n
0.271 0.267 0.267 rg
BT 35.250 538.589 Td /F4 9.8 Tf [(Fig. 7: Predictive performance of experimentation strategies one-way ANOVA results \(n=200\))] TJ ET
Q
BT 26.250 509.434 Td /F1 9.8 Tf [(Then, selected simulation runs were repeated 200 times to obtain distributions of performance variables. One-way ANOVA was )] TJ ET
BT 26.250 497.529 Td /F1 9.8 Tf [(used on these performance data series to test for statistical significance of differences in predictive and business performance )] TJ ET
BT 26.250 485.625 Td /F1 9.8 Tf [(of experimentation strategies. Results of these Monte Carlo simulation experiments indicate that )] TJ ET
BT 442.965 485.625 Td /F5 9.8 Tf [(the level of front loading)] TJ ET
BT 546.481 485.625 Td /F1 9.8 Tf [( and )] TJ ET
BT 26.250 473.720 Td /F1 9.8 Tf [(the )] TJ ET
BT 42.513 473.720 Td /F5 9.8 Tf [(number of parallel solution concept explorations)] TJ ET
BT 248.979 473.720 Td /F1 9.8 Tf [( used significantly influence predictive and business performance.)] TJ ET
BT 26.250 454.315 Td /F1 9.8 Tf [(In the following, outputs of the simulation runs are discussed in greater detail.)] TJ ET
BT 26.250 417.713 Td /F4 12.0 Tf [(Influence of the level of front loading on predictive and business performance)] TJ ET
BT 26.250 397.758 Td /F1 9.8 Tf [(Simulation experiments probing for the influence of the level of front loading on predictive and business performance were )] TJ ET
BT 26.250 385.854 Td /F1 9.8 Tf [(conducted assuming a \(HTS, H2L\) = \(5, 2\) funnel shape and a surrogate marker chain tightness of 70%. Both conditions reflect )] TJ ET
BT 26.250 373.949 Td /F1 9.8 Tf [(respectively best current operational reality and scientific constraints.)] TJ ET
BT 26.250 354.544 Td /F1 9.8 Tf [(\()] TJ ET
BT 29.497 354.544 Td /F5 9.8 Tf [(1\) Predictive performance)] TJ ET
BT 141.115 354.544 Td /F1 9.8 Tf [(. Figure 7 indicates that positive predictive performance of old paradigm, front-loading, and early front )] TJ ET
BT 26.250 342.639 Td /F1 9.8 Tf [(loading differ significantly [F\(2, 596\)=13,79; )] TJ ET
BT 215.654 342.639 Td /F5 9.8 Tf [(p)] TJ ET
BT 221.075 342.639 Td /F1 9.8 Tf [(<.01], although the actual differences in mean scores are quite small. Also, this )] TJ ET
BT 26.250 330.735 Td /F1 9.8 Tf [(result needs to be moderated by the fact that the effect size, calculated using eta squared, was 0.044, indicating a small to )] TJ ET
BT 26.250 318.830 Td /F1 9.8 Tf [(medium impact of the independent variables on the outcome variable. Levene’s test showed non-homogeneity of variance )] TJ ET
BT 26.250 306.925 Td /F1 9.8 Tf [(across the three groups — which we consider to be normal considering the different experimentation strategy types — implying )] TJ ET
BT 26.250 295.020 Td /F1 9.8 Tf [(the need for studying differences between groups at the )] TJ ET
BT 269.600 295.020 Td /F5 9.8 Tf [(p)] TJ ET
BT 275.021 295.020 Td /F1 9.8 Tf [(<.01 level of significance. Both Tukey’s HSD and Scheffe’s post-hoc )] TJ ET
BT 26.250 283.116 Td /F1 9.8 Tf [(comparisons indicated that old paradigm \(M=0.974; SD=0.05\) is significantly outperformed by front-loaded paradigm \(M=0.992; )] TJ ET
BT 26.250 271.211 Td /F1 9.8 Tf [(SD=0.026\), and early front loading \(M=0.993; SD=0.036\) at the )] TJ ET
BT 301.298 271.211 Td /F5 9.8 Tf [(p)] TJ ET
BT 306.719 271.211 Td /F1 9.8 Tf [(<.01 level of significance for positive predictive performance. )] TJ ET
BT 26.250 259.306 Td /F1 9.8 Tf [(Front-loaded paradigm and early front loading did not differ significantly. Also, negative predictive performance did not differ )] TJ ET
BT 26.250 247.401 Td /F1 9.8 Tf [(significantly across discovery experimentation strategies.)] TJ ET
BT 26.250 227.997 Td /F1 9.8 Tf [(\()] TJ ET
BT 29.497 227.997 Td /F5 9.8 Tf [(2\) Business performance)] TJ ET
BT 137.868 227.997 Td /F1 9.8 Tf [(. Financial assumptions used to perform this analysis include the following: product revenue R was )] TJ ET
BT 26.250 216.092 Td /F1 9.8 Tf [(assumed to build up linearly to one billion dollars a year in a period of seven years, a conservative estimation of a typical )] TJ ET
BT 26.250 204.187 Td /F1 9.8 Tf [(industry average blockbuster product \(Duyck, 2003\). )] TJ ET
BT 254.927 204.187 Td /F5 9.8 Tf [(C)] TJ ET
BT 261.966 202.123 Td /F5 8.7 Tf [(D)] TJ ET
BT 268.223 204.187 Td /F1 9.8 Tf [( and )] TJ ET
BT 289.907 204.187 Td /F5 9.8 Tf [(C)] TJ ET
BT 296.947 202.123 Td /F5 8.7 Tf [(Clin)] TJ ET
BT 311.871 204.187 Td /F1 9.8 Tf [( amount to $20 and $400 million dollar respectively. These )] TJ ET
BT 26.250 192.282 Td /F1 9.8 Tf [(numbers represent typical project costs excluding the contribution for attrition, which is usually taken into account when )] TJ ET
BT 26.250 180.378 Td /F1 9.8 Tf [(specifying development costs \(Kennedy, 1997; Duyck, 2003\).)] TJ ET
BT 26.250 160.973 Td /F1 9.8 Tf [(There was a statistically significant difference at the )] TJ ET
BT 251.134 160.973 Td /F5 9.8 Tf [(p)] TJ ET
BT 256.555 160.973 Td /F1 9.8 Tf [(<.01 level for the different experimentation strategies. Referring to Figure )] TJ ET
BT 26.250 149.068 Td /F1 9.8 Tf [(8, old paradigm, front-loaded paradigm and early front loading differed significantly [F\(2, 596\)=12.9; )] TJ ET
BT 456.810 149.068 Td /F5 9.8 Tf [(p)] TJ ET
BT 462.231 149.068 Td /F1 9.8 Tf [(<.01] on business )] TJ ET
BT 26.250 137.163 Td /F1 9.8 Tf [(performance. This result needs to be moderated by the fact that the effect size, calculated using eta squared, was 0.041, )] TJ ET
BT 26.250 125.259 Td /F1 9.8 Tf [(indicating a small to medium impact of the independent variables on the outcome variable. Levene’s test showed non-)] TJ ET
BT 26.250 113.354 Td /F1 9.8 Tf [(homogeneity of variance across the three groups — which I consider to be normal considering the different experimentation )] TJ ET
BT 26.250 101.449 Td /F1 9.8 Tf [(strategy types — implying the need for studying differences between groups at the p<.01 level of significance.)] TJ ET
0.965 0.965 0.965 rg
26.250 27.818 555.000 63.750 re f
0.267 0.267 0.267 rg
0.267 0.267 0.267 RG
26.250 91.568 m 581.250 91.568 l 581.250 90.818 l 26.250 90.818 l f
0.271 0.267 0.267 rg
BT 35.250 72.799 Td /F1 9.8 Tf [(f941d249-ff07-ac30-bd06-cdf400e8874e)] TJ ET
0.500 0.500 0.500 rg
BT 35.250 46.418 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 36.418 Td /F1 8.0 Tf [(https://journal.emergentpublications.com/wp-content/uploads/2015/11/f941d249-ff07-ac30-bd06-cdf400e8874e-300x303.png)] TJ ET
q
35.250 27.818 537.000 0.000 re W n
Q
Q
q
15.000 27.818 577.500 749.182 re W n
0.271 0.267 0.267 rg
BT 26.250 750.278 Td /F4 12.0 Tf [(Monte Carlo simulation results)] TJ ET
BT 26.250 713.126 Td /F4 12.0 Tf [(Simulation model behavior)] TJ ET
BT 26.250 693.172 Td /F1 9.8 Tf [(All experiments were executed within a Monte Carlo design. Two-way ANOVA screening experiments \()] TJ ET
BT 470.021 693.172 Td /F5 9.8 Tf [(n)] TJ ET
BT 475.442 693.172 Td /F1 9.8 Tf [(=100\) showed that the )] TJ ET
BT 26.250 681.267 Td /F1 9.8 Tf [(simulation method used only provides meaningful results within an operating window determined by 1\) the ruggedness of the )] TJ ET
BT 26.250 669.363 Td /F1 9.8 Tf [(solution landscape; and 2\) the number of compounds selected in LO; \()] TJ ET
BT 329.738 669.363 Td /F5 9.8 Tf [(p\))] TJ ET
BT 338.406 669.363 Td /F1 9.8 Tf [(. As to ruggedness, to get meaningful results standard )] TJ ET
BT 26.250 657.458 Td /F1 9.8 Tf [(deviations were not allowed to move beyond SD=1. Also, when \()] TJ ET
BT 304.525 657.458 Td /F5 9.8 Tf [(p\))] TJ ET
BT 313.192 657.458 Td /F1 9.8 Tf [( was allowed to move below a certain threshold, results again )] TJ ET
BT 26.250 645.553 Td /F1 9.8 Tf [(became meaningless. Therefore, simulation experiments were conducted at )] TJ ET
BT 356.258 645.553 Td /F5 9.8 Tf [(p)] TJ ET
BT 361.679 645.553 Td /F1 9.8 Tf [(=15 out of 24,000 initial virtual compounds )] TJ ET
BT 26.250 633.648 Td /F1 9.8 Tf [(leading to a )] TJ ET
BT 79.368 633.648 Td /F5 9.8 Tf [(p\(H)] TJ ET
BT 95.075 637.537 Td /F5 8.7 Tf [(+)] TJ ET
BT 100.137 633.648 Td /F5 9.8 Tf [(\))] TJ ET
BT 103.383 633.648 Td /F1 9.8 Tf [( = 0.000625. This was the lowest possible value, which fits an industry average of about five compounds )] TJ ET
BT 26.250 621.744 Td /F1 9.8 Tf [(being advanced to clinical development out of 10,000 synthesized compounds \(Furness, 2003\).)] TJ ET
0.965 0.965 0.965 rg
26.250 526.458 555.000 85.405 re f
0.267 0.267 0.267 rg
0.267 0.267 0.267 RG
26.250 611.863 m 581.250 611.863 l 581.250 611.113 l 26.250 611.113 l f
26.250 526.458 m 581.250 526.458 l 581.250 527.208 l 26.250 527.208 l f
0.271 0.267 0.267 rg
BT 35.250 593.094 Td /F1 9.8 Tf [(ac5a75c8-8b15-0ff2-0d79-d1b877d74bc7)] TJ ET
0.500 0.500 0.500 rg
BT 35.250 566.713 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 556.713 Td /F1 8.0 Tf [(https://journal.emergentpublications.com/wp-content/uploads/2015/11/ac5a75c8-8b15-0ff2-0d79-d1b877d74bc7-300x151.png)] TJ ET
q
35.250 530.208 537.000 17.905 re W n
0.271 0.267 0.267 rg
BT 35.250 538.589 Td /F4 9.8 Tf [(Fig. 7: Predictive performance of experimentation strategies one-way ANOVA results \(n=200\))] TJ ET
Q
BT 26.250 509.434 Td /F1 9.8 Tf [(Then, selected simulation runs were repeated 200 times to obtain distributions of performance variables. One-way ANOVA was )] TJ ET
BT 26.250 497.529 Td /F1 9.8 Tf [(used on these performance data series to test for statistical significance of differences in predictive and business performance )] TJ ET
BT 26.250 485.625 Td /F1 9.8 Tf [(of experimentation strategies. Results of these Monte Carlo simulation experiments indicate that )] TJ ET
BT 442.965 485.625 Td /F5 9.8 Tf [(the level of front loading)] TJ ET
BT 546.481 485.625 Td /F1 9.8 Tf [( and )] TJ ET
BT 26.250 473.720 Td /F1 9.8 Tf [(the )] TJ ET
BT 42.513 473.720 Td /F5 9.8 Tf [(number of parallel solution concept explorations)] TJ ET
BT 248.979 473.720 Td /F1 9.8 Tf [( used significantly influence predictive and business performance.)] TJ ET
BT 26.250 454.315 Td /F1 9.8 Tf [(In the following, outputs of the simulation runs are discussed in greater detail.)] TJ ET
BT 26.250 417.713 Td /F4 12.0 Tf [(Influence of the level of front loading on predictive and business performance)] TJ ET
BT 26.250 397.758 Td /F1 9.8 Tf [(Simulation experiments probing for the influence of the level of front loading on predictive and business performance were )] TJ ET
BT 26.250 385.854 Td /F1 9.8 Tf [(conducted assuming a \(HTS, H2L\) = \(5, 2\) funnel shape and a surrogate marker chain tightness of 70%. Both conditions reflect )] TJ ET
BT 26.250 373.949 Td /F1 9.8 Tf [(respectively best current operational reality and scientific constraints.)] TJ ET
BT 26.250 354.544 Td /F1 9.8 Tf [(\()] TJ ET
BT 29.497 354.544 Td /F5 9.8 Tf [(1\) Predictive performance)] TJ ET
BT 141.115 354.544 Td /F1 9.8 Tf [(. Figure 7 indicates that positive predictive performance of old paradigm, front-loading, and early front )] TJ ET
BT 26.250 342.639 Td /F1 9.8 Tf [(loading differ significantly [F\(2, 596\)=13,79; )] TJ ET
BT 215.654 342.639 Td /F5 9.8 Tf [(p)] TJ ET
BT 221.075 342.639 Td /F1 9.8 Tf [(<.01], although the actual differences in mean scores are quite small. Also, this )] TJ ET
BT 26.250 330.735 Td /F1 9.8 Tf [(result needs to be moderated by the fact that the effect size, calculated using eta squared, was 0.044, indicating a small to )] TJ ET
BT 26.250 318.830 Td /F1 9.8 Tf [(medium impact of the independent variables on the outcome variable. Levene’s test showed non-homogeneity of variance )] TJ ET
BT 26.250 306.925 Td /F1 9.8 Tf [(across the three groups — which we consider to be normal considering the different experimentation strategy types — implying )] TJ ET
BT 26.250 295.020 Td /F1 9.8 Tf [(the need for studying differences between groups at the )] TJ ET
BT 269.600 295.020 Td /F5 9.8 Tf [(p)] TJ ET
BT 275.021 295.020 Td /F1 9.8 Tf [(<.01 level of significance. Both Tukey’s HSD and Scheffe’s post-hoc )] TJ ET
BT 26.250 283.116 Td /F1 9.8 Tf [(comparisons indicated that old paradigm \(M=0.974; SD=0.05\) is significantly outperformed by front-loaded paradigm \(M=0.992; )] TJ ET
BT 26.250 271.211 Td /F1 9.8 Tf [(SD=0.026\), and early front loading \(M=0.993; SD=0.036\) at the )] TJ ET
BT 301.298 271.211 Td /F5 9.8 Tf [(p)] TJ ET
BT 306.719 271.211 Td /F1 9.8 Tf [(<.01 level of significance for positive predictive performance. )] TJ ET
BT 26.250 259.306 Td /F1 9.8 Tf [(Front-loaded paradigm and early front loading did not differ significantly. Also, negative predictive performance did not differ )] TJ ET
BT 26.250 247.401 Td /F1 9.8 Tf [(significantly across discovery experimentation strategies.)] TJ ET
BT 26.250 227.997 Td /F1 9.8 Tf [(\()] TJ ET
BT 29.497 227.997 Td /F5 9.8 Tf [(2\) Business performance)] TJ ET
BT 137.868 227.997 Td /F1 9.8 Tf [(. Financial assumptions used to perform this analysis include the following: product revenue R was )] TJ ET
BT 26.250 216.092 Td /F1 9.8 Tf [(assumed to build up linearly to one billion dollars a year in a period of seven years, a conservative estimation of a typical )] TJ ET
BT 26.250 204.187 Td /F1 9.8 Tf [(industry average blockbuster product \(Duyck, 2003\). )] TJ ET
BT 254.927 204.187 Td /F5 9.8 Tf [(C)] TJ ET
BT 261.966 202.123 Td /F5 8.7 Tf [(D)] TJ ET
BT 268.223 204.187 Td /F1 9.8 Tf [( and )] TJ ET
BT 289.907 204.187 Td /F5 9.8 Tf [(C)] TJ ET
BT 296.947 202.123 Td /F5 8.7 Tf [(Clin)] TJ ET
BT 311.871 204.187 Td /F1 9.8 Tf [( amount to $20 and $400 million dollar respectively. These )] TJ ET
BT 26.250 192.282 Td /F1 9.8 Tf [(numbers represent typical project costs excluding the contribution for attrition, which is usually taken into account when )] TJ ET
BT 26.250 180.378 Td /F1 9.8 Tf [(specifying development costs \(Kennedy, 1997; Duyck, 2003\).)] TJ ET
BT 26.250 160.973 Td /F1 9.8 Tf [(There was a statistically significant difference at the )] TJ ET
BT 251.134 160.973 Td /F5 9.8 Tf [(p)] TJ ET
BT 256.555 160.973 Td /F1 9.8 Tf [(<.01 level for the different experimentation strategies. Referring to Figure )] TJ ET
BT 26.250 149.068 Td /F1 9.8 Tf [(8, old paradigm, front-loaded paradigm and early front loading differed significantly [F\(2, 596\)=12.9; )] TJ ET
BT 456.810 149.068 Td /F5 9.8 Tf [(p)] TJ ET
BT 462.231 149.068 Td /F1 9.8 Tf [(<.01] on business )] TJ ET
BT 26.250 137.163 Td /F1 9.8 Tf [(performance. This result needs to be moderated by the fact that the effect size, calculated using eta squared, was 0.041, )] TJ ET
BT 26.250 125.259 Td /F1 9.8 Tf [(indicating a small to medium impact of the independent variables on the outcome variable. Levene’s test showed non-)] TJ ET
BT 26.250 113.354 Td /F1 9.8 Tf [(homogeneity of variance across the three groups — which I consider to be normal considering the different experimentation )] TJ ET
BT 26.250 101.449 Td /F1 9.8 Tf [(strategy types — implying the need for studying differences between groups at the p<.01 level of significance.)] TJ ET
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BT 25.000 19.825 Td /F1 11.0 Tf [(Emergence: Complexity and Organization)] TJ ET
