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Pathways to Efficient Drug Development - Advances in Modeling and Simulation Outcomes to Fuel Pipeline Productivity

Executive Summary

Clinical Trials Due an Overhaul to Conquer the Pharma 'Valley of Death'.

Costs associated with drug development have risen from around $138m in 1975 to over $1 billion in 2005, with clinical trials representing a key factor in this increasing expenditure, according to a new report by healthcare industry experts GBI Research.

The new research* looks at why researchers in the pharma industry now refer to the clinical development process as the 'valley of death', which must be crossed to attain