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Empirical Essays on Global Pharmaceutical Innovation

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Empirical Essays on Global Pharmaceutical Innovation Imperial College London Business School Empirical Essays on Global Pharmaceutical Innovation Eliana Barrenho Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy of Imperial College London Abstract The economic impact of the pharmaceutical industry is incontestable. The to- tal drug bill across OECD countries has continuously risen over the last decades (OECD, 2011), and, consequently, healthcare payers are increasingly implement- ing stricter policies that promote access to cheaper treatments. These policies, however, may hinder incentives to innovate, especially in diseases of substantial public health importance for which there is either under-investment or where in- novation is difficult. Therefore, it is fundamental that health systems design policies that strike the right balance between promoting the development of affordable drugs, and allowing sufficient rents to innovators in order to incentivise R&D investment. The debate on this balance has never been more pertinent, with a slowdown in the number of drugs in the pipeline for potential market launch. Around 90% of drug candidates do not successfully complete the mid-stage of drug discovery (Paul et al., 2010; Mestre-Ferrandiz et al., 2012), contributing to the escalation of R&D costs with potentially significant social welfare consequences. The aim of this thesis is to contribute to this debate by exploring the nature of the R&D process, and assessing the factors associated with decreased productivity across disease areas and its equity implications. We survey the literature on the determinants of pharmaceutical innovation and critically appraise the evidence on factors that influence innovation of new therapies. We identify gaps and contribute conceptually to the understanding of the determinants of innovation. We depart from the existing literature in three significant ways. The major contribution is the analysis of failure for all stages of the R&D process, using a unique global panel dataset built by merging data on the lifecycle of industry innovation processes with global health data (Chapter 4). Secondly, we have used methodological approaches that model the dynamic nature of R&D decisions to forecast drug availability in the coming decades (Chapter 5). Thirdly, we are the first to assess the global impact of innovation on equity of access to new therapies (Chapter 6). Acknowledging the implications of data limitations (Chapter 3) we produce in- sight that contributes to understanding the determinants of failure in innovation and its implications for future availability of new therapies. Results suggest those 1 determinants differ across the R&D stages. Furthermore, market competition may intensify the level of failure if too many young drugs are competing in the market, whilst collaboration between firms has an unclear effect on innovation. Moreover, the distribution of the R&D activity and disease burden have not changed sig- nificantly over the last two decades with a concentration of innovation in more commercially attractive disease areas associated with high mortality in the richest countries. Finally, that those equity concerns are likely to persist unless new pol- icy interventions are designed to address inequalities in R&D and access to new therapies. 2 Contents 1 Introduction 15 2 The determinants of pharmaceutical innovation: a critical review of the literature 17 I Introduction . 17 II Method and scope of the review . 18 A Selection and inclusion criteria of the search . 19 B Results from the search . 20 III What determines R&D? An overview framework . 21 A Demand-side factors . 21 B Supply-side factors . 23 C Policy-shaping factors . 24 IV Conceptual and theoretical contributions . 24 A Demand-side factors . 25 B Supply-side factors . 25 C Policy-shaping factors and regulatory framework . 26 V Empirical evidence . 27 A Demand-side factors: market size and epidemiological distribution . 28 B Supply-side factors . 29 C Policy-shaping factors: drug approval, patenting, price regulation and trade policy 33 VI Discussion and final remarks . 34 VII Appendix: Tables . 37 VIII Appendix: Figures . 40 3 Data and empirical methods of this thesis 41 I Description of data sources . 41 II Assumptions on the construction of the datasets . 43 A Unit of analysis - Innovation at therapeutic indication level . 43 B The construction of the datasets . 44 C Product market definition . 45 D Variables . 45 III Implications of data limitations . 48 IV Appendix: Tables . 51 4 The determinants of failure in drug development: a duration analysis 53 I Background . 53 II Specification model and estimation strategy . 56 III Data and variables . 61 IV Results . 63 3 A Descriptive statistics and nonparametric analysis . 63 B Semi-parametric analysis . 65 V Discussion and conclusions . 67 VI Appendix: Tables . 71 VII Appendix: Figures . 83 5 Micro-simulation of future pharmaceutical R&D landscape 87 I Introduction . 87 II Methodology . 88 A Market entry and failure as competing events in the dynamics of the R&D pipeline 89 B The birth function . 92 C The dynamic micro-simulation model . 93 D Data and variables . 95 III Results . 97 A Descriptive statistics . 97 B Micro-simulation results . 98 IV Discussion and conclusions . 101 V Appendix: Tables . 104 VI Appendix: Figures . 128 6 Measuring inequalities in pharmaceutical innovation in terms of unmet health need 139 I Introduction . 139 II Background and literature . 140 III Methods for the measurement of inequalities . 142 A The concentration curve . 142 B The family of rank-dependent concentration indices . 143 IV Variables and data . 144 A Global pharmaceutical R&D activity . 144 B Global health need . 145 C Disease market attractiveness . 146 D Sources of Information . 146 V Descriptive statistics . 147 VI Results from concentration curves and concentration indices . 149 A The two broad disease groups . 150 B Disease subcategories . 150 C What changed from 1990 to 2010? . 152 VII Discussion and conclusions . 152 VIII Appendix: Tables . 155 IX Appendix: Figures . 166 7 Concluding remarks 173 I Policy implications and value for decision makers . 175 4 List of Figures 2.1 Framework of complementary factors explaining R&D allocation . 22 2.2 Preferred reporting Items for Systematic reviews and Meta-Analyses (PRISMA) flowchart 40 4.1 Annual percentage rate of failure of drug projects . 83 4.2 Annual percentage rate of start-ups of drug projects . 84 4.3 Kaplan-Meier survival function: market competition from young drugs in discovery . 84 4.4 Kaplan-Meier survival function: competition from potential competitors in discovery . 85 4.5 Kaplan-Meier survival function: intensity of alliances in Phase 3 trials . 85 4.6 Kaplan-Meier survival function: participation of big firms in Phase 3 trials . 86 5.1 Illustration of the competing risks model . 90 5.2 Illustration of the micro-simulation model . 94 5.3 Historical regional trends in the number of market launches . 128 5.4 NCEs, historical trends in the number of market launches at therapeutic level . 128 5.5 Biologics, historical trends in the number of market launches at therapeutic level . 129 5.6 NCEs, historical trends in the number of market launches at therapeutic level in the USA 129 5.7 NCEs, historical trends in the number of market launches at therapeutic level in the EMA region . 130 5.8 NCEs, historical trends in the number of market launches at therapeutic level in Japan . 130 5.9 NCEs, historical trends in the number of market launches at therapeutic level in the EMAP region . 131 5.10 NCEs, historical trends in the number of market launches at therapeutic.
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