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BT 35.250 767.476 Td /F4 9.8 Tf [(Fig. 8: Business performance of experimentation strategies one-way ANOVA results \(n=200\))] TJ ET
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BT 26.250 738.321 Td /F1 9.8 Tf [(Both Tukey’s HSD and Scheffe’s post-hoc comparisons indicated that old paradigm \(M=2241.9; SD=137.8\) is significantly )] TJ ET
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BT 509.119 726.417 Td /F1 9.8 Tf [(<.01 level of )] TJ ET
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BT 26.250 702.607 Td /F1 9.8 Tf [(does amount to $2.7 million in favor of early front loading using the above-mentioned assumptions.)] TJ ET
BT 26.250 666.005 Td /F4 12.0 Tf [(Influence of discovery exploration funnel on predictive and business performance)] TJ ET
BT 26.250 646.050 Td /F1 9.8 Tf [(Simulation experiments probing for the influence of varying shapes of the discovery exploration funnel on predictive and )] TJ ET
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BT 26.250 602.836 Td /F1 9.8 Tf [(\()] TJ ET
BT 29.497 602.836 Td /F5 9.8 Tf [(1\) Predictive performance)] TJ ET
BT 141.115 602.836 Td /F1 9.8 Tf [(. Referring to Figure 9, positive [F\(3, 769\)=23,84; )] TJ ET
BT 354.903 602.836 Td /F5 9.8 Tf [(p)] TJ ET
BT 360.324 602.836 Td /F1 9.8 Tf [(<.01] and negative [F\(3, 796\)=21.93; p<.01] )] TJ ET
BT 26.250 590.931 Td /F1 9.8 Tf [(predictive performance for the various front-loaded funnel shaping strategies differed significantly, although the actual )] TJ ET
BT 26.250 579.027 Td /F1 9.8 Tf [(differences in mean scores are quite small. Effect size, calculated using eta squared, was 0.084 and 0.077 for positive and )] TJ ET
BT 26.250 567.122 Td /F1 9.8 Tf [(negative predictive performance respectively, indicating a medium impact of the independent variables on the outcome )] TJ ET
BT 26.250 555.217 Td /F1 9.8 Tf [(variables. Levene’s test showed non-homogeneity of variance across the three groups — which I consider to be normal )] TJ ET
BT 26.250 543.312 Td /F1 9.8 Tf [(considering the different experimentation strategy types — implying the need for studying differences between groups at the )] TJ ET
BT 562.763 543.312 Td /F5 9.8 Tf [(p)] TJ ET
BT 26.250 531.408 Td /F1 9.8 Tf [(<.01 level of significance.)] TJ ET
BT 26.250 512.003 Td /F1 9.8 Tf [(Both Tukey’s HSD and Scheffe’s post-hoc comparisons for positive predictive performance indicated that a \(1; 1\) and a \(5; 1\) )] TJ ET
BT 26.250 500.098 Td /F1 9.8 Tf [(funnel shaping strategy both differ significantly from a \(5; 2\) and \(5; 3\) strategy at the )] TJ ET
BT 393.679 500.098 Td /F5 9.8 Tf [(p)] TJ ET
BT 399.100 500.098 Td /F1 9.8 Tf [(<.01 level of significance. A \(5; 2\) and a )] TJ ET
BT 26.250 488.193 Td /F1 9.8 Tf [(\(5; 3\) strategy do not differ significantly from each other.)] TJ ET
BT 26.250 468.789 Td /F1 9.8 Tf [(Both Tukey’s HSD and Scheffe’s post-hoc comparisons for negative predictive performance indicated that a \(1; 1\) and a \(5; 1\) )] TJ ET
BT 26.250 456.884 Td /F1 9.8 Tf [(funnel shaping strategy both differ significantly from a \(5; 2\) strategy and \(5; 3\) strategy at the )] TJ ET
BT 431.070 456.884 Td /F5 9.8 Tf [(p)] TJ ET
BT 436.491 456.884 Td /F1 9.8 Tf [(<.01 level of significance. Also, )] TJ ET
BT 26.250 444.979 Td /F1 9.8 Tf [(\(5; 2\) and \(5; 3\) strategies differ significantly from each other at the )] TJ ET
BT 316.712 444.979 Td /F5 9.8 Tf [(p)] TJ ET
BT 322.133 444.979 Td /F1 9.8 Tf [(<.01 level of significance.)] TJ ET
BT 26.250 425.574 Td /F1 9.8 Tf [(\()] TJ ET
BT 29.497 425.574 Td /F5 9.8 Tf [(2\) Business performance)] TJ ET
BT 137.868 425.574 Td /F1 9.8 Tf [(. A one-way between-group analysis of variance was conducted to probe for the impact of various )] TJ ET
BT 26.250 413.670 Td /F1 9.8 Tf [(front-loaded funnel shapes on the business performance of the experimentation strategy used. There was a statistically )] TJ ET
BT 26.250 401.765 Td /F1 9.8 Tf [(significant difference at the p<.01 level for the different funnel shaping strategies. Financial assumptions used to perform )] TJ ET
BT 26.250 389.860 Td /F1 9.8 Tf [(ANOVA were discussed above.)] TJ ET
BT 26.250 370.455 Td /F1 9.8 Tf [(Referring to Figure 10, business performance [F\(3, 769\)=7.9; )] TJ ET
BT 292.055 370.455 Td /F5 9.8 Tf [(p)] TJ ET
BT 297.476 370.455 Td /F1 9.8 Tf [(<.01] for the various front-loaded funnel shaping strategies )] TJ ET
BT 26.250 358.551 Td /F1 9.8 Tf [(differed significantly. This result needs to be moderated by the fact that the effect size, calculated using eta squared, was 0.031, )] TJ ET
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BT 26.250 334.741 Td /F1 9.8 Tf [(of variance across the three groups. Still, differences between groups were studied at the )] TJ ET
BT 413.188 334.741 Td /F5 9.8 Tf [(p)] TJ ET
BT 418.609 334.741 Td /F1 9.8 Tf [(<.01 level of significance.)] TJ ET
BT 26.250 315.336 Td /F1 9.8 Tf [(Both Tukey’s HSD and Scheffe’s post-hoc comparisons for business performance indicated that a \(5; 1\) funnel shaping strategy )] TJ ET
BT 26.250 303.432 Td /F1 9.8 Tf [(\(M=2157.5, SD=75.6\) outperforms a \(1; 1\) strategy \(M=2134.8, SD=99.1\) by $22M and both a \(5; 2\) strategy \(M=2120.9, )] TJ ET
BT 26.250 291.527 Td /F1 9.8 Tf [(SD=91.5\) and \(5; 3\) strategy \(M=2121.3, SD=79.3\) by $36M, at the p<.01 level of significance. A \(1; 1\) strategy does not differ )] TJ ET
BT 26.250 279.622 Td /F1 9.8 Tf [(significantly from the others and a \(5; 2\) and \(5; 3\) strategy do not differ significantly from each other.)] TJ ET
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BT 35.250 767.476 Td /F4 9.8 Tf [(Fig. 8: Business performance of experimentation strategies one-way ANOVA results \(n=200\))] TJ ET
Q
BT 26.250 738.321 Td /F1 9.8 Tf [(Both Tukey’s HSD and Scheffe’s post-hoc comparisons indicated that old paradigm \(M=2241.9; SD=137.8\) is significantly )] TJ ET
BT 26.250 726.417 Td /F1 9.8 Tf [(outperformed by front-loaded paradigm \(M=2287.3; SD=78.2\), and early front loading \(M=2290; SD=94\) at the )] TJ ET
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BT 509.119 726.417 Td /F1 9.8 Tf [(<.01 level of )] TJ ET
BT 26.250 714.512 Td /F1 9.8 Tf [(significance for business performance. Front-loading and early front loading did not differ significantly, although the difference )] TJ ET
BT 26.250 702.607 Td /F1 9.8 Tf [(does amount to $2.7 million in favor of early front loading using the above-mentioned assumptions.)] TJ ET
BT 26.250 666.005 Td /F4 12.0 Tf [(Influence of discovery exploration funnel on predictive and business performance)] TJ ET
BT 26.250 646.050 Td /F1 9.8 Tf [(Simulation experiments probing for the influence of varying shapes of the discovery exploration funnel on predictive and )] TJ ET
BT 26.250 634.146 Td /F1 9.8 Tf [(business performance of early front-loaded experimentation strategies were conducted assuming a surrogate marker chain )] TJ ET
BT 26.250 622.241 Td /F1 9.8 Tf [(tightness of 70%, which reflects best current scientific constraints.)] TJ ET
BT 26.250 602.836 Td /F1 9.8 Tf [(\()] TJ ET
BT 29.497 602.836 Td /F5 9.8 Tf [(1\) Predictive performance)] TJ ET
BT 141.115 602.836 Td /F1 9.8 Tf [(. Referring to Figure 9, positive [F\(3, 769\)=23,84; )] TJ ET
BT 354.903 602.836 Td /F5 9.8 Tf [(p)] TJ ET
BT 360.324 602.836 Td /F1 9.8 Tf [(<.01] and negative [F\(3, 796\)=21.93; p<.01] )] TJ ET
BT 26.250 590.931 Td /F1 9.8 Tf [(predictive performance for the various front-loaded funnel shaping strategies differed significantly, although the actual )] TJ ET
BT 26.250 579.027 Td /F1 9.8 Tf [(differences in mean scores are quite small. Effect size, calculated using eta squared, was 0.084 and 0.077 for positive and )] TJ ET
BT 26.250 567.122 Td /F1 9.8 Tf [(negative predictive performance respectively, indicating a medium impact of the independent variables on the outcome )] TJ ET
BT 26.250 555.217 Td /F1 9.8 Tf [(variables. Levene’s test showed non-homogeneity of variance across the three groups — which I consider to be normal )] TJ ET
BT 26.250 543.312 Td /F1 9.8 Tf [(considering the different experimentation strategy types — implying the need for studying differences between groups at the )] TJ ET
BT 562.763 543.312 Td /F5 9.8 Tf [(p)] TJ ET
BT 26.250 531.408 Td /F1 9.8 Tf [(<.01 level of significance.)] TJ ET
BT 26.250 512.003 Td /F1 9.8 Tf [(Both Tukey’s HSD and Scheffe’s post-hoc comparisons for positive predictive performance indicated that a \(1; 1\) and a \(5; 1\) )] TJ ET
BT 26.250 500.098 Td /F1 9.8 Tf [(funnel shaping strategy both differ significantly from a \(5; 2\) and \(5; 3\) strategy at the )] TJ ET
BT 393.679 500.098 Td /F5 9.8 Tf [(p)] TJ ET
BT 399.100 500.098 Td /F1 9.8 Tf [(<.01 level of significance. A \(5; 2\) and a )] TJ ET
BT 26.250 488.193 Td /F1 9.8 Tf [(\(5; 3\) strategy do not differ significantly from each other.)] TJ ET
BT 26.250 468.789 Td /F1 9.8 Tf [(Both Tukey’s HSD and Scheffe’s post-hoc comparisons for negative predictive performance indicated that a \(1; 1\) and a \(5; 1\) )] TJ ET
BT 26.250 456.884 Td /F1 9.8 Tf [(funnel shaping strategy both differ significantly from a \(5; 2\) strategy and \(5; 3\) strategy at the )] TJ ET
BT 431.070 456.884 Td /F5 9.8 Tf [(p)] TJ ET
BT 436.491 456.884 Td /F1 9.8 Tf [(<.01 level of significance. Also, )] TJ ET
BT 26.250 444.979 Td /F1 9.8 Tf [(\(5; 2\) and \(5; 3\) strategies differ significantly from each other at the )] TJ ET
BT 316.712 444.979 Td /F5 9.8 Tf [(p)] TJ ET
BT 322.133 444.979 Td /F1 9.8 Tf [(<.01 level of significance.)] TJ ET
BT 26.250 425.574 Td /F1 9.8 Tf [(\()] TJ ET
BT 29.497 425.574 Td /F5 9.8 Tf [(2\) Business performance)] TJ ET
BT 137.868 425.574 Td /F1 9.8 Tf [(. A one-way between-group analysis of variance was conducted to probe for the impact of various )] TJ ET
BT 26.250 413.670 Td /F1 9.8 Tf [(front-loaded funnel shapes on the business performance of the experimentation strategy used. There was a statistically )] TJ ET
BT 26.250 401.765 Td /F1 9.8 Tf [(significant difference at the p<.01 level for the different funnel shaping strategies. Financial assumptions used to perform )] TJ ET
BT 26.250 389.860 Td /F1 9.8 Tf [(ANOVA were discussed above.)] TJ ET
BT 26.250 370.455 Td /F1 9.8 Tf [(Referring to Figure 10, business performance [F\(3, 769\)=7.9; )] TJ ET
BT 292.055 370.455 Td /F5 9.8 Tf [(p)] TJ ET
BT 297.476 370.455 Td /F1 9.8 Tf [(<.01] for the various front-loaded funnel shaping strategies )] TJ ET
BT 26.250 358.551 Td /F1 9.8 Tf [(differed significantly. This result needs to be moderated by the fact that the effect size, calculated using eta squared, was 0.031, )] TJ ET
BT 26.250 346.646 Td /F1 9.8 Tf [(indicating a small to medium impact of the independent variables on the outcome variable. Levene’s test showed homogeneity )] TJ ET
BT 26.250 334.741 Td /F1 9.8 Tf [(of variance across the three groups. Still, differences between groups were studied at the )] TJ ET
BT 413.188 334.741 Td /F5 9.8 Tf [(p)] TJ ET
BT 418.609 334.741 Td /F1 9.8 Tf [(<.01 level of significance.)] TJ ET
BT 26.250 315.336 Td /F1 9.8 Tf [(Both Tukey’s HSD and Scheffe’s post-hoc comparisons for business performance indicated that a \(5; 1\) funnel shaping strategy )] TJ ET
BT 26.250 303.432 Td /F1 9.8 Tf [(\(M=2157.5, SD=75.6\) outperforms a \(1; 1\) strategy \(M=2134.8, SD=99.1\) by $22M and both a \(5; 2\) strategy \(M=2120.9, )] TJ ET
BT 26.250 291.527 Td /F1 9.8 Tf [(SD=91.5\) and \(5; 3\) strategy \(M=2121.3, SD=79.3\) by $36M, at the p<.01 level of significance. A \(1; 1\) strategy does not differ )] TJ ET
BT 26.250 279.622 Td /F1 9.8 Tf [(significantly from the others and a \(5; 2\) and \(5; 3\) strategy do not differ significantly from each other.)] TJ ET
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BT 35.250 250.973 Td /F1 9.8 Tf [(c62bf7fa-1a46-9592-0c42-6cf361adb885)] TJ ET
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BT 35.250 224.591 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 214.591 Td /F1 8.0 Tf [(https://journal.emergentpublications.com/wp-content/uploads/2015/11/c62bf7fa-1a46-9592-0c42-6cf361adb885-300x165.png)] TJ ET
q
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BT 35.250 196.467 Td /F4 9.8 Tf [(Fig. 9: Figure 9)] TJ ET
BT 35.250 177.097 Td /F5 9.8 Tf [(Predictive performance of front-loaded funnel shaping strategies one-way ANOVA results \(n=200\))] TJ ET
Q
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0.500 0.500 0.500 rg
BT 35.250 110.450 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 100.450 Td /F1 8.0 Tf [(https://journal.emergentpublications.com/wp-content/uploads/2015/11/b9f2ceb5-07b9-79ff-529f-fff2b4289ae0-300x311.png)] TJ ET
q
35.250 73.945 537.000 17.905 re W n
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BT 35.250 82.326 Td /F4 9.8 Tf [(Fig. 10: Business performance of front-loaded funnel shaping strategies one-way ANOVA results \(n=200\))] TJ ET
Q
Q
q
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BT 35.250 767.476 Td /F4 9.8 Tf [(Fig. 8: Business performance of experimentation strategies one-way ANOVA results \(n=200\))] TJ ET
Q
BT 26.250 738.321 Td /F1 9.8 Tf [(Both Tukey’s HSD and Scheffe’s post-hoc comparisons indicated that old paradigm \(M=2241.9; SD=137.8\) is significantly )] TJ ET
BT 26.250 726.417 Td /F1 9.8 Tf [(outperformed by front-loaded paradigm \(M=2287.3; SD=78.2\), and early front loading \(M=2290; SD=94\) at the )] TJ ET
BT 503.698 726.417 Td /F5 9.8 Tf [(p)] TJ ET
BT 509.119 726.417 Td /F1 9.8 Tf [(<.01 level of )] TJ ET
BT 26.250 714.512 Td /F1 9.8 Tf [(significance for business performance. Front-loading and early front loading did not differ significantly, although the difference )] TJ ET
BT 26.250 702.607 Td /F1 9.8 Tf [(does amount to $2.7 million in favor of early front loading using the above-mentioned assumptions.)] TJ ET
BT 26.250 666.005 Td /F4 12.0 Tf [(Influence of discovery exploration funnel on predictive and business performance)] TJ ET
BT 26.250 646.050 Td /F1 9.8 Tf [(Simulation experiments probing for the influence of varying shapes of the discovery exploration funnel on predictive and )] TJ ET
BT 26.250 634.146 Td /F1 9.8 Tf [(business performance of early front-loaded experimentation strategies were conducted assuming a surrogate marker chain )] TJ ET
BT 26.250 622.241 Td /F1 9.8 Tf [(tightness of 70%, which reflects best current scientific constraints.)] TJ ET
BT 26.250 602.836 Td /F1 9.8 Tf [(\()] TJ ET
BT 29.497 602.836 Td /F5 9.8 Tf [(1\) Predictive performance)] TJ ET
BT 141.115 602.836 Td /F1 9.8 Tf [(. Referring to Figure 9, positive [F\(3, 769\)=23,84; )] TJ ET
BT 354.903 602.836 Td /F5 9.8 Tf [(p)] TJ ET
BT 360.324 602.836 Td /F1 9.8 Tf [(<.01] and negative [F\(3, 796\)=21.93; p<.01] )] TJ ET
BT 26.250 590.931 Td /F1 9.8 Tf [(predictive performance for the various front-loaded funnel shaping strategies differed significantly, although the actual )] TJ ET
BT 26.250 579.027 Td /F1 9.8 Tf [(differences in mean scores are quite small. Effect size, calculated using eta squared, was 0.084 and 0.077 for positive and )] TJ ET
BT 26.250 567.122 Td /F1 9.8 Tf [(negative predictive performance respectively, indicating a medium impact of the independent variables on the outcome )] TJ ET
BT 26.250 555.217 Td /F1 9.8 Tf [(variables. Levene’s test showed non-homogeneity of variance across the three groups — which I consider to be normal )] TJ ET
BT 26.250 543.312 Td /F1 9.8 Tf [(considering the different experimentation strategy types — implying the need for studying differences between groups at the )] TJ ET
BT 562.763 543.312 Td /F5 9.8 Tf [(p)] TJ ET
BT 26.250 531.408 Td /F1 9.8 Tf [(<.01 level of significance.)] TJ ET
BT 26.250 512.003 Td /F1 9.8 Tf [(Both Tukey’s HSD and Scheffe’s post-hoc comparisons for positive predictive performance indicated that a \(1; 1\) and a \(5; 1\) )] TJ ET
BT 26.250 500.098 Td /F1 9.8 Tf [(funnel shaping strategy both differ significantly from a \(5; 2\) and \(5; 3\) strategy at the )] TJ ET
BT 393.679 500.098 Td /F5 9.8 Tf [(p)] TJ ET
BT 399.100 500.098 Td /F1 9.8 Tf [(<.01 level of significance. A \(5; 2\) and a )] TJ ET
BT 26.250 488.193 Td /F1 9.8 Tf [(\(5; 3\) strategy do not differ significantly from each other.)] TJ ET
BT 26.250 468.789 Td /F1 9.8 Tf [(Both Tukey’s HSD and Scheffe’s post-hoc comparisons for negative predictive performance indicated that a \(1; 1\) and a \(5; 1\) )] TJ ET
BT 26.250 456.884 Td /F1 9.8 Tf [(funnel shaping strategy both differ significantly from a \(5; 2\) strategy and \(5; 3\) strategy at the )] TJ ET
BT 431.070 456.884 Td /F5 9.8 Tf [(p)] TJ ET
BT 436.491 456.884 Td /F1 9.8 Tf [(<.01 level of significance. Also, )] TJ ET
BT 26.250 444.979 Td /F1 9.8 Tf [(\(5; 2\) and \(5; 3\) strategies differ significantly from each other at the )] TJ ET
BT 316.712 444.979 Td /F5 9.8 Tf [(p)] TJ ET
BT 322.133 444.979 Td /F1 9.8 Tf [(<.01 level of significance.)] TJ ET
BT 26.250 425.574 Td /F1 9.8 Tf [(\()] TJ ET
BT 29.497 425.574 Td /F5 9.8 Tf [(2\) Business performance)] TJ ET
BT 137.868 425.574 Td /F1 9.8 Tf [(. A one-way between-group analysis of variance was conducted to probe for the impact of various )] TJ ET
BT 26.250 413.670 Td /F1 9.8 Tf [(front-loaded funnel shapes on the business performance of the experimentation strategy used. There was a statistically )] TJ ET
BT 26.250 401.765 Td /F1 9.8 Tf [(significant difference at the p<.01 level for the different funnel shaping strategies. Financial assumptions used to perform )] TJ ET
BT 26.250 389.860 Td /F1 9.8 Tf [(ANOVA were discussed above.)] TJ ET
BT 26.250 370.455 Td /F1 9.8 Tf [(Referring to Figure 10, business performance [F\(3, 769\)=7.9; )] TJ ET
BT 292.055 370.455 Td /F5 9.8 Tf [(p)] TJ ET
BT 297.476 370.455 Td /F1 9.8 Tf [(<.01] for the various front-loaded funnel shaping strategies )] TJ ET
BT 26.250 358.551 Td /F1 9.8 Tf [(differed significantly. This result needs to be moderated by the fact that the effect size, calculated using eta squared, was 0.031, )] TJ ET
BT 26.250 346.646 Td /F1 9.8 Tf [(indicating a small to medium impact of the independent variables on the outcome variable. Levene’s test showed homogeneity )] TJ ET
BT 26.250 334.741 Td /F1 9.8 Tf [(of variance across the three groups. Still, differences between groups were studied at the )] TJ ET
BT 413.188 334.741 Td /F5 9.8 Tf [(p)] TJ ET
BT 418.609 334.741 Td /F1 9.8 Tf [(<.01 level of significance.)] TJ ET
BT 26.250 315.336 Td /F1 9.8 Tf [(Both Tukey’s HSD and Scheffe’s post-hoc comparisons for business performance indicated that a \(5; 1\) funnel shaping strategy )] TJ ET
BT 26.250 303.432 Td /F1 9.8 Tf [(\(M=2157.5, SD=75.6\) outperforms a \(1; 1\) strategy \(M=2134.8, SD=99.1\) by $22M and both a \(5; 2\) strategy \(M=2120.9, )] TJ ET
BT 26.250 291.527 Td /F1 9.8 Tf [(SD=91.5\) and \(5; 3\) strategy \(M=2121.3, SD=79.3\) by $36M, at the p<.01 level of significance. A \(1; 1\) strategy does not differ )] TJ ET
BT 26.250 279.622 Td /F1 9.8 Tf [(significantly from the others and a \(5; 2\) and \(5; 3\) strategy do not differ significantly from each other.)] TJ ET
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BT 35.250 214.591 Td /F1 8.0 Tf [(https://journal.emergentpublications.com/wp-content/uploads/2015/11/c62bf7fa-1a46-9592-0c42-6cf361adb885-300x165.png)] TJ ET
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BT 35.250 196.467 Td /F4 9.8 Tf [(Fig. 9: Figure 9)] TJ ET
BT 35.250 177.097 Td /F5 9.8 Tf [(Predictive performance of front-loaded funnel shaping strategies one-way ANOVA results \(n=200\))] TJ ET
Q
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BT 35.250 136.831 Td /F1 9.8 Tf [(b9f2ceb5-07b9-79ff-529f-fff2b4289ae0)] TJ ET
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BT 35.250 82.326 Td /F4 9.8 Tf [(Fig. 10: Business performance of front-loaded funnel shaping strategies one-way ANOVA results \(n=200\))] TJ ET
Q
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BT 291.710 19.825 Td /F1 11.0 Tf [(9)] TJ ET
BT 25.000 19.825 Td /F1 11.0 Tf [(Emergence: Complexity and Organization)] TJ ET
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BT 26.250 750.278 Td /F4 12.0 Tf [(Discussion)] TJ ET
BT 26.250 730.324 Td /F1 9.8 Tf [(Two key dimensions of experimentation strategies for pharmaceutical discovery were used for theorizing in the computer )] TJ ET
BT 26.250 718.419 Td /F1 9.8 Tf [(simulation described above: 1\) the number of solution variables and problem-solving mechanisms characterized at various )] TJ ET
BT 26.250 706.515 Td /F1 9.8 Tf [(points during the discovery experimentation process; and 2\) the shape of the solution concept exploration funnel.)] TJ ET
BT 26.250 687.110 Td /F1 9.8 Tf [(\()] TJ ET
BT 29.497 687.110 Td /F5 9.8 Tf [(1\) The effect of front loading)] TJ ET
BT 151.440 687.110 Td /F1 9.8 Tf [(. Both front-loaded strategies \(early front loading and front loading\) significantly outperform the old )] TJ ET
BT 26.250 675.205 Td /F1 9.8 Tf [(paradigm experimentation strategy on positive predictive performance. However, the difference between both front-loaded )] TJ ET
BT 26.250 663.300 Td /F1 9.8 Tf [(strategies is insignificant, meaning in the context of pharmaceutical discovery that, in contrast to some practitioner views )] TJ ET
BT 26.250 651.396 Td /F1 9.8 Tf [(\(Pickering, 2001; Oprea, 2002; Yu & Adedayo, 2003\), in-silico characterization of bio-availability does not significantly increase )] TJ ET
BT 26.250 639.491 Td /F1 9.8 Tf [(positive predictive value as compared to its characterization starting in H2L, as done in the front-loaded paradigm. Also, in )] TJ ET
BT 26.250 627.586 Td /F1 9.8 Tf [(contrast to some practitioner views \(DeWitte, 2002\), our simulation results show non-significant differences in negative )] TJ ET
BT 26.250 615.681 Td /F1 9.8 Tf [(predictive performance for the various strategies.)] TJ ET
BT 26.250 596.277 Td /F1 9.8 Tf [(Finally and most importantly, this simulation leads us to propose that there is an inverse relationship between the previously )] TJ ET
BT 26.250 584.372 Td /F1 9.8 Tf [(defined chosen level of residual ambiguity at the end of discovery research and its positive predictive value. In practical terms, )] TJ ET
BT 26.250 572.467 Td /F1 9.8 Tf [(an experimentation strategy favoring more extensive characterization — aiming for lower levels of residual ambiguity — of a )] TJ ET
BT 26.250 560.562 Td /F1 9.8 Tf [(solution concept in discovery will increase its chances of surviving development after having successfully passed discovery. )] TJ ET
BT 26.250 548.658 Td /F1 9.8 Tf [(However, it will not decrease the probability of missed opportunities in development.)] TJ ET
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BT 35.250 465.503 Td /F4 9.8 Tf [(Fig. 11: A summary of discovery experimentation strategies)] TJ ET
Q
BT 26.250 436.348 Td /F1 9.8 Tf [(\()] TJ ET
BT 29.497 436.348 Td /F5 9.8 Tf [(2\) The effect of parallelism)] TJ ET
BT 144.371 436.348 Td /F1 9.8 Tf [(. Previous studies in technology-intensive industries indicate the benefits of broadening the concept )] TJ ET
BT 26.250 424.443 Td /F1 9.8 Tf [(testing funnel \(Sobek II )] TJ ET
BT 128.683 424.443 Td /F5 9.8 Tf [(et al)] TJ ET
BT 147.111 424.443 Td /F1 9.8 Tf [(., 1999\) or at least propose to optimize the shape of the concept funnel \(Dahan & Mendelson, )] TJ ET
BT 26.250 412.539 Td /F1 9.8 Tf [(2001\). However, the impact of concept funnel shaping strategies on predictive performance has not been studied before.)] TJ ET
BT 26.250 393.134 Td /F1 9.8 Tf [(In the simulation experiment, broadening the solution concept exploration funnel was found to have significant effects on )] TJ ET
BT 26.250 381.229 Td /F1 9.8 Tf [(predictive performance of an early front-loaded experimentation strategy during discovery. More specifically, broadening the )] TJ ET
BT 26.250 369.324 Td /F1 9.8 Tf [(funnel increases its negative predictive power, significantly decreasing the chances of missed opportunities in subsequent )] TJ ET
BT 26.250 357.420 Td /F1 9.8 Tf [(development. However, a minimum number of parallel concept explorations are required to gain effect. In contrast, simulation )] TJ ET
BT 26.250 345.515 Td /F1 9.8 Tf [(results showed that broadening the concept exploration funnel did significantly reduce the effect on positive predictive )] TJ ET
BT 26.250 333.610 Td /F1 9.8 Tf [(performance during discovery, significantly decreasing the chances of a candidate solution concept surviving development once )] TJ ET
BT 26.250 321.705 Td /F1 9.8 Tf [(promoted at the end of discovery.)] TJ ET
BT 26.250 302.301 Td /F1 9.8 Tf [(\()] TJ ET
BT 29.497 302.301 Td /F5 9.8 Tf [(3\) Business performance)] TJ ET
BT 137.868 302.301 Td /F1 9.8 Tf [(. This study’s simulation results indicate that front-loaded strategies applied during discovery lead to )] TJ ET
BT 26.250 290.396 Td /F1 9.8 Tf [(higher business value than old paradigm strategies. However, the difference in business performance between both early front-)] TJ ET
BT 26.250 278.491 Td /F1 9.8 Tf [(loaded and front-loaded strategies was insignificant. Also, broadening the concept funnel to a certain optimum point had a )] TJ ET
BT 26.250 266.586 Td /F1 9.8 Tf [(positive impact on business performance.)] TJ ET
BT 26.250 247.182 Td /F1 9.8 Tf [(Finally, the Bayesian belief network logic allows us to calculate the overall effects of different experimentation strategies )] TJ ET
BT 26.250 235.277 Td /F1 9.8 Tf [(concerning the level of front loading and discovery funnel shape. Given the financial assumptions described above, and given a )] TJ ET
BT 26.250 223.372 Td /F1 9.8 Tf [(surrogate marker chain featuring a predictive power of 70%, we see in Figure 11 that the choice of an early front-loaded )] TJ ET
BT 26.250 211.467 Td /F1 9.8 Tf [(experimentation strategy together with the decision to apply a \(5,1\) funnel shaping strategy could lead to maximum business )] TJ ET
BT 26.250 199.563 Td /F1 9.8 Tf [(performance.)] TJ ET
BT 26.250 162.960 Td /F4 12.0 Tf [(Validity considerations and limitations)] TJ ET
BT 26.250 143.006 Td /F1 9.8 Tf [(Computer simulations are without doubt reliable, but the important question is whether a specific simulation model is an )] TJ ET
BT 26.250 131.101 Td /F1 9.8 Tf [(acceptable representation of the corresponding real system, given the goal of the simulation model \(Kleijnen )] TJ ET
BT 495.010 131.101 Td /F5 9.8 Tf [(et al)] TJ ET
BT 513.438 131.101 Td /F1 9.8 Tf [(., 2001\). )] TJ ET
BT 26.250 119.196 Td /F1 9.8 Tf [(Models for simulation purposes cannot be shown to be true or valid in any absolute sense. What can be said is that the model )] TJ ET
BT 26.250 107.292 Td /F1 9.8 Tf [(can be valid under certain assumptions. In this study, key assumptions were formulated to reduce the complexity of )] TJ ET
BT 26.250 95.387 Td /F1 9.8 Tf [(representing the innovation process without endangering the fulfilment of the research goal of theory development. In summary, )] TJ ET
BT 26.250 83.482 Td /F1 9.8 Tf [(three simplifying assumptions were made concerning the representation of the solution landscape, and for optimization and )] TJ ET
BT 26.250 71.577 Td /F1 9.8 Tf [(selection conducted during the innovation process.)] TJ ET
BT 26.250 52.173 Td /F1 9.8 Tf [(First, the solution landscape was represented using three compound properties aggregated into reference compounds and )] TJ ET
BT 26.250 40.268 Td /F1 9.8 Tf [(chemical classes. This oversimplification of reality was necessary to make the implementation of the conceptual model possible )] TJ ET
Q
q
15.000 25.982 577.500 751.018 re W n
0.271 0.267 0.267 rg
BT 26.250 750.278 Td /F4 12.0 Tf [(Discussion)] TJ ET
BT 26.250 730.324 Td /F1 9.8 Tf [(Two key dimensions of experimentation strategies for pharmaceutical discovery were used for theorizing in the computer )] TJ ET
BT 26.250 718.419 Td /F1 9.8 Tf [(simulation described above: 1\) the number of solution variables and problem-solving mechanisms characterized at various )] TJ ET
BT 26.250 706.515 Td /F1 9.8 Tf [(points during the discovery experimentation process; and 2\) the shape of the solution concept exploration funnel.)] TJ ET
BT 26.250 687.110 Td /F1 9.8 Tf [(\()] TJ ET
BT 29.497 687.110 Td /F5 9.8 Tf [(1\) The effect of front loading)] TJ ET
BT 151.440 687.110 Td /F1 9.8 Tf [(. Both front-loaded strategies \(early front loading and front loading\) significantly outperform the old )] TJ ET
BT 26.250 675.205 Td /F1 9.8 Tf [(paradigm experimentation strategy on positive predictive performance. However, the difference between both front-loaded )] TJ ET
BT 26.250 663.300 Td /F1 9.8 Tf [(strategies is insignificant, meaning in the context of pharmaceutical discovery that, in contrast to some practitioner views )] TJ ET
BT 26.250 651.396 Td /F1 9.8 Tf [(\(Pickering, 2001; Oprea, 2002; Yu & Adedayo, 2003\), in-silico characterization of bio-availability does not significantly increase )] TJ ET
BT 26.250 639.491 Td /F1 9.8 Tf [(positive predictive value as compared to its characterization starting in H2L, as done in the front-loaded paradigm. Also, in )] TJ ET
BT 26.250 627.586 Td /F1 9.8 Tf [(contrast to some practitioner views \(DeWitte, 2002\), our simulation results show non-significant differences in negative )] TJ ET
BT 26.250 615.681 Td /F1 9.8 Tf [(predictive performance for the various strategies.)] TJ ET
BT 26.250 596.277 Td /F1 9.8 Tf [(Finally and most importantly, this simulation leads us to propose that there is an inverse relationship between the previously )] TJ ET
BT 26.250 584.372 Td /F1 9.8 Tf [(defined chosen level of residual ambiguity at the end of discovery research and its positive predictive value. In practical terms, )] TJ ET
BT 26.250 572.467 Td /F1 9.8 Tf [(an experimentation strategy favoring more extensive characterization — aiming for lower levels of residual ambiguity — of a )] TJ ET
BT 26.250 560.562 Td /F1 9.8 Tf [(solution concept in discovery will increase its chances of surviving development after having successfully passed discovery. )] TJ ET
BT 26.250 548.658 Td /F1 9.8 Tf [(However, it will not decrease the probability of missed opportunities in development.)] TJ ET
0.965 0.965 0.965 rg
26.250 453.372 555.000 85.405 re f
0.267 0.267 0.267 rg
0.267 0.267 0.267 RG
26.250 538.777 m 581.250 538.777 l 581.250 538.027 l 26.250 538.027 l f
26.250 453.372 m 581.250 453.372 l 581.250 454.122 l 26.250 454.122 l f
0.271 0.267 0.267 rg
BT 35.250 520.008 Td /F1 9.8 Tf [(e0a8de54-b908-bc76-3462-18304fc5a775)] TJ ET
0.500 0.500 0.500 rg
BT 35.250 493.627 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 483.627 Td /F1 8.0 Tf [(https://journal.emergentpublications.com/wp-content/uploads/2015/11/e0a8de54-b908-bc76-3462-18304fc5a775-300x181.png)] TJ ET
q
35.250 457.122 537.000 17.905 re W n
0.271 0.267 0.267 rg
BT 35.250 465.503 Td /F4 9.8 Tf [(Fig. 11: A summary of discovery experimentation strategies)] TJ ET
Q
BT 26.250 436.348 Td /F1 9.8 Tf [(\()] TJ ET
BT 29.497 436.348 Td /F5 9.8 Tf [(2\) The effect of parallelism)] TJ ET
BT 144.371 436.348 Td /F1 9.8 Tf [(. Previous studies in technology-intensive industries indicate the benefits of broadening the concept )] TJ ET
BT 26.250 424.443 Td /F1 9.8 Tf [(testing funnel \(Sobek II )] TJ ET
BT 128.683 424.443 Td /F5 9.8 Tf [(et al)] TJ ET
BT 147.111 424.443 Td /F1 9.8 Tf [(., 1999\) or at least propose to optimize the shape of the concept funnel \(Dahan & Mendelson, )] TJ ET
BT 26.250 412.539 Td /F1 9.8 Tf [(2001\). However, the impact of concept funnel shaping strategies on predictive performance has not been studied before.)] TJ ET
BT 26.250 393.134 Td /F1 9.8 Tf [(In the simulation experiment, broadening the solution concept exploration funnel was found to have significant effects on )] TJ ET
BT 26.250 381.229 Td /F1 9.8 Tf [(predictive performance of an early front-loaded experimentation strategy during discovery. More specifically, broadening the )] TJ ET
BT 26.250 369.324 Td /F1 9.8 Tf [(funnel increases its negative predictive power, significantly decreasing the chances of missed opportunities in subsequent )] TJ ET
BT 26.250 357.420 Td /F1 9.8 Tf [(development. However, a minimum number of parallel concept explorations are required to gain effect. In contrast, simulation )] TJ ET
BT 26.250 345.515 Td /F1 9.8 Tf [(results showed that broadening the concept exploration funnel did significantly reduce the effect on positive predictive )] TJ ET
BT 26.250 333.610 Td /F1 9.8 Tf [(performance during discovery, significantly decreasing the chances of a candidate solution concept surviving development once )] TJ ET
BT 26.250 321.705 Td /F1 9.8 Tf [(promoted at the end of discovery.)] TJ ET
BT 26.250 302.301 Td /F1 9.8 Tf [(\()] TJ ET
BT 29.497 302.301 Td /F5 9.8 Tf [(3\) Business performance)] TJ ET
BT 137.868 302.301 Td /F1 9.8 Tf [(. This study’s simulation results indicate that front-loaded strategies applied during discovery lead to )] TJ ET
BT 26.250 290.396 Td /F1 9.8 Tf [(higher business value than old paradigm strategies. However, the difference in business performance between both early front-)] TJ ET
BT 26.250 278.491 Td /F1 9.8 Tf [(loaded and front-loaded strategies was insignificant. Also, broadening the concept funnel to a certain optimum point had a )] TJ ET
BT 26.250 266.586 Td /F1 9.8 Tf [(positive impact on business performance.)] TJ ET
BT 26.250 247.182 Td /F1 9.8 Tf [(Finally, the Bayesian belief network logic allows us to calculate the overall effects of different experimentation strategies )] TJ ET
BT 26.250 235.277 Td /F1 9.8 Tf [(concerning the level of front loading and discovery funnel shape. Given the financial assumptions described above, and given a )] TJ ET
BT 26.250 223.372 Td /F1 9.8 Tf [(surrogate marker chain featuring a predictive power of 70%, we see in Figure 11 that the choice of an early front-loaded )] TJ ET
BT 26.250 211.467 Td /F1 9.8 Tf [(experimentation strategy together with the decision to apply a \(5,1\) funnel shaping strategy could lead to maximum business )] TJ ET
BT 26.250 199.563 Td /F1 9.8 Tf [(performance.)] TJ ET
BT 26.250 162.960 Td /F4 12.0 Tf [(Validity considerations and limitations)] TJ ET
BT 26.250 143.006 Td /F1 9.8 Tf [(Computer simulations are without doubt reliable, but the important question is whether a specific simulation model is an )] TJ ET
BT 26.250 131.101 Td /F1 9.8 Tf [(acceptable representation of the corresponding real system, given the goal of the simulation model \(Kleijnen )] TJ ET
BT 495.010 131.101 Td /F5 9.8 Tf [(et al)] TJ ET
BT 513.438 131.101 Td /F1 9.8 Tf [(., 2001\). )] TJ ET
BT 26.250 119.196 Td /F1 9.8 Tf [(Models for simulation purposes cannot be shown to be true or valid in any absolute sense. What can be said is that the model )] TJ ET
BT 26.250 107.292 Td /F1 9.8 Tf [(can be valid under certain assumptions. In this study, key assumptions were formulated to reduce the complexity of )] TJ ET
BT 26.250 95.387 Td /F1 9.8 Tf [(representing the innovation process without endangering the fulfilment of the research goal of theory development. In summary, )] TJ ET
BT 26.250 83.482 Td /F1 9.8 Tf [(three simplifying assumptions were made concerning the representation of the solution landscape, and for optimization and )] TJ ET
BT 26.250 71.577 Td /F1 9.8 Tf [(selection conducted during the innovation process.)] TJ ET
BT 26.250 52.173 Td /F1 9.8 Tf [(First, the solution landscape was represented using three compound properties aggregated into reference compounds and )] TJ ET
BT 26.250 40.268 Td /F1 9.8 Tf [(chemical classes. This oversimplification of reality was necessary to make the implementation of the conceptual model possible )] TJ ET
Q
q
15.000 25.982 577.500 751.018 re W n
0.271 0.267 0.267 rg
BT 26.250 750.278 Td /F4 12.0 Tf [(Discussion)] TJ ET
BT 26.250 730.324 Td /F1 9.8 Tf [(Two key dimensions of experimentation strategies for pharmaceutical discovery were used for theorizing in the computer )] TJ ET
BT 26.250 718.419 Td /F1 9.8 Tf [(simulation described above: 1\) the number of solution variables and problem-solving mechanisms characterized at various )] TJ ET
BT 26.250 706.515 Td /F1 9.8 Tf [(points during the discovery experimentation process; and 2\) the shape of the solution concept exploration funnel.)] TJ ET
BT 26.250 687.110 Td /F1 9.8 Tf [(\()] TJ ET
BT 29.497 687.110 Td /F5 9.8 Tf [(1\) The effect of front loading)] TJ ET
BT 151.440 687.110 Td /F1 9.8 Tf [(. Both front-loaded strategies \(early front loading and front loading\) significantly outperform the old )] TJ ET
BT 26.250 675.205 Td /F1 9.8 Tf [(paradigm experimentation strategy on positive predictive performance. However, the difference between both front-loaded )] TJ ET
BT 26.250 663.300 Td /F1 9.8 Tf [(strategies is insignificant, meaning in the context of pharmaceutical discovery that, in contrast to some practitioner views )] TJ ET
BT 26.250 651.396 Td /F1 9.8 Tf [(\(Pickering, 2001; Oprea, 2002; Yu & Adedayo, 2003\), in-silico characterization of bio-availability does not significantly increase )] TJ ET
BT 26.250 639.491 Td /F1 9.8 Tf [(positive predictive value as compared to its characterization starting in H2L, as done in the front-loaded paradigm. Also, in )] TJ ET
BT 26.250 627.586 Td /F1 9.8 Tf [(contrast to some practitioner views \(DeWitte, 2002\), our simulation results show non-significant differences in negative )] TJ ET
BT 26.250 615.681 Td /F1 9.8 Tf [(predictive performance for the various strategies.)] TJ ET
BT 26.250 596.277 Td /F1 9.8 Tf [(Finally and most importantly, this simulation leads us to propose that there is an inverse relationship between the previously )] TJ ET
BT 26.250 584.372 Td /F1 9.8 Tf [(defined chosen level of residual ambiguity at the end of discovery research and its positive predictive value. In practical terms, )] TJ ET
BT 26.250 572.467 Td /F1 9.8 Tf [(an experimentation strategy favoring more extensive characterization — aiming for lower levels of residual ambiguity — of a )] TJ ET
BT 26.250 560.562 Td /F1 9.8 Tf [(solution concept in discovery will increase its chances of surviving development after having successfully passed discovery. )] TJ ET
BT 26.250 548.658 Td /F1 9.8 Tf [(However, it will not decrease the probability of missed opportunities in development.)] TJ ET
0.965 0.965 0.965 rg
26.250 453.372 555.000 85.405 re f
0.267 0.267 0.267 rg
0.267 0.267 0.267 RG
26.250 538.777 m 581.250 538.777 l 581.250 538.027 l 26.250 538.027 l f
26.250 453.372 m 581.250 453.372 l 581.250 454.122 l 26.250 454.122 l f
0.271 0.267 0.267 rg
BT 35.250 520.008 Td /F1 9.8 Tf [(e0a8de54-b908-bc76-3462-18304fc5a775)] TJ ET
0.500 0.500 0.500 rg
BT 35.250 493.627 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 483.627 Td /F1 8.0 Tf [(https://journal.emergentpublications.com/wp-content/uploads/2015/11/e0a8de54-b908-bc76-3462-18304fc5a775-300x181.png)] TJ ET
q
35.250 457.122 537.000 17.905 re W n
0.271 0.267 0.267 rg
BT 35.250 465.503 Td /F4 9.8 Tf [(Fig. 11: A summary of discovery experimentation strategies)] TJ ET
Q
BT 26.250 436.348 Td /F1 9.8 Tf [(\()] TJ ET
BT 29.497 436.348 Td /F5 9.8 Tf [(2\) The effect of parallelism)] TJ ET
BT 144.371 436.348 Td /F1 9.8 Tf [(. Previous studies in technology-intensive industries indicate the benefits of broadening the concept )] TJ ET
BT 26.250 424.443 Td /F1 9.8 Tf [(testing funnel \(Sobek II )] TJ ET
BT 128.683 424.443 Td /F5 9.8 Tf [(et al)] TJ ET
BT 147.111 424.443 Td /F1 9.8 Tf [(., 1999\) or at least propose to optimize the shape of the concept funnel \(Dahan & Mendelson, )] TJ ET
BT 26.250 412.539 Td /F1 9.8 Tf [(2001\). However, the impact of concept funnel shaping strategies on predictive performance has not been studied before.)] TJ ET
BT 26.250 393.134 Td /F1 9.8 Tf [(In the simulation experiment, broadening the solution concept exploration funnel was found to have significant effects on )] TJ ET
BT 26.250 381.229 Td /F1 9.8 Tf [(predictive performance of an early front-loaded experimentation strategy during discovery. More specifically, broadening the )] TJ ET
BT 26.250 369.324 Td /F1 9.8 Tf [(funnel increases its negative predictive power, significantly decreasing the chances of missed opportunities in subsequent )] TJ ET
BT 26.250 357.420 Td /F1 9.8 Tf [(development. However, a minimum number of parallel concept explorations are required to gain effect. In contrast, simulation )] TJ ET
BT 26.250 345.515 Td /F1 9.8 Tf [(results showed that broadening the concept exploration funnel did significantly reduce the effect on positive predictive )] TJ ET
BT 26.250 333.610 Td /F1 9.8 Tf [(performance during discovery, significantly decreasing the chances of a candidate solution concept surviving development once )] TJ ET
BT 26.250 321.705 Td /F1 9.8 Tf [(promoted at the end of discovery.)] TJ ET
BT 26.250 302.301 Td /F1 9.8 Tf [(\()] TJ ET
BT 29.497 302.301 Td /F5 9.8 Tf [(3\) Business performance)] TJ ET
BT 137.868 302.301 Td /F1 9.8 Tf [(. This study’s simulation results indicate that front-loaded strategies applied during discovery lead to )] TJ ET
BT 26.250 290.396 Td /F1 9.8 Tf [(higher business value than old paradigm strategies. However, the difference in business performance between both early front-)] TJ ET
BT 26.250 278.491 Td /F1 9.8 Tf [(loaded and front-loaded strategies was insignificant. Also, broadening the concept funnel to a certain optimum point had a )] TJ ET
BT 26.250 266.586 Td /F1 9.8 Tf [(positive impact on business performance.)] TJ ET
BT 26.250 247.182 Td /F1 9.8 Tf [(Finally, the Bayesian belief network logic allows us to calculate the overall effects of different experimentation strategies )] TJ ET
BT 26.250 235.277 Td /F1 9.8 Tf [(concerning the level of front loading and discovery funnel shape. Given the financial assumptions described above, and given a )] TJ ET
BT 26.250 223.372 Td /F1 9.8 Tf [(surrogate marker chain featuring a predictive power of 70%, we see in Figure 11 that the choice of an early front-loaded )] TJ ET
BT 26.250 211.467 Td /F1 9.8 Tf [(experimentation strategy together with the decision to apply a \(5,1\) funnel shaping strategy could lead to maximum business )] TJ ET
BT 26.250 199.563 Td /F1 9.8 Tf [(performance.)] TJ ET
BT 26.250 162.960 Td /F4 12.0 Tf [(Validity considerations and limitations)] TJ ET
BT 26.250 143.006 Td /F1 9.8 Tf [(Computer simulations are without doubt reliable, but the important question is whether a specific simulation model is an )] TJ ET
BT 26.250 131.101 Td /F1 9.8 Tf [(acceptable representation of the corresponding real system, given the goal of the simulation model \(Kleijnen )] TJ ET
BT 495.010 131.101 Td /F5 9.8 Tf [(et al)] TJ ET
BT 513.438 131.101 Td /F1 9.8 Tf [(., 2001\). )] TJ ET
BT 26.250 119.196 Td /F1 9.8 Tf [(Models for simulation purposes cannot be shown to be true or valid in any absolute sense. What can be said is that the model )] TJ ET
BT 26.250 107.292 Td /F1 9.8 Tf [(can be valid under certain assumptions. In this study, key assumptions were formulated to reduce the complexity of )] TJ ET
BT 26.250 95.387 Td /F1 9.8 Tf [(representing the innovation process without endangering the fulfilment of the research goal of theory development. In summary, )] TJ ET
BT 26.250 83.482 Td /F1 9.8 Tf [(three simplifying assumptions were made concerning the representation of the solution landscape, and for optimization and )] TJ ET
BT 26.250 71.577 Td /F1 9.8 Tf [(selection conducted during the innovation process.)] TJ ET
BT 26.250 52.173 Td /F1 9.8 Tf [(First, the solution landscape was represented using three compound properties aggregated into reference compounds and )] TJ ET
BT 26.250 40.268 Td /F1 9.8 Tf [(chemical classes. This oversimplification of reality was necessary to make the implementation of the conceptual model possible )] TJ ET
Q
BT 35.250 520.008 Td /F1 9.8 Tf [(e0a8de54-b908-bc76-3462-18304fc5a775)] TJ ET
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BT 35.250 493.627 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 483.627 Td /F1 8.0 Tf [(https://journal.emergentpublications.com/wp-content/uploads/2015/11/e0a8de54-b908-bc76-3462-18304fc5a775-300x181.png)] TJ ET
q
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BT 291.710 19.825 Td /F1 11.0 Tf [(10)] TJ ET
BT 25.000 19.825 Td /F1 11.0 Tf [(Emergence: Complexity and Organization)] TJ ET
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BT 26.250 767.476 Td /F1 9.8 Tf [(in a VBA environment. Second, the innovation environment was represented as a mechanistic process of optimization stages )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(concluded by a number of decision gates, where candidate solution concepts were promoted to the next stage or terminated if )] TJ ET
BT 26.250 743.667 Td /F1 9.8 Tf [(they did not fulfill the selection criteria. Respecting the garbage can philosophy, the complex scientist optimization behavior was )] TJ ET
BT 26.250 731.762 Td /F1 9.8 Tf [(conservatively reduced to a simple multi-factorial function, taking the minimum of the \(P\) and \(B\) values as an input to a search )] TJ ET
BT 26.250 719.857 Td /F1 9.8 Tf [(for maximum performance in an extant search space. White box validation \(Pidd, 1992\) of this process with PharmaCo )] TJ ET
BT 26.250 707.952 Td /F1 9.8 Tf [(scientists showed that the model behaves in a reasonable fashion, depicting a familiar universe of organizing the discovery )] TJ ET
BT 26.250 696.048 Td /F1 9.8 Tf [(research process, and confirmed the face validity of this complexity reduction of reality, provided it served the purpose of theory )] TJ ET
BT 26.250 684.143 Td /F1 9.8 Tf [(development.)] TJ ET
BT 26.250 664.738 Td /F1 9.8 Tf [(However, considering the complexities of the probabilistic modelling of the innovation process and the hard-to-unravel nature of )] TJ ET
BT 26.250 652.833 Td /F1 9.8 Tf [(the model’s inner working, face validation was insufficient to claim internal validity of the simulation model. Therefore, validation )] TJ ET
BT 26.250 640.929 Td /F5 9.8 Tf [(ex negatio)] TJ ET
BT 71.237 640.929 Td /F1 9.8 Tf [( \(Masuch & Lapotin, 1989\) of key assumptions was done, showing that these assumptions do little harm to the )] TJ ET
BT 26.250 629.024 Td /F1 9.8 Tf [(model’s predictive power. This is why comparative performance conclusions about experimentation strategies were checked for )] TJ ET
BT 26.250 617.119 Td /F1 9.8 Tf [(robustness by varying the most basic parameters in the model. Thus, simulation results were shown to be robust for changes in )] TJ ET
BT 26.250 605.214 Td /F1 9.8 Tf [(solution landscape ruggedness up to a certain level, down to a certain level for varying numbers of the set of compounds )] TJ ET
BT 26.250 593.310 Td /F1 9.8 Tf [(declared active in LO, and for changes in the marginal probability )] TJ ET
BT 309.673 593.310 Td /F5 9.8 Tf [(p\(H\))] TJ ET
BT 328.627 593.310 Td /F1 9.8 Tf [(.)] TJ ET
BT 26.250 573.905 Td /F1 9.8 Tf [(Finally, external validity must be gained through empirical observation of the model’s predictions. “The behavior of the ‘real’ )] TJ ET
BT 26.250 562.000 Td /F1 9.8 Tf [(system is observed under specified conditions and the model is then run under conditions which are as close as possible to )] TJ ET
BT 26.250 550.095 Td /F1 9.8 Tf [(these. If the model is valid in a black box sense, then the observations of the model should be indistinguishable from those of )] TJ ET
BT 26.250 538.191 Td /F1 9.8 Tf [(the ‘real’ system” \(Pidd, 1992: 106\). Recent experience at PharmaCo[8] suggests external validity of the findings that front )] TJ ET
BT 26.250 526.286 Td /F1 9.8 Tf [(loading outperforms other experimentation strategies on positive predictive performance. Further longitudinal research data on )] TJ ET
BT 26.250 514.381 Td /F1 9.8 Tf [(compounds declared active by discovery and succeeding clinical development at PharmaCo and other pharmaceutical )] TJ ET
BT 26.250 502.476 Td /F1 9.8 Tf [(companies would be required to gain sufficient empirical support for the model’s predictions. It should be noted here that the )] TJ ET
BT 26.250 490.572 Td /F1 9.8 Tf [(model’s predictions on negative predictive performance are not verifiable in practice, since the key conditional probability )] TJ ET
BT 26.250 478.667 Td /F1 9.8 Tf [(involved is not observable in practice.)] TJ ET
BT 26.250 442.064 Td /F4 12.0 Tf [(Conclusion)] TJ ET
BT 26.250 422.110 Td /F1 9.8 Tf [(This study is, we believe, a first step in exploring the implications of realizing that the NPD process is a complex adaptive )] TJ ET
BT 26.250 410.205 Td /F1 9.8 Tf [(system, which does not have a single pathway to an inevitable outcome. It attempts to conceptually and quantitatively relate )] TJ ET
BT 26.250 398.301 Td /F1 9.8 Tf [(policy variables used to manage the fuzzy front-end innovation process to predictive performance. Thus, it adds predictability as )] TJ ET
BT 26.250 386.396 Td /F1 9.8 Tf [(a performance dimension to evaluate the fuzzy front end policy—performance link. Doing so, it makes a contribution to a )] TJ ET
BT 26.250 374.491 Td /F1 9.8 Tf [(growing research agenda designing optimized experimentation strategies in highly ambiguous and uncertain solution spaces )] TJ ET
BT 26.250 362.586 Td /F1 9.8 Tf [(\(Pelikan & Goldberg, 2003; Callan, 2003\).)] TJ ET
BT 26.250 343.182 Td /F1 9.8 Tf [(The annotated adaptive systems model of PharmaCo’s discovery research process produced reliable, internally valid results )] TJ ET
BT 26.250 331.277 Td /F1 9.8 Tf [(that could be used for theory generation.)] TJ ET
BT 26.250 311.872 Td /F1 9.8 Tf [(More specifically, simulation results indicated that both front-loaded experimentation strategies and their solution concept )] TJ ET
BT 26.250 299.967 Td /F1 9.8 Tf [(exploration funnel shape influence significantly predictive and business performance of experimentation strategies. A further )] TJ ET
BT 26.250 288.063 Td /F1 9.8 Tf [(stage in the research may look at the question of conducting experiments and allowing for a change in the possible target in the )] TJ ET
BT 26.250 276.158 Td /F1 9.8 Tf [(light of what happens. This could increase the rate of discovery of new drugs further, and this would be of great benefit in the )] TJ ET
BT 26.250 264.253 Td /F1 9.8 Tf [(current situation.)] TJ ET
BT 26.250 244.848 Td /F1 9.8 Tf [(Finally, as computer simulation was used as a technique for theorizing, further empirical validation of results is necessary to )] TJ ET
BT 26.250 232.944 Td /F1 9.8 Tf [(gain sufficient support for the model’s predictions. Most probably, falsification attempts in other pharmaceutical or research-)] TJ ET
BT 26.250 221.039 Td /F1 9.8 Tf [(intensive contexts will lead to modifications of the model’s present version, which only underlines the purpose of the model as a )] TJ ET
BT 26.250 209.134 Td /F1 9.8 Tf [(suitable adaptive theory generator.)] TJ ET
BT 26.250 172.532 Td /F4 12.0 Tf [(References)] TJ ET
BT 26.250 145.077 Td /F1 9.8 Tf [(1.)] TJ ET
BT 38.132 145.077 Td /F1 9.8 Tf [(Allen, P. M. and Ebeling, W. \(1983\). “Evolution and the stochastic description of simple ecosystems,” BioSystems, ISSN )] TJ ET
BT 26.250 133.173 Td /F1 9.8 Tf [(0303-2647, 16: 113-126.)] TJ ET
BT 26.250 113.768 Td /F1 9.8 Tf [(2.)] TJ ET
BT 38.132 113.768 Td /F1 9.8 Tf [(Allen, P. M. \(2001\). “The dynamics of knowledge and ignorance: Learning the new systems science,” in M.)] TJ ET
BT 26.250 94.363 Td /F1 9.8 Tf [(3.)] TJ ET
BT 38.132 94.363 Td /F1 9.8 Tf [(Matthies, H. Malchow and J. Kriz \(eds.\), Integrative Approaches to Natural and Social Dynamics, Berlin, Germany: Springer )] TJ ET
BT 26.250 82.458 Td /F1 9.8 Tf [(Verlag, ISBN 3540412921, pp. 3-29.)] TJ ET
BT 26.250 63.054 Td /F1 9.8 Tf [(4.)] TJ ET
BT 38.132 63.054 Td /F1 9.8 Tf [(Allen, P. M. and Strathern, M. \(2005\). “Models, knowledge creation and their limits,” Futures, ISSN 0746-2468, 37\(7\): 729-)] TJ ET
BT 26.250 51.149 Td /F1 9.8 Tf [(744 .)] TJ ET
Q
q
15.000 41.268 577.500 735.732 re W n
0.271 0.267 0.267 rg
BT 26.250 767.476 Td /F1 9.8 Tf [(in a VBA environment. Second, the innovation environment was represented as a mechanistic process of optimization stages )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(concluded by a number of decision gates, where candidate solution concepts were promoted to the next stage or terminated if )] TJ ET
BT 26.250 743.667 Td /F1 9.8 Tf [(they did not fulfill the selection criteria. Respecting the garbage can philosophy, the complex scientist optimization behavior was )] TJ ET
BT 26.250 731.762 Td /F1 9.8 Tf [(conservatively reduced to a simple multi-factorial function, taking the minimum of the \(P\) and \(B\) values as an input to a search )] TJ ET
BT 26.250 719.857 Td /F1 9.8 Tf [(for maximum performance in an extant search space. White box validation \(Pidd, 1992\) of this process with PharmaCo )] TJ ET
BT 26.250 707.952 Td /F1 9.8 Tf [(scientists showed that the model behaves in a reasonable fashion, depicting a familiar universe of organizing the discovery )] TJ ET
BT 26.250 696.048 Td /F1 9.8 Tf [(research process, and confirmed the face validity of this complexity reduction of reality, provided it served the purpose of theory )] TJ ET
BT 26.250 684.143 Td /F1 9.8 Tf [(development.)] TJ ET
BT 26.250 664.738 Td /F1 9.8 Tf [(However, considering the complexities of the probabilistic modelling of the innovation process and the hard-to-unravel nature of )] TJ ET
BT 26.250 652.833 Td /F1 9.8 Tf [(the model’s inner working, face validation was insufficient to claim internal validity of the simulation model. Therefore, validation )] TJ ET
BT 26.250 640.929 Td /F5 9.8 Tf [(ex negatio)] TJ ET
BT 71.237 640.929 Td /F1 9.8 Tf [( \(Masuch & Lapotin, 1989\) of key assumptions was done, showing that these assumptions do little harm to the )] TJ ET
BT 26.250 629.024 Td /F1 9.8 Tf [(model’s predictive power. This is why comparative performance conclusions about experimentation strategies were checked for )] TJ ET
BT 26.250 617.119 Td /F1 9.8 Tf [(robustness by varying the most basic parameters in the model. Thus, simulation results were shown to be robust for changes in )] TJ ET
BT 26.250 605.214 Td /F1 9.8 Tf [(solution landscape ruggedness up to a certain level, down to a certain level for varying numbers of the set of compounds )] TJ ET
BT 26.250 593.310 Td /F1 9.8 Tf [(declared active in LO, and for changes in the marginal probability )] TJ ET
BT 309.673 593.310 Td /F5 9.8 Tf [(p\(H\))] TJ ET
BT 328.627 593.310 Td /F1 9.8 Tf [(.)] TJ ET
BT 26.250 573.905 Td /F1 9.8 Tf [(Finally, external validity must be gained through empirical observation of the model’s predictions. “The behavior of the ‘real’ )] TJ ET
BT 26.250 562.000 Td /F1 9.8 Tf [(system is observed under specified conditions and the model is then run under conditions which are as close as possible to )] TJ ET
BT 26.250 550.095 Td /F1 9.8 Tf [(these. If the model is valid in a black box sense, then the observations of the model should be indistinguishable from those of )] TJ ET
BT 26.250 538.191 Td /F1 9.8 Tf [(the ‘real’ system” \(Pidd, 1992: 106\). Recent experience at PharmaCo[8] suggests external validity of the findings that front )] TJ ET
BT 26.250 526.286 Td /F1 9.8 Tf [(loading outperforms other experimentation strategies on positive predictive performance. Further longitudinal research data on )] TJ ET
BT 26.250 514.381 Td /F1 9.8 Tf [(compounds declared active by discovery and succeeding clinical development at PharmaCo and other pharmaceutical )] TJ ET
BT 26.250 502.476 Td /F1 9.8 Tf [(companies would be required to gain sufficient empirical support for the model’s predictions. It should be noted here that the )] TJ ET
BT 26.250 490.572 Td /F1 9.8 Tf [(model’s predictions on negative predictive performance are not verifiable in practice, since the key conditional probability )] TJ ET
BT 26.250 478.667 Td /F1 9.8 Tf [(involved is not observable in practice.)] TJ ET
BT 26.250 442.064 Td /F4 12.0 Tf [(Conclusion)] TJ ET
BT 26.250 422.110 Td /F1 9.8 Tf [(This study is, we believe, a first step in exploring the implications of realizing that the NPD process is a complex adaptive )] TJ ET
BT 26.250 410.205 Td /F1 9.8 Tf [(system, which does not have a single pathway to an inevitable outcome. It attempts to conceptually and quantitatively relate )] TJ ET
BT 26.250 398.301 Td /F1 9.8 Tf [(policy variables used to manage the fuzzy front-end innovation process to predictive performance. Thus, it adds predictability as )] TJ ET
BT 26.250 386.396 Td /F1 9.8 Tf [(a performance dimension to evaluate the fuzzy front end policy—performance link. Doing so, it makes a contribution to a )] TJ ET
BT 26.250 374.491 Td /F1 9.8 Tf [(growing research agenda designing optimized experimentation strategies in highly ambiguous and uncertain solution spaces )] TJ ET
BT 26.250 362.586 Td /F1 9.8 Tf [(\(Pelikan & Goldberg, 2003; Callan, 2003\).)] TJ ET
BT 26.250 343.182 Td /F1 9.8 Tf [(The annotated adaptive systems model of PharmaCo’s discovery research process produced reliable, internally valid results )] TJ ET
BT 26.250 331.277 Td /F1 9.8 Tf [(that could be used for theory generation.)] TJ ET
BT 26.250 311.872 Td /F1 9.8 Tf [(More specifically, simulation results indicated that both front-loaded experimentation strategies and their solution concept )] TJ ET
BT 26.250 299.967 Td /F1 9.8 Tf [(exploration funnel shape influence significantly predictive and business performance of experimentation strategies. A further )] TJ ET
BT 26.250 288.063 Td /F1 9.8 Tf [(stage in the research may look at the question of conducting experiments and allowing for a change in the possible target in the )] TJ ET
BT 26.250 276.158 Td /F1 9.8 Tf [(light of what happens. This could increase the rate of discovery of new drugs further, and this would be of great benefit in the )] TJ ET
BT 26.250 264.253 Td /F1 9.8 Tf [(current situation.)] TJ ET
BT 26.250 244.848 Td /F1 9.8 Tf [(Finally, as computer simulation was used as a technique for theorizing, further empirical validation of results is necessary to )] TJ ET
BT 26.250 232.944 Td /F1 9.8 Tf [(gain sufficient support for the model’s predictions. Most probably, falsification attempts in other pharmaceutical or research-)] TJ ET
BT 26.250 221.039 Td /F1 9.8 Tf [(intensive contexts will lead to modifications of the model’s present version, which only underlines the purpose of the model as a )] TJ ET
BT 26.250 209.134 Td /F1 9.8 Tf [(suitable adaptive theory generator.)] TJ ET
BT 26.250 172.532 Td /F4 12.0 Tf [(References)] TJ ET
BT 26.250 145.077 Td /F1 9.8 Tf [(1.)] TJ ET
BT 38.132 145.077 Td /F1 9.8 Tf [(Allen, P. M. and Ebeling, W. \(1983\). “Evolution and the stochastic description of simple ecosystems,” BioSystems, ISSN )] TJ ET
BT 26.250 133.173 Td /F1 9.8 Tf [(0303-2647, 16: 113-126.)] TJ ET
BT 26.250 113.768 Td /F1 9.8 Tf [(2.)] TJ ET
BT 38.132 113.768 Td /F1 9.8 Tf [(Allen, P. M. \(2001\). “The dynamics of knowledge and ignorance: Learning the new systems science,” in M.)] TJ ET
BT 26.250 94.363 Td /F1 9.8 Tf [(3.)] TJ ET
BT 38.132 94.363 Td /F1 9.8 Tf [(Matthies, H. Malchow and J. Kriz \(eds.\), Integrative Approaches to Natural and Social Dynamics, Berlin, Germany: Springer )] TJ ET
BT 26.250 82.458 Td /F1 9.8 Tf [(Verlag, ISBN 3540412921, pp. 3-29.)] TJ ET
BT 26.250 63.054 Td /F1 9.8 Tf [(4.)] TJ ET
BT 38.132 63.054 Td /F1 9.8 Tf [(Allen, P. M. and Strathern, M. \(2005\). “Models, knowledge creation and their limits,” Futures, ISSN 0746-2468, 37\(7\): 729-)] TJ ET
BT 26.250 51.149 Td /F1 9.8 Tf [(744 .)] TJ ET
Q
q
15.000 41.268 577.500 735.732 re W n
0.271 0.267 0.267 rg
BT 26.250 767.476 Td /F1 9.8 Tf [(in a VBA environment. Second, the innovation environment was represented as a mechanistic process of optimization stages )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(concluded by a number of decision gates, where candidate solution concepts were promoted to the next stage or terminated if )] TJ ET
BT 26.250 743.667 Td /F1 9.8 Tf [(they did not fulfill the selection criteria. Respecting the garbage can philosophy, the complex scientist optimization behavior was )] TJ ET
BT 26.250 731.762 Td /F1 9.8 Tf [(conservatively reduced to a simple multi-factorial function, taking the minimum of the \(P\) and \(B\) values as an input to a search )] TJ ET
BT 26.250 719.857 Td /F1 9.8 Tf [(for maximum performance in an extant search space. White box validation \(Pidd, 1992\) of this process with PharmaCo )] TJ ET
BT 26.250 707.952 Td /F1 9.8 Tf [(scientists showed that the model behaves in a reasonable fashion, depicting a familiar universe of organizing the discovery )] TJ ET
BT 26.250 696.048 Td /F1 9.8 Tf [(research process, and confirmed the face validity of this complexity reduction of reality, provided it served the purpose of theory )] TJ ET
BT 26.250 684.143 Td /F1 9.8 Tf [(development.)] TJ ET
BT 26.250 664.738 Td /F1 9.8 Tf [(However, considering the complexities of the probabilistic modelling of the innovation process and the hard-to-unravel nature of )] TJ ET
BT 26.250 652.833 Td /F1 9.8 Tf [(the model’s inner working, face validation was insufficient to claim internal validity of the simulation model. Therefore, validation )] TJ ET
BT 26.250 640.929 Td /F5 9.8 Tf [(ex negatio)] TJ ET
BT 71.237 640.929 Td /F1 9.8 Tf [( \(Masuch & Lapotin, 1989\) of key assumptions was done, showing that these assumptions do little harm to the )] TJ ET
BT 26.250 629.024 Td /F1 9.8 Tf [(model’s predictive power. This is why comparative performance conclusions about experimentation strategies were checked for )] TJ ET
BT 26.250 617.119 Td /F1 9.8 Tf [(robustness by varying the most basic parameters in the model. Thus, simulation results were shown to be robust for changes in )] TJ ET
BT 26.250 605.214 Td /F1 9.8 Tf [(solution landscape ruggedness up to a certain level, down to a certain level for varying numbers of the set of compounds )] TJ ET
BT 26.250 593.310 Td /F1 9.8 Tf [(declared active in LO, and for changes in the marginal probability )] TJ ET
BT 309.673 593.310 Td /F5 9.8 Tf [(p\(H\))] TJ ET
BT 328.627 593.310 Td /F1 9.8 Tf [(.)] TJ ET
BT 26.250 573.905 Td /F1 9.8 Tf [(Finally, external validity must be gained through empirical observation of the model’s predictions. “The behavior of the ‘real’ )] TJ ET
BT 26.250 562.000 Td /F1 9.8 Tf [(system is observed under specified conditions and the model is then run under conditions which are as close as possible to )] TJ ET
BT 26.250 550.095 Td /F1 9.8 Tf [(these. If the model is valid in a black box sense, then the observations of the model should be indistinguishable from those of )] TJ ET
BT 26.250 538.191 Td /F1 9.8 Tf [(the ‘real’ system” \(Pidd, 1992: 106\). Recent experience at PharmaCo[8] suggests external validity of the findings that front )] TJ ET
BT 26.250 526.286 Td /F1 9.8 Tf [(loading outperforms other experimentation strategies on positive predictive performance. Further longitudinal research data on )] TJ ET
BT 26.250 514.381 Td /F1 9.8 Tf [(compounds declared active by discovery and succeeding clinical development at PharmaCo and other pharmaceutical )] TJ ET
BT 26.250 502.476 Td /F1 9.8 Tf [(companies would be required to gain sufficient empirical support for the model’s predictions. It should be noted here that the )] TJ ET
BT 26.250 490.572 Td /F1 9.8 Tf [(model’s predictions on negative predictive performance are not verifiable in practice, since the key conditional probability )] TJ ET
BT 26.250 478.667 Td /F1 9.8 Tf [(involved is not observable in practice.)] TJ ET
BT 26.250 442.064 Td /F4 12.0 Tf [(Conclusion)] TJ ET
BT 26.250 422.110 Td /F1 9.8 Tf [(This study is, we believe, a first step in exploring the implications of realizing that the NPD process is a complex adaptive )] TJ ET
BT 26.250 410.205 Td /F1 9.8 Tf [(system, which does not have a single pathway to an inevitable outcome. It attempts to conceptually and quantitatively relate )] TJ ET
BT 26.250 398.301 Td /F1 9.8 Tf [(policy variables used to manage the fuzzy front-end innovation process to predictive performance. Thus, it adds predictability as )] TJ ET
BT 26.250 386.396 Td /F1 9.8 Tf [(a performance dimension to evaluate the fuzzy front end policy—performance link. Doing so, it makes a contribution to a )] TJ ET
BT 26.250 374.491 Td /F1 9.8 Tf [(growing research agenda designing optimized experimentation strategies in highly ambiguous and uncertain solution spaces )] TJ ET
BT 26.250 362.586 Td /F1 9.8 Tf [(\(Pelikan & Goldberg, 2003; Callan, 2003\).)] TJ ET
BT 26.250 343.182 Td /F1 9.8 Tf [(The annotated adaptive systems model of PharmaCo’s discovery research process produced reliable, internally valid results )] TJ ET
BT 26.250 331.277 Td /F1 9.8 Tf [(that could be used for theory generation.)] TJ ET
BT 26.250 311.872 Td /F1 9.8 Tf [(More specifically, simulation results indicated that both front-loaded experimentation strategies and their solution concept )] TJ ET
BT 26.250 299.967 Td /F1 9.8 Tf [(exploration funnel shape influence significantly predictive and business performance of experimentation strategies. A further )] TJ ET
BT 26.250 288.063 Td /F1 9.8 Tf [(stage in the research may look at the question of conducting experiments and allowing for a change in the possible target in the )] TJ ET
BT 26.250 276.158 Td /F1 9.8 Tf [(light of what happens. This could increase the rate of discovery of new drugs further, and this would be of great benefit in the )] TJ ET
BT 26.250 264.253 Td /F1 9.8 Tf [(current situation.)] TJ ET
BT 26.250 244.848 Td /F1 9.8 Tf [(Finally, as computer simulation was used as a technique for theorizing, further empirical validation of results is necessary to )] TJ ET
BT 26.250 232.944 Td /F1 9.8 Tf [(gain sufficient support for the model’s predictions. Most probably, falsification attempts in other pharmaceutical or research-)] TJ ET
BT 26.250 221.039 Td /F1 9.8 Tf [(intensive contexts will lead to modifications of the model’s present version, which only underlines the purpose of the model as a )] TJ ET
BT 26.250 209.134 Td /F1 9.8 Tf [(suitable adaptive theory generator.)] TJ ET
BT 26.250 172.532 Td /F4 12.0 Tf [(References)] TJ ET
BT 26.250 145.077 Td /F1 9.8 Tf [(1.)] TJ ET
BT 38.132 145.077 Td /F1 9.8 Tf [(Allen, P. M. and Ebeling, W. \(1983\). “Evolution and the stochastic description of simple ecosystems,” BioSystems, ISSN )] TJ ET
BT 26.250 133.173 Td /F1 9.8 Tf [(0303-2647, 16: 113-126.)] TJ ET
BT 26.250 113.768 Td /F1 9.8 Tf [(2.)] TJ ET
BT 38.132 113.768 Td /F1 9.8 Tf [(Allen, P. M. \(2001\). “The dynamics of knowledge and ignorance: Learning the new systems science,” in M.)] TJ ET
BT 26.250 94.363 Td /F1 9.8 Tf [(3.)] TJ ET
BT 38.132 94.363 Td /F1 9.8 Tf [(Matthies, H. Malchow and J. Kriz \(eds.\), Integrative Approaches to Natural and Social Dynamics, Berlin, Germany: Springer )] TJ ET
BT 26.250 82.458 Td /F1 9.8 Tf [(Verlag, ISBN 3540412921, pp. 3-29.)] TJ ET
BT 26.250 63.054 Td /F1 9.8 Tf [(4.)] TJ ET
BT 38.132 63.054 Td /F1 9.8 Tf [(Allen, P. M. and Strathern, M. \(2005\). “Models, knowledge creation and their limits,” Futures, ISSN 0746-2468, 37\(7\): 729-)] TJ ET
BT 26.250 51.149 Td /F1 9.8 Tf [(744 .)] TJ ET
Q
q
0.000 0.000 0.000 rg
BT 291.710 19.825 Td /F1 11.0 Tf [(11)] TJ ET
BT 25.000 19.825 Td /F1 11.0 Tf [(Emergence: Complexity and Organization)] TJ ET
Q
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0.271 0.267 0.267 rg
q
15.000 29.977 577.500 747.023 re W n
0.271 0.267 0.267 rg
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BT 43.553 345.453 Td /F1 9.8 Tf [(Holland, J. H. \(1998\). Emergence: From Chaos to Order, Oxford, England: Oxford University Press, ISBN 0198504098.)] TJ ET
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BT 26.250 150.096 Td /F1 9.8 Tf [(26.)] TJ ET
BT 43.553 150.096 Td /F1 9.8 Tf [(Krishnan, V., Eppinger, S. D. and Whitney, D. \(1997\). “A model based framework to overlap product development activities,” )] TJ ET
BT 26.250 138.191 Td /F1 9.8 Tf [(Management Science, ISSN 0025-1909, 43\(4\): 437-451.)] TJ ET
BT 26.250 118.786 Td /F1 9.8 Tf [(27.)] TJ ET
BT 43.553 118.786 Td /F1 9.8 Tf [(Lesko, L. J., Rowland, M., Peck, C. C. and Blaschke, T. F. \(2000\). “Optimizing the science of drug development: )] TJ ET
BT 26.250 106.881 Td /F1 9.8 Tf [(Opportunities for better candidate selection and accelerated evaluation in humans,” Pharmaceutical Research, ISSN 0724-)] TJ ET
BT 26.250 94.977 Td /F1 9.8 Tf [(8741, 17\(11\): 1335-1344.)] TJ ET
BT 26.250 75.572 Td /F1 9.8 Tf [(28.)] TJ ET
BT 43.553 75.572 Td /F1 9.8 Tf [(Loch, C. H. and Terwiesch, C. \(1998\). “Communication and uncertainty in concurrent engineering,” Management Science, )] TJ ET
BT 26.250 63.667 Td /F1 9.8 Tf [(ISSN 0025-1909, 44\(8\): 1032-1048.)] TJ ET
BT 26.250 44.262 Td /F1 9.8 Tf [(29.)] TJ ET
BT 43.553 44.262 Td /F1 9.8 Tf [(Loch, C. H., Terwiesch, C. and Thomke, S. \(2001\). “Parallel and sequential testing of design alternatives,” Management )] TJ ET
Q
q
15.000 29.977 577.500 747.023 re W n
0.271 0.267 0.267 rg
BT 26.250 759.976 Td /F1 9.8 Tf [(5.)] TJ ET
BT 38.132 759.976 Td /F1 9.8 Tf [(Abernathy, W. and Rosenbloom, R. \(1968\). “Parallel and sequential R&D strategies: Application of a simple model,” IEEE )] TJ ET
BT 26.250 748.071 Td /F1 9.8 Tf [(Transactions on Engineering Management, ISSN 0018-9391, 15\(1\): 2-10.)] TJ ET
BT 26.250 728.667 Td /F1 9.8 Tf [(6.)] TJ ET
BT 38.132 728.667 Td /F1 9.8 Tf [(Bacon, G., Beckman, S., Mowery, D. and Wilson, E. \(1994\). “Managing product definition in high-technology industries: A )] TJ ET
BT 26.250 716.762 Td /F1 9.8 Tf [(pilot study,” California Management Review, ISSN 0008-1256, 36: 32-56.)] TJ ET
BT 26.250 697.357 Td /F1 9.8 Tf [(7.)] TJ ET
BT 38.132 697.357 Td /F1 9.8 Tf [(Callan, R. \(2003\). Artificial Intelligence, New York, NY: Palgrave Macmillan, ISBN 0333801369.)] TJ ET
BT 26.250 677.952 Td /F1 9.8 Tf [(8.)] TJ ET
BT 38.132 677.952 Td /F1 9.8 Tf [(Clark, K. B. and Wheelwright, S. C. \(1993\). Managing New Product and Process Development: Text and Cases, New York, )] TJ ET
BT 26.250 666.048 Td /F1 9.8 Tf [(NY: The Free Press, ISBN 0029055172 \(1992\).)] TJ ET
BT 26.250 646.643 Td /F1 9.8 Tf [(9.)] TJ ET
BT 38.132 646.643 Td /F1 9.8 Tf [(Cohen, M. D., March, J. G. and Olsen, J. P. \(1972\). “A garbage can model of organizational choice,” Administrative Science )] TJ ET
BT 26.250 634.738 Td /F1 9.8 Tf [(Quarterly, ISSN 0001-8392, 17\(1\): 1-25.)] TJ ET
BT 26.250 615.333 Td /F1 9.8 Tf [(10.)] TJ ET
BT 43.553 615.333 Td /F1 9.8 Tf [(Congdon, P. \(2001\) Bayesian Statistical Modelling, Chichester, England: John Wiley & Sons, ISBN 0471496006.)] TJ ET
BT 26.250 595.929 Td /F1 9.8 Tf [(11.)] TJ ET
BT 43.553 595.929 Td /F1 9.8 Tf [(Dahan, E. and Mendelson, H. \(2001\). “An extreme-value model of concept testing,” Management Science, ISSN 0025-)] TJ ET
BT 26.250 584.024 Td /F1 9.8 Tf [(1909, 47\(1\): 102-116.)] TJ ET
BT 26.250 564.619 Td /F1 9.8 Tf [(12.)] TJ ET
BT 43.553 564.619 Td /F1 9.8 Tf [(DeWitte, R. S. \(2002\). “On experimental design in drug discovery,” Current Drug Discovery, ISSN 1570-1638, \(February\): )] TJ ET
BT 26.250 552.714 Td /F1 9.8 Tf [(19-22.)] TJ ET
BT 26.250 533.310 Td /F1 9.8 Tf [(13.)] TJ ET
BT 43.553 533.310 Td /F1 9.8 Tf [(Drews, J. \(1998\). “Innovation deficit revisited: Reflections on the productivity of pharmaceutical R&D, ” Drug Discovery )] TJ ET
BT 26.250 521.405 Td /F1 9.8 Tf [(Today, ISSN 1359-6446, 3: 491-494.)] TJ ET
BT 26.250 502.000 Td /F1 9.8 Tf [(14.)] TJ ET
BT 43.553 502.000 Td /F1 9.8 Tf [(Duyck, J. \(2003\). “Attrition and translation,” Science, ISSN 0036-8075, 302\(0ctober 24\): 603-605.)] TJ ET
BT 26.250 482.595 Td /F1 9.8 Tf [(15.)] TJ ET
BT 43.553 482.595 Td /F1 9.8 Tf [(Furness, L. M. \(2003\). “Genomics applications that facilitate the understanding of drug action and toxicity,” in J. Licinio and )] TJ ET
BT 26.250 470.691 Td /F1 9.8 Tf [(M.-L. Wong \(eds.\), Pharmacogenomics: The Search for Individualized Therapies, Weinheim, Germany: Wiley-VCH, ISBN )] TJ ET
BT 26.250 458.786 Td /F1 9.8 Tf [(3527303804.)] TJ ET
BT 26.250 439.381 Td /F1 9.8 Tf [(16.)] TJ ET
BT 43.553 439.381 Td /F1 9.8 Tf [(Gerwin, D. and Barrowman, N. J. \(2002\). “An evaluation of research on integrated product development,” Management )] TJ ET
BT 26.250 427.476 Td /F1 9.8 Tf [(Science, ISSN 0025-1909, 48\(7\): 938-953.)] TJ ET
BT 26.250 408.072 Td /F1 9.8 Tf [(17.)] TJ ET
BT 43.553 408.072 Td /F1 9.8 Tf [(Golder, P. N. and Tellis, G. J. \(2004\). “Growing, growing, gone: Cascades, diffusion, and turning points in the product life )] TJ ET
BT 26.250 396.167 Td /F1 9.8 Tf [(cycle,” Marketing Science, ISSN 0732-2399, 23\(2\): 207-218.)] TJ ET
BT 26.250 376.762 Td /F1 9.8 Tf [(18.)] TJ ET
BT 43.553 376.762 Td /F1 9.8 Tf [(Holland, J. \(1992\). Adaptation in Natural and Artificial Sy s t e m s , C a m b r i d g e , M A : M I T P r e s s , I S B N )] TJ ET
BT 26.250 364.857 Td /F1 9.8 Tf [(0195088166.)] TJ ET
BT 26.250 345.453 Td /F1 9.8 Tf [(19.)] TJ ET
BT 43.553 345.453 Td /F1 9.8 Tf [(Holland, J. H. \(1998\). Emergence: From Chaos to Order, Oxford, England: Oxford University Press, ISBN 0198504098.)] TJ ET
BT 26.250 326.048 Td /F1 9.8 Tf [(20.)] TJ ET
BT 43.553 326.048 Td /F1 9.8 Tf [(Iansiti, M. \(1998\). Technology Integration: Making Critical Choices in a Dynamic World , Boston, MA: Harvard Business )] TJ ET
BT 26.250 314.143 Td /F1 9.8 Tf [(School Press, ISBN 0875847870.)] TJ ET
BT 26.250 294.738 Td /F1 9.8 Tf [(21.)] TJ ET
BT 43.553 294.738 Td /F1 9.8 Tf [(Jensen, F. V. \(2001\). Bayesian Networks and Decision Graphs, New York, NY: Springer-Verlag, ISBN 0387952594.)] TJ ET
BT 26.250 275.334 Td /F1 9.8 Tf [(22.)] TJ ET
BT 43.553 275.334 Td /F1 9.8 Tf [(Kennedy, T. \(1997\). “Managing the drug discovery/development interface,” Drug Discovery Today, ISSN 13596446, 2\(10\): )] TJ ET
BT 26.250 263.429 Td /F1 9.8 Tf [(436-444.)] TJ ET
BT 26.250 244.024 Td /F1 9.8 Tf [(23.)] TJ ET
BT 43.553 244.024 Td /F1 9.8 Tf [(Khurana, A. and Rosenthal, S. R. \(1997\). “Integrating the fuzzy front end of new product development,” Sloan Management )] TJ ET
BT 26.250 232.119 Td /F1 9.8 Tf [(Review, ISSN 1532-9194, \(Winter\): 103-120.)] TJ ET
BT 26.250 212.715 Td /F1 9.8 Tf [(24.)] TJ ET
BT 43.553 212.715 Td /F1 9.8 Tf [(Kim, J. and Wilemon, D. \(2002\). “Focusing the fuzzy front-end in new product development,” R&D Management, ISSN 0033-)] TJ ET
BT 26.250 200.810 Td /F1 9.8 Tf [(6807, 32\(4\): 269-279.)] TJ ET
BT 26.250 181.405 Td /F1 9.8 Tf [(25.)] TJ ET
BT 43.553 181.405 Td /F1 9.8 Tf [(Kleijnen, J. P. C., Cheng, R. C. H. and Bettonvil, B. \(2001\). “Validation of trace-driven simulation models: Bootstrap tests,” )] TJ ET
BT 26.250 169.500 Td /F1 9.8 Tf [(Management Science, ISSN 0025-1909, 47\(11\): 1533-1538.)] TJ ET
BT 26.250 150.096 Td /F1 9.8 Tf [(26.)] TJ ET
BT 43.553 150.096 Td /F1 9.8 Tf [(Krishnan, V., Eppinger, S. D. and Whitney, D. \(1997\). “A model based framework to overlap product development activities,” )] TJ ET
BT 26.250 138.191 Td /F1 9.8 Tf [(Management Science, ISSN 0025-1909, 43\(4\): 437-451.)] TJ ET
BT 26.250 118.786 Td /F1 9.8 Tf [(27.)] TJ ET
BT 43.553 118.786 Td /F1 9.8 Tf [(Lesko, L. J., Rowland, M., Peck, C. C. and Blaschke, T. F. \(2000\). “Optimizing the science of drug development: )] TJ ET
BT 26.250 106.881 Td /F1 9.8 Tf [(Opportunities for better candidate selection and accelerated evaluation in humans,” Pharmaceutical Research, ISSN 0724-)] TJ ET
BT 26.250 94.977 Td /F1 9.8 Tf [(8741, 17\(11\): 1335-1344.)] TJ ET
BT 26.250 75.572 Td /F1 9.8 Tf [(28.)] TJ ET
BT 43.553 75.572 Td /F1 9.8 Tf [(Loch, C. H. and Terwiesch, C. \(1998\). “Communication and uncertainty in concurrent engineering,” Management Science, )] TJ ET
BT 26.250 63.667 Td /F1 9.8 Tf [(ISSN 0025-1909, 44\(8\): 1032-1048.)] TJ ET
BT 26.250 44.262 Td /F1 9.8 Tf [(29.)] TJ ET
BT 43.553 44.262 Td /F1 9.8 Tf [(Loch, C. H., Terwiesch, C. and Thomke, S. \(2001\). “Parallel and sequential testing of design alternatives,” Management )] TJ ET
Q
q
15.000 29.977 577.500 747.023 re W n
0.271 0.267 0.267 rg
BT 26.250 759.976 Td /F1 9.8 Tf [(5.)] TJ ET
BT 38.132 759.976 Td /F1 9.8 Tf [(Abernathy, W. and Rosenbloom, R. \(1968\). “Parallel and sequential R&D strategies: Application of a simple model,” IEEE )] TJ ET
BT 26.250 748.071 Td /F1 9.8 Tf [(Transactions on Engineering Management, ISSN 0018-9391, 15\(1\): 2-10.)] TJ ET
BT 26.250 728.667 Td /F1 9.8 Tf [(6.)] TJ ET
BT 38.132 728.667 Td /F1 9.8 Tf [(Bacon, G., Beckman, S., Mowery, D. and Wilson, E. \(1994\). “Managing product definition in high-technology industries: A )] TJ ET
BT 26.250 716.762 Td /F1 9.8 Tf [(pilot study,” California Management Review, ISSN 0008-1256, 36: 32-56.)] TJ ET
BT 26.250 697.357 Td /F1 9.8 Tf [(7.)] TJ ET
BT 38.132 697.357 Td /F1 9.8 Tf [(Callan, R. \(2003\). Artificial Intelligence, New York, NY: Palgrave Macmillan, ISBN 0333801369.)] TJ ET
BT 26.250 677.952 Td /F1 9.8 Tf [(8.)] TJ ET
BT 38.132 677.952 Td /F1 9.8 Tf [(Clark, K. B. and Wheelwright, S. C. \(1993\). Managing New Product and Process Development: Text and Cases, New York, )] TJ ET
BT 26.250 666.048 Td /F1 9.8 Tf [(NY: The Free Press, ISBN 0029055172 \(1992\).)] TJ ET
BT 26.250 646.643 Td /F1 9.8 Tf [(9.)] TJ ET
BT 38.132 646.643 Td /F1 9.8 Tf [(Cohen, M. D., March, J. G. and Olsen, J. P. \(1972\). “A garbage can model of organizational choice,” Administrative Science )] TJ ET
BT 26.250 634.738 Td /F1 9.8 Tf [(Quarterly, ISSN 0001-8392, 17\(1\): 1-25.)] TJ ET
BT 26.250 615.333 Td /F1 9.8 Tf [(10.)] TJ ET
BT 43.553 615.333 Td /F1 9.8 Tf [(Congdon, P. \(2001\) Bayesian Statistical Modelling, Chichester, England: John Wiley & Sons, ISBN 0471496006.)] TJ ET
BT 26.250 595.929 Td /F1 9.8 Tf [(11.)] TJ ET
BT 43.553 595.929 Td /F1 9.8 Tf [(Dahan, E. and Mendelson, H. \(2001\). “An extreme-value model of concept testing,” Management Science, ISSN 0025-)] TJ ET
BT 26.250 584.024 Td /F1 9.8 Tf [(1909, 47\(1\): 102-116.)] TJ ET
BT 26.250 564.619 Td /F1 9.8 Tf [(12.)] TJ ET
BT 43.553 564.619 Td /F1 9.8 Tf [(DeWitte, R. S. \(2002\). “On experimental design in drug discovery,” Current Drug Discovery, ISSN 1570-1638, \(February\): )] TJ ET
BT 26.250 552.714 Td /F1 9.8 Tf [(19-22.)] TJ ET
BT 26.250 533.310 Td /F1 9.8 Tf [(13.)] TJ ET
BT 43.553 533.310 Td /F1 9.8 Tf [(Drews, J. \(1998\). “Innovation deficit revisited: Reflections on the productivity of pharmaceutical R&D, ” Drug Discovery )] TJ ET
BT 26.250 521.405 Td /F1 9.8 Tf [(Today, ISSN 1359-6446, 3: 491-494.)] TJ ET
BT 26.250 502.000 Td /F1 9.8 Tf [(14.)] TJ ET
BT 43.553 502.000 Td /F1 9.8 Tf [(Duyck, J. \(2003\). “Attrition and translation,” Science, ISSN 0036-8075, 302\(0ctober 24\): 603-605.)] TJ ET
BT 26.250 482.595 Td /F1 9.8 Tf [(15.)] TJ ET
BT 43.553 482.595 Td /F1 9.8 Tf [(Furness, L. M. \(2003\). “Genomics applications that facilitate the understanding of drug action and toxicity,” in J. Licinio and )] TJ ET
BT 26.250 470.691 Td /F1 9.8 Tf [(M.-L. Wong \(eds.\), Pharmacogenomics: The Search for Individualized Therapies, Weinheim, Germany: Wiley-VCH, ISBN )] TJ ET
BT 26.250 458.786 Td /F1 9.8 Tf [(3527303804.)] TJ ET
BT 26.250 439.381 Td /F1 9.8 Tf [(16.)] TJ ET
BT 43.553 439.381 Td /F1 9.8 Tf [(Gerwin, D. and Barrowman, N. J. \(2002\). “An evaluation of research on integrated product development,” Management )] TJ ET
BT 26.250 427.476 Td /F1 9.8 Tf [(Science, ISSN 0025-1909, 48\(7\): 938-953.)] TJ ET
BT 26.250 408.072 Td /F1 9.8 Tf [(17.)] TJ ET
BT 43.553 408.072 Td /F1 9.8 Tf [(Golder, P. N. and Tellis, G. J. \(2004\). “Growing, growing, gone: Cascades, diffusion, and turning points in the product life )] TJ ET
BT 26.250 396.167 Td /F1 9.8 Tf [(cycle,” Marketing Science, ISSN 0732-2399, 23\(2\): 207-218.)] TJ ET
BT 26.250 376.762 Td /F1 9.8 Tf [(18.)] TJ ET
BT 43.553 376.762 Td /F1 9.8 Tf [(Holland, J. \(1992\). Adaptation in Natural and Artificial Sy s t e m s , C a m b r i d g e , M A : M I T P r e s s , I S B N )] TJ ET
BT 26.250 364.857 Td /F1 9.8 Tf [(0195088166.)] TJ ET
BT 26.250 345.453 Td /F1 9.8 Tf [(19.)] TJ ET
BT 43.553 345.453 Td /F1 9.8 Tf [(Holland, J. H. \(1998\). Emergence: From Chaos to Order, Oxford, England: Oxford University Press, ISBN 0198504098.)] TJ ET
BT 26.250 326.048 Td /F1 9.8 Tf [(20.)] TJ ET
BT 43.553 326.048 Td /F1 9.8 Tf [(Iansiti, M. \(1998\). Technology Integration: Making Critical Choices in a Dynamic World , Boston, MA: Harvard Business )] TJ ET
BT 26.250 314.143 Td /F1 9.8 Tf [(School Press, ISBN 0875847870.)] TJ ET
BT 26.250 294.738 Td /F1 9.8 Tf [(21.)] TJ ET
BT 43.553 294.738 Td /F1 9.8 Tf [(Jensen, F. V. \(2001\). Bayesian Networks and Decision Graphs, New York, NY: Springer-Verlag, ISBN 0387952594.)] TJ ET
BT 26.250 275.334 Td /F1 9.8 Tf [(22.)] TJ ET
BT 43.553 275.334 Td /F1 9.8 Tf [(Kennedy, T. \(1997\). “Managing the drug discovery/development interface,” Drug Discovery Today, ISSN 13596446, 2\(10\): )] TJ ET
BT 26.250 263.429 Td /F1 9.8 Tf [(436-444.)] TJ ET
BT 26.250 244.024 Td /F1 9.8 Tf [(23.)] TJ ET
BT 43.553 244.024 Td /F1 9.8 Tf [(Khurana, A. and Rosenthal, S. R. \(1997\). “Integrating the fuzzy front end of new product development,” Sloan Management )] TJ ET
BT 26.250 232.119 Td /F1 9.8 Tf [(Review, ISSN 1532-9194, \(Winter\): 103-120.)] TJ ET
BT 26.250 212.715 Td /F1 9.8 Tf [(24.)] TJ ET
BT 43.553 212.715 Td /F1 9.8 Tf [(Kim, J. and Wilemon, D. \(2002\). “Focusing the fuzzy front-end in new product development,” R&D Management, ISSN 0033-)] TJ ET
BT 26.250 200.810 Td /F1 9.8 Tf [(6807, 32\(4\): 269-279.)] TJ ET
BT 26.250 181.405 Td /F1 9.8 Tf [(25.)] TJ ET
BT 43.553 181.405 Td /F1 9.8 Tf [(Kleijnen, J. P. C., Cheng, R. C. H. and Bettonvil, B. \(2001\). “Validation of trace-driven simulation models: Bootstrap tests,” )] TJ ET
BT 26.250 169.500 Td /F1 9.8 Tf [(Management Science, ISSN 0025-1909, 47\(11\): 1533-1538.)] TJ ET
BT 26.250 150.096 Td /F1 9.8 Tf [(26.)] TJ ET
BT 43.553 150.096 Td /F1 9.8 Tf [(Krishnan, V., Eppinger, S. D. and Whitney, D. \(1997\). “A model based framework to overlap product development activities,” )] TJ ET
BT 26.250 138.191 Td /F1 9.8 Tf [(Management Science, ISSN 0025-1909, 43\(4\): 437-451.)] TJ ET
BT 26.250 118.786 Td /F1 9.8 Tf [(27.)] TJ ET
BT 43.553 118.786 Td /F1 9.8 Tf [(Lesko, L. J., Rowland, M., Peck, C. C. and Blaschke, T. F. \(2000\). “Optimizing the science of drug development: )] TJ ET
BT 26.250 106.881 Td /F1 9.8 Tf [(Opportunities for better candidate selection and accelerated evaluation in humans,” Pharmaceutical Research, ISSN 0724-)] TJ ET
BT 26.250 94.977 Td /F1 9.8 Tf [(8741, 17\(11\): 1335-1344.)] TJ ET
BT 26.250 75.572 Td /F1 9.8 Tf [(28.)] TJ ET
BT 43.553 75.572 Td /F1 9.8 Tf [(Loch, C. H. and Terwiesch, C. \(1998\). “Communication and uncertainty in concurrent engineering,” Management Science, )] TJ ET
BT 26.250 63.667 Td /F1 9.8 Tf [(ISSN 0025-1909, 44\(8\): 1032-1048.)] TJ ET
BT 26.250 44.262 Td /F1 9.8 Tf [(29.)] TJ ET
BT 43.553 44.262 Td /F1 9.8 Tf [(Loch, C. H., Terwiesch, C. and Thomke, S. \(2001\). “Parallel and sequential testing of design alternatives,” Management )] TJ ET
Q
q
0.000 0.000 0.000 rg
BT 291.710 19.825 Td /F1 11.0 Tf [(12)] TJ ET
BT 25.000 19.825 Td /F1 11.0 Tf [(Emergence: Complexity and Organization)] TJ ET
Q
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15.000 41.881 577.500 735.119 re W n
0.271 0.267 0.267 rg
BT 26.250 767.476 Td /F1 9.8 Tf [(Science, ISSN 0025-1909, 47\(5\): 663-678.)] TJ ET
BT 26.250 748.071 Td /F1 9.8 Tf [(30.)] TJ ET
BT 43.553 748.071 Td /F1 9.8 Tf [(MacCormack, A. and Verganti, R. \(2003\). “Managing the sources of uncertainty: Matching process and context in software )] TJ ET
BT 26.250 736.167 Td /F1 9.8 Tf [(development,” Journal of Product Innovation Management, ISSN 0737-6782, 20: 217-232.)] TJ ET
BT 26.250 716.762 Td /F1 9.8 Tf [(31.)] TJ ET
BT 43.553 716.762 Td /F1 9.8 Tf [(Macdonald, S. J. F. and Smith, P. W. \(2001\). “Lead optimization in 12 months? True confessions of a chemistry team,” Drug )] TJ ET
BT 26.250 704.857 Td /F1 9.8 Tf [(Discovery Today, ISSN 1359-6446, 6\(18\): 947-953.)] TJ ET
BT 26.250 685.452 Td /F1 9.8 Tf [(32.)] TJ ET
BT 43.553 685.452 Td /F1 9.8 Tf [(Masuch, M. and Lapotin, P. \(1989\). “Beyond garbage cans: An AI model of organizational choice,” Administrative Science )] TJ ET
BT 26.250 673.548 Td /F1 9.8 Tf [(Quarterly, ISSN 0001-8392, 34\(1\): 38-67.)] TJ ET
BT 26.250 654.143 Td /F1 9.8 Tf [(33.)] TJ ET
BT 43.553 654.143 Td /F1 9.8 Tf [(McCarthy, I. P., Tsinopoulos, C., Allen, P. M. and Rose- Anderssen, C. \(2006\). “New product development as a complex )] TJ ET
BT 26.250 642.238 Td /F1 9.8 Tf [(adaptive system of decisions,” Journal of Product Innovation Management, ISSN 1532-9194, Forthcoming.)] TJ ET
BT 26.250 622.833 Td /F1 9.8 Tf [(34.)] TJ ET
BT 43.553 622.833 Td /F1 9.8 Tf [(Mena, C., Thomson, A. and Jeffrey, P. \(2001\). “HS Marston report: An evolutionary model of the product design process,” )] TJ ET
BT 26.250 610.929 Td /F1 9.8 Tf [(Innovative Manufacturing Initiative: The complexities of product definition, unpublished report.)] TJ ET
BT 26.250 591.524 Td /F1 9.8 Tf [(35.)] TJ ET
BT 43.553 591.524 Td /F1 9.8 Tf [(Mihm, J., Loch, C. H. and Huchzermeier, A. \(2003\). “Problem-solving oscillations in complex engineering projects,” )] TJ ET
BT 26.250 579.619 Td /F1 9.8 Tf [(Management Science, ISSN 0025-1909, 46\(6\): 733-750.)] TJ ET
BT 26.250 560.214 Td /F1 9.8 Tf [(36.)] TJ ET
BT 43.553 560.214 Td /F1 9.8 Tf [(Mitchell, M. \(2001\). An Introduction to Genetic Algorithms, Cambridge, MA: MIT Press, ISBN 0521648211.)] TJ ET
BT 26.250 540.810 Td /F1 9.8 Tf [(37.)] TJ ET
BT 43.553 540.810 Td /F1 9.8 Tf [(Müller, P. \(1999\). “Simulation-based optimal design,” in J. M. Bernardo, J. O. Berger, A. P. David and A. F. M. Smith \(eds.\), )] TJ ET
BT 26.250 528.905 Td /F1 9.8 Tf [(Bayesian Statistics 6, Oxford, England: Oxford University Press, ISBN 0198504853, pp. 459-474.)] TJ ET
BT 26.250 509.500 Td /F1 9.8 Tf [(38.)] TJ ET
BT 43.553 509.500 Td /F1 9.8 Tf [(Newell, A. and Simon, H. A. \(1976\). “Computer science as empirical enquiry : Symbols and search,” Communications of the )] TJ ET
BT 26.250 497.595 Td /F1 9.8 Tf [(ACM, ISSN 0001-0782, 19\(3\): 113-126.)] TJ ET
BT 26.250 478.191 Td /F1 9.8 Tf [(39.)] TJ ET
BT 43.553 478.191 Td /F1 9.8 Tf [(Oprea, T. I. \(2002\). “Virtual screening in lead discovery: A viewpoint,” Molecules, ISSN 1420-3049, 7: 51-62.)] TJ ET
BT 26.250 458.786 Td /F1 9.8 Tf [(40.)] TJ ET
BT 43.553 458.786 Td /F1 9.8 Tf [(Oprea, T. I., Davis, A. M., Teague, S. J. and Leeson, P. D. \(2001\). “Is there a difference between leads and drugs? A )] TJ ET
BT 26.250 446.881 Td /F1 9.8 Tf [(historical perspective,” Journal of Chemical Information and Computer Sciences, ISSN 0095-2338, 41: 1308-1315.)] TJ ET
BT 26.250 427.476 Td /F1 9.8 Tf [(41.)] TJ ET
BT 43.553 427.476 Td /F1 9.8 Tf [(Pacl, H., Festel, G. and Wess, G. \(2004\). The Future of Pharma R&D, Huenenberg: Festel Capital, ISBN 3-00-014012-3)] TJ ET
BT 26.250 408.072 Td /F1 9.8 Tf [(42.)] TJ ET
BT 43.553 408.072 Td /F1 9.8 Tf [(Parmigiani, G. \(2002\). Modeling in Medical Decision Making: A Bayesian Approach, Chichester, England: John Wiley & )] TJ ET
BT 26.250 396.167 Td /F1 9.8 Tf [(Sons, ISBN 0471986089.)] TJ ET
BT 26.250 376.762 Td /F1 9.8 Tf [(43.)] TJ ET
BT 43.553 376.762 Td /F1 9.8 Tf [(Pelikan, M. and Goldberg, D. E. \(2003\). “A hierarchy machine: learning to optimize from nature and humans,” Complexity, )] TJ ET
BT 26.250 364.857 Td /F1 9.8 Tf [(ISSN 1076-2787, 8\(5\): 36-45.)] TJ ET
BT 26.250 345.453 Td /F1 9.8 Tf [(44.)] TJ ET
BT 43.553 345.453 Td /F1 9.8 Tf [(Pickering, L. \(2001\). “ADME/Tox models can speed develoment,” Drug Discovery & Development, ISSN 1367-6733, )] TJ ET
BT 26.250 333.548 Td /F1 9.8 Tf [(\(January\): 34-38.)] TJ ET
BT 26.250 314.143 Td /F1 9.8 Tf [(45.)] TJ ET
BT 43.553 314.143 Td /F1 9.8 Tf [(Pidd, M. \(1992\). Computer Simulation in Management Science, 3rd edition, New York, NY: John Wiley & Sons, ISBN )] TJ ET
BT 26.250 302.238 Td /F1 9.8 Tf [(0470092300.)] TJ ET
BT 26.250 282.834 Td /F1 9.8 Tf [(46.)] TJ ET
BT 43.553 282.834 Td /F1 9.8 Tf [(Poole, M. S., Van de Ven, A. H., Dooley, K. and Holmes, M. E. \(2000\). Organizational Change and Innovation Processes, )] TJ ET
BT 26.250 270.929 Td /F1 9.8 Tf [(Oxford, England: Oxford University Press, ISBN 0195131983.)] TJ ET
BT 26.250 251.524 Td /F1 9.8 Tf [(47.)] TJ ET
BT 43.553 251.524 Td /F1 9.8 Tf [(Roemer, T. A., Ahmadi, R. H. and Wang, R. H. \(2000\). “Time-cost trade-offs in overlapped product development,” )] TJ ET
BT 26.250 239.619 Td /F1 9.8 Tf [(Operations Research, ISSN 0030-364X, 48\(6\): 858-865.)] TJ ET
BT 26.250 220.215 Td /F1 9.8 Tf [(48.)] TJ ET
BT 43.553 220.215 Td /F1 9.8 Tf [(Rumelhart, D. E., McClelland, J. L. and the PDP Research Group \(1986\). Parallel Distributed Processes: Explorations in the )] TJ ET
BT 26.250 208.310 Td /F1 9.8 Tf [(Microstructure of Cognition. Volume 1: Foundations, Cambridge, MA: Bradford Books, ISBN 0262631105.)] TJ ET
BT 26.250 188.905 Td /F1 9.8 Tf [(49.)] TJ ET
BT 43.553 188.905 Td /F1 9.8 Tf [(Schrader, S., Riggs, W. M. and Smith, R. P. \(1992\). “Choice over uncertainty and ambiguity in technical problem solving,” )] TJ ET
BT 26.250 177.000 Td /F1 9.8 Tf [(Journal of Engineering and Technology , ISSN 0923-4748, 10\(1\): 73-99.)] TJ ET
BT 26.250 157.596 Td /F1 9.8 Tf [(50.)] TJ ET
BT 43.553 157.596 Td /F1 9.8 Tf [(Smith, R. and Eppinger, S. D. \(1997a\). “A prediction model of sequential iteration in engineering design,” Management )] TJ ET
BT 26.250 145.691 Td /F1 9.8 Tf [(Science, ISSN 0025-1909, 43\(8\): 1104-1120.)] TJ ET
BT 26.250 126.286 Td /F1 9.8 Tf [(51.)] TJ ET
BT 43.553 126.286 Td /F1 9.8 Tf [(Smith, R. P. and Eppinger, S. D. \(1997b\). “Identifying controlling features of engineering design iterations,” Management )] TJ ET
BT 26.250 114.381 Td /F1 9.8 Tf [(Science, ISSN 0025-1909, 43\(3\): 276-293.)] TJ ET
BT 26.250 94.977 Td /F1 9.8 Tf [(52.)] TJ ET
BT 43.553 94.977 Td /F1 9.8 Tf [(Sobek II, D. K., Ward, A. C. and Liker, J. K. \(1999\). “Toyota’s principles of set-based concurrent engineering,” Sloan )] TJ ET
BT 26.250 83.072 Td /F1 9.8 Tf [(Management Review, ISSN 1532-9194, \(Winter\): 67-83.)] TJ ET
BT 26.250 63.667 Td /F1 9.8 Tf [(53.)] TJ ET
BT 43.553 63.667 Td /F1 9.8 Tf [(Terwiesch, C., Loch, C. H. and De Meyer, A. \(2002\). “Exchanging preliminary information in concurrent engineering: )] TJ ET
BT 26.250 51.762 Td /F1 9.8 Tf [(Alternative coordination strategies,” Organization Science, ISSN 1047-7039, 13\(4\): 402-419.)] TJ ET
Q
q
15.000 41.881 577.500 735.119 re W n
0.271 0.267 0.267 rg
BT 26.250 767.476 Td /F1 9.8 Tf [(Science, ISSN 0025-1909, 47\(5\): 663-678.)] TJ ET
BT 26.250 748.071 Td /F1 9.8 Tf [(30.)] TJ ET
BT 43.553 748.071 Td /F1 9.8 Tf [(MacCormack, A. and Verganti, R. \(2003\). “Managing the sources of uncertainty: Matching process and context in software )] TJ ET
BT 26.250 736.167 Td /F1 9.8 Tf [(development,” Journal of Product Innovation Management, ISSN 0737-6782, 20: 217-232.)] TJ ET
BT 26.250 716.762 Td /F1 9.8 Tf [(31.)] TJ ET
BT 43.553 716.762 Td /F1 9.8 Tf [(Macdonald, S. J. F. and Smith, P. W. \(2001\). “Lead optimization in 12 months? True confessions of a chemistry team,” Drug )] TJ ET
BT 26.250 704.857 Td /F1 9.8 Tf [(Discovery Today, ISSN 1359-6446, 6\(18\): 947-953.)] TJ ET
BT 26.250 685.452 Td /F1 9.8 Tf [(32.)] TJ ET
BT 43.553 685.452 Td /F1 9.8 Tf [(Masuch, M. and Lapotin, P. \(1989\). “Beyond garbage cans: An AI model of organizational choice,” Administrative Science )] TJ ET
BT 26.250 673.548 Td /F1 9.8 Tf [(Quarterly, ISSN 0001-8392, 34\(1\): 38-67.)] TJ ET
BT 26.250 654.143 Td /F1 9.8 Tf [(33.)] TJ ET
BT 43.553 654.143 Td /F1 9.8 Tf [(McCarthy, I. P., Tsinopoulos, C., Allen, P. M. and Rose- Anderssen, C. \(2006\). “New product development as a complex )] TJ ET
BT 26.250 642.238 Td /F1 9.8 Tf [(adaptive system of decisions,” Journal of Product Innovation Management, ISSN 1532-9194, Forthcoming.)] TJ ET
BT 26.250 622.833 Td /F1 9.8 Tf [(34.)] TJ ET
BT 43.553 622.833 Td /F1 9.8 Tf [(Mena, C., Thomson, A. and Jeffrey, P. \(2001\). “HS Marston report: An evolutionary model of the product design process,” )] TJ ET
BT 26.250 610.929 Td /F1 9.8 Tf [(Innovative Manufacturing Initiative: The complexities of product definition, unpublished report.)] TJ ET
BT 26.250 591.524 Td /F1 9.8 Tf [(35.)] TJ ET
BT 43.553 591.524 Td /F1 9.8 Tf [(Mihm, J., Loch, C. H. and Huchzermeier, A. \(2003\). “Problem-solving oscillations in complex engineering projects,” )] TJ ET
BT 26.250 579.619 Td /F1 9.8 Tf [(Management Science, ISSN 0025-1909, 46\(6\): 733-750.)] TJ ET
BT 26.250 560.214 Td /F1 9.8 Tf [(36.)] TJ ET
BT 43.553 560.214 Td /F1 9.8 Tf [(Mitchell, M. \(2001\). An Introduction to Genetic Algorithms, Cambridge, MA: MIT Press, ISBN 0521648211.)] TJ ET
BT 26.250 540.810 Td /F1 9.8 Tf [(37.)] TJ ET
BT 43.553 540.810 Td /F1 9.8 Tf [(Müller, P. \(1999\). “Simulation-based optimal design,” in J. M. Bernardo, J. O. Berger, A. P. David and A. F. M. Smith \(eds.\), )] TJ ET
BT 26.250 528.905 Td /F1 9.8 Tf [(Bayesian Statistics 6, Oxford, England: Oxford University Press, ISBN 0198504853, pp. 459-474.)] TJ ET
BT 26.250 509.500 Td /F1 9.8 Tf [(38.)] TJ ET
BT 43.553 509.500 Td /F1 9.8 Tf [(Newell, A. and Simon, H. A. \(1976\). “Computer science as empirical enquiry : Symbols and search,” Communications of the )] TJ ET
BT 26.250 497.595 Td /F1 9.8 Tf [(ACM, ISSN 0001-0782, 19\(3\): 113-126.)] TJ ET
BT 26.250 478.191 Td /F1 9.8 Tf [(39.)] TJ ET
BT 43.553 478.191 Td /F1 9.8 Tf [(Oprea, T. I. \(2002\). “Virtual screening in lead discovery: A viewpoint,” Molecules, ISSN 1420-3049, 7: 51-62.)] TJ ET
BT 26.250 458.786 Td /F1 9.8 Tf [(40.)] TJ ET
BT 43.553 458.786 Td /F1 9.8 Tf [(Oprea, T. I., Davis, A. M., Teague, S. J. and Leeson, P. D. \(2001\). “Is there a difference between leads and drugs? A )] TJ ET
BT 26.250 446.881 Td /F1 9.8 Tf [(historical perspective,” Journal of Chemical Information and Computer Sciences, ISSN 0095-2338, 41: 1308-1315.)] TJ ET
BT 26.250 427.476 Td /F1 9.8 Tf [(41.)] TJ ET
BT 43.553 427.476 Td /F1 9.8 Tf [(Pacl, H., Festel, G. and Wess, G. \(2004\). The Future of Pharma R&D, Huenenberg: Festel Capital, ISBN 3-00-014012-3)] TJ ET
BT 26.250 408.072 Td /F1 9.8 Tf [(42.)] TJ ET
BT 43.553 408.072 Td /F1 9.8 Tf [(Parmigiani, G. \(2002\). Modeling in Medical Decision Making: A Bayesian Approach, Chichester, England: John Wiley & )] TJ ET
BT 26.250 396.167 Td /F1 9.8 Tf [(Sons, ISBN 0471986089.)] TJ ET
BT 26.250 376.762 Td /F1 9.8 Tf [(43.)] TJ ET
BT 43.553 376.762 Td /F1 9.8 Tf [(Pelikan, M. and Goldberg, D. E. \(2003\). “A hierarchy machine: learning to optimize from nature and humans,” Complexity, )] TJ ET
BT 26.250 364.857 Td /F1 9.8 Tf [(ISSN 1076-2787, 8\(5\): 36-45.)] TJ ET
BT 26.250 345.453 Td /F1 9.8 Tf [(44.)] TJ ET
BT 43.553 345.453 Td /F1 9.8 Tf [(Pickering, L. \(2001\). “ADME/Tox models can speed develoment,” Drug Discovery & Development, ISSN 1367-6733, )] TJ ET
BT 26.250 333.548 Td /F1 9.8 Tf [(\(January\): 34-38.)] TJ ET
BT 26.250 314.143 Td /F1 9.8 Tf [(45.)] TJ ET
BT 43.553 314.143 Td /F1 9.8 Tf [(Pidd, M. \(1992\). Computer Simulation in Management Science, 3rd edition, New York, NY: John Wiley & Sons, ISBN )] TJ ET
BT 26.250 302.238 Td /F1 9.8 Tf [(0470092300.)] TJ ET
BT 26.250 282.834 Td /F1 9.8 Tf [(46.)] TJ ET
BT 43.553 282.834 Td /F1 9.8 Tf [(Poole, M. S., Van de Ven, A. H., Dooley, K. and Holmes, M. E. \(2000\). Organizational Change and Innovation Processes, )] TJ ET
BT 26.250 270.929 Td /F1 9.8 Tf [(Oxford, England: Oxford University Press, ISBN 0195131983.)] TJ ET
BT 26.250 251.524 Td /F1 9.8 Tf [(47.)] TJ ET
BT 43.553 251.524 Td /F1 9.8 Tf [(Roemer, T. A., Ahmadi, R. H. and Wang, R. H. \(2000\). “Time-cost trade-offs in overlapped product development,” )] TJ ET
BT 26.250 239.619 Td /F1 9.8 Tf [(Operations Research, ISSN 0030-364X, 48\(6\): 858-865.)] TJ ET
BT 26.250 220.215 Td /F1 9.8 Tf [(48.)] TJ ET
BT 43.553 220.215 Td /F1 9.8 Tf [(Rumelhart, D. E., McClelland, J. L. and the PDP Research Group \(1986\). Parallel Distributed Processes: Explorations in the )] TJ ET
BT 26.250 208.310 Td /F1 9.8 Tf [(Microstructure of Cognition. Volume 1: Foundations, Cambridge, MA: Bradford Books, ISBN 0262631105.)] TJ ET
BT 26.250 188.905 Td /F1 9.8 Tf [(49.)] TJ ET
BT 43.553 188.905 Td /F1 9.8 Tf [(Schrader, S., Riggs, W. M. and Smith, R. P. \(1992\). “Choice over uncertainty and ambiguity in technical problem solving,” )] TJ ET
BT 26.250 177.000 Td /F1 9.8 Tf [(Journal of Engineering and Technology , ISSN 0923-4748, 10\(1\): 73-99.)] TJ ET
BT 26.250 157.596 Td /F1 9.8 Tf [(50.)] TJ ET
BT 43.553 157.596 Td /F1 9.8 Tf [(Smith, R. and Eppinger, S. D. \(1997a\). “A prediction model of sequential iteration in engineering design,” Management )] TJ ET
BT 26.250 145.691 Td /F1 9.8 Tf [(Science, ISSN 0025-1909, 43\(8\): 1104-1120.)] TJ ET
BT 26.250 126.286 Td /F1 9.8 Tf [(51.)] TJ ET
BT 43.553 126.286 Td /F1 9.8 Tf [(Smith, R. P. and Eppinger, S. D. \(1997b\). “Identifying controlling features of engineering design iterations,” Management )] TJ ET
BT 26.250 114.381 Td /F1 9.8 Tf [(Science, ISSN 0025-1909, 43\(3\): 276-293.)] TJ ET
BT 26.250 94.977 Td /F1 9.8 Tf [(52.)] TJ ET
BT 43.553 94.977 Td /F1 9.8 Tf [(Sobek II, D. K., Ward, A. C. and Liker, J. K. \(1999\). “Toyota’s principles of set-based concurrent engineering,” Sloan )] TJ ET
BT 26.250 83.072 Td /F1 9.8 Tf [(Management Review, ISSN 1532-9194, \(Winter\): 67-83.)] TJ ET
BT 26.250 63.667 Td /F1 9.8 Tf [(53.)] TJ ET
BT 43.553 63.667 Td /F1 9.8 Tf [(Terwiesch, C., Loch, C. H. and De Meyer, A. \(2002\). “Exchanging preliminary information in concurrent engineering: )] TJ ET
BT 26.250 51.762 Td /F1 9.8 Tf [(Alternative coordination strategies,” Organization Science, ISSN 1047-7039, 13\(4\): 402-419.)] TJ ET
Q
q
15.000 41.881 577.500 735.119 re W n
0.271 0.267 0.267 rg
BT 26.250 767.476 Td /F1 9.8 Tf [(Science, ISSN 0025-1909, 47\(5\): 663-678.)] TJ ET
BT 26.250 748.071 Td /F1 9.8 Tf [(30.)] TJ ET
BT 43.553 748.071 Td /F1 9.8 Tf [(MacCormack, A. and Verganti, R. \(2003\). “Managing the sources of uncertainty: Matching process and context in software )] TJ ET
BT 26.250 736.167 Td /F1 9.8 Tf [(development,” Journal of Product Innovation Management, ISSN 0737-6782, 20: 217-232.)] TJ ET
BT 26.250 716.762 Td /F1 9.8 Tf [(31.)] TJ ET
BT 43.553 716.762 Td /F1 9.8 Tf [(Macdonald, S. J. F. and Smith, P. W. \(2001\). “Lead optimization in 12 months? True confessions of a chemistry team,” Drug )] TJ ET
BT 26.250 704.857 Td /F1 9.8 Tf [(Discovery Today, ISSN 1359-6446, 6\(18\): 947-953.)] TJ ET
BT 26.250 685.452 Td /F1 9.8 Tf [(32.)] TJ ET
BT 43.553 685.452 Td /F1 9.8 Tf [(Masuch, M. and Lapotin, P. \(1989\). “Beyond garbage cans: An AI model of organizational choice,” Administrative Science )] TJ ET
BT 26.250 673.548 Td /F1 9.8 Tf [(Quarterly, ISSN 0001-8392, 34\(1\): 38-67.)] TJ ET
BT 26.250 654.143 Td /F1 9.8 Tf [(33.)] TJ ET
BT 43.553 654.143 Td /F1 9.8 Tf [(McCarthy, I. P., Tsinopoulos, C., Allen, P. M. and Rose- Anderssen, C. \(2006\). “New product development as a complex )] TJ ET
BT 26.250 642.238 Td /F1 9.8 Tf [(adaptive system of decisions,” Journal of Product Innovation Management, ISSN 1532-9194, Forthcoming.)] TJ ET
BT 26.250 622.833 Td /F1 9.8 Tf [(34.)] TJ ET
BT 43.553 622.833 Td /F1 9.8 Tf [(Mena, C., Thomson, A. and Jeffrey, P. \(2001\). “HS Marston report: An evolutionary model of the product design process,” )] TJ ET
BT 26.250 610.929 Td /F1 9.8 Tf [(Innovative Manufacturing Initiative: The complexities of product definition, unpublished report.)] TJ ET
BT 26.250 591.524 Td /F1 9.8 Tf [(35.)] TJ ET
BT 43.553 591.524 Td /F1 9.8 Tf [(Mihm, J., Loch, C. H. and Huchzermeier, A. \(2003\). “Problem-solving oscillations in complex engineering projects,” )] TJ ET
BT 26.250 579.619 Td /F1 9.8 Tf [(Management Science, ISSN 0025-1909, 46\(6\): 733-750.)] TJ ET
BT 26.250 560.214 Td /F1 9.8 Tf [(36.)] TJ ET
BT 43.553 560.214 Td /F1 9.8 Tf [(Mitchell, M. \(2001\). An Introduction to Genetic Algorithms, Cambridge, MA: MIT Press, ISBN 0521648211.)] TJ ET
BT 26.250 540.810 Td /F1 9.8 Tf [(37.)] TJ ET
BT 43.553 540.810 Td /F1 9.8 Tf [(Müller, P. \(1999\). “Simulation-based optimal design,” in J. M. Bernardo, J. O. Berger, A. P. David and A. F. M. Smith \(eds.\), )] TJ ET
BT 26.250 528.905 Td /F1 9.8 Tf [(Bayesian Statistics 6, Oxford, England: Oxford University Press, ISBN 0198504853, pp. 459-474.)] TJ ET
BT 26.250 509.500 Td /F1 9.8 Tf [(38.)] TJ ET
BT 43.553 509.500 Td /F1 9.8 Tf [(Newell, A. and Simon, H. A. \(1976\). “Computer science as empirical enquiry : Symbols and search,” Communications of the )] TJ ET
BT 26.250 497.595 Td /F1 9.8 Tf [(ACM, ISSN 0001-0782, 19\(3\): 113-126.)] TJ ET
BT 26.250 478.191 Td /F1 9.8 Tf [(39.)] TJ ET
BT 43.553 478.191 Td /F1 9.8 Tf [(Oprea, T. I. \(2002\). “Virtual screening in lead discovery: A viewpoint,” Molecules, ISSN 1420-3049, 7: 51-62.)] TJ ET
BT 26.250 458.786 Td /F1 9.8 Tf [(40.)] TJ ET
BT 43.553 458.786 Td /F1 9.8 Tf [(Oprea, T. I., Davis, A. M., Teague, S. J. and Leeson, P. D. \(2001\). “Is there a difference between leads and drugs? A )] TJ ET
BT 26.250 446.881 Td /F1 9.8 Tf [(historical perspective,” Journal of Chemical Information and Computer Sciences, ISSN 0095-2338, 41: 1308-1315.)] TJ ET
BT 26.250 427.476 Td /F1 9.8 Tf [(41.)] TJ ET
BT 43.553 427.476 Td /F1 9.8 Tf [(Pacl, H., Festel, G. and Wess, G. \(2004\). The Future of Pharma R&D, Huenenberg: Festel Capital, ISBN 3-00-014012-3)] TJ ET
BT 26.250 408.072 Td /F1 9.8 Tf [(42.)] TJ ET
BT 43.553 408.072 Td /F1 9.8 Tf [(Parmigiani, G. \(2002\). Modeling in Medical Decision Making: A Bayesian Approach, Chichester, England: John Wiley & )] TJ ET
BT 26.250 396.167 Td /F1 9.8 Tf [(Sons, ISBN 0471986089.)] TJ ET
BT 26.250 376.762 Td /F1 9.8 Tf [(43.)] TJ ET
BT 43.553 376.762 Td /F1 9.8 Tf [(Pelikan, M. and Goldberg, D. E. \(2003\). “A hierarchy machine: learning to optimize from nature and humans,” Complexity, )] TJ ET
BT 26.250 364.857 Td /F1 9.8 Tf [(ISSN 1076-2787, 8\(5\): 36-45.)] TJ ET
BT 26.250 345.453 Td /F1 9.8 Tf [(44.)] TJ ET
BT 43.553 345.453 Td /F1 9.8 Tf [(Pickering, L. \(2001\). “ADME/Tox models can speed develoment,” Drug Discovery & Development, ISSN 1367-6733, )] TJ ET
BT 26.250 333.548 Td /F1 9.8 Tf [(\(January\): 34-38.)] TJ ET
BT 26.250 314.143 Td /F1 9.8 Tf [(45.)] TJ ET
BT 43.553 314.143 Td /F1 9.8 Tf [(Pidd, M. \(1992\). Computer Simulation in Management Science, 3rd edition, New York, NY: John Wiley & Sons, ISBN )] TJ ET
BT 26.250 302.238 Td /F1 9.8 Tf [(0470092300.)] TJ ET
BT 26.250 282.834 Td /F1 9.8 Tf [(46.)] TJ ET
BT 43.553 282.834 Td /F1 9.8 Tf [(Poole, M. S., Van de Ven, A. H., Dooley, K. and Holmes, M. E. \(2000\). Organizational Change and Innovation Processes, )] TJ ET
BT 26.250 270.929 Td /F1 9.8 Tf [(Oxford, England: Oxford University Press, ISBN 0195131983.)] TJ ET
BT 26.250 251.524 Td /F1 9.8 Tf [(47.)] TJ ET
BT 43.553 251.524 Td /F1 9.8 Tf [(Roemer, T. A., Ahmadi, R. H. and Wang, R. H. \(2000\). “Time-cost trade-offs in overlapped product development,” )] TJ ET
BT 26.250 239.619 Td /F1 9.8 Tf [(Operations Research, ISSN 0030-364X, 48\(6\): 858-865.)] TJ ET
BT 26.250 220.215 Td /F1 9.8 Tf [(48.)] TJ ET
BT 43.553 220.215 Td /F1 9.8 Tf [(Rumelhart, D. E., McClelland, J. L. and the PDP Research Group \(1986\). Parallel Distributed Processes: Explorations in the )] TJ ET
BT 26.250 208.310 Td /F1 9.8 Tf [(Microstructure of Cognition. Volume 1: Foundations, Cambridge, MA: Bradford Books, ISBN 0262631105.)] TJ ET
BT 26.250 188.905 Td /F1 9.8 Tf [(49.)] TJ ET
BT 43.553 188.905 Td /F1 9.8 Tf [(Schrader, S., Riggs, W. M. and Smith, R. P. \(1992\). “Choice over uncertainty and ambiguity in technical problem solving,” )] TJ ET
BT 26.250 177.000 Td /F1 9.8 Tf [(Journal of Engineering and Technology , ISSN 0923-4748, 10\(1\): 73-99.)] TJ ET
BT 26.250 157.596 Td /F1 9.8 Tf [(50.)] TJ ET
BT 43.553 157.596 Td /F1 9.8 Tf [(Smith, R. and Eppinger, S. D. \(1997a\). “A prediction model of sequential iteration in engineering design,” Management )] TJ ET
BT 26.250 145.691 Td /F1 9.8 Tf [(Science, ISSN 0025-1909, 43\(8\): 1104-1120.)] TJ ET
BT 26.250 126.286 Td /F1 9.8 Tf [(51.)] TJ ET
BT 43.553 126.286 Td /F1 9.8 Tf [(Smith, R. P. and Eppinger, S. D. \(1997b\). “Identifying controlling features of engineering design iterations,” Management )] TJ ET
BT 26.250 114.381 Td /F1 9.8 Tf [(Science, ISSN 0025-1909, 43\(3\): 276-293.)] TJ ET
BT 26.250 94.977 Td /F1 9.8 Tf [(52.)] TJ ET
BT 43.553 94.977 Td /F1 9.8 Tf [(Sobek II, D. K., Ward, A. C. and Liker, J. K. \(1999\). “Toyota’s principles of set-based concurrent engineering,” Sloan )] TJ ET
BT 26.250 83.072 Td /F1 9.8 Tf [(Management Review, ISSN 1532-9194, \(Winter\): 67-83.)] TJ ET
BT 26.250 63.667 Td /F1 9.8 Tf [(53.)] TJ ET
BT 43.553 63.667 Td /F1 9.8 Tf [(Terwiesch, C., Loch, C. H. and De Meyer, A. \(2002\). “Exchanging preliminary information in concurrent engineering: )] TJ ET
BT 26.250 51.762 Td /F1 9.8 Tf [(Alternative coordination strategies,” Organization Science, ISSN 1047-7039, 13\(4\): 402-419.)] TJ ET
Q
q
0.000 0.000 0.000 rg
BT 291.710 19.825 Td /F1 11.0 Tf [(13)] TJ ET
BT 25.000 19.825 Td /F1 11.0 Tf [(Emergence: Complexity and Organization)] TJ ET
Q
endstream
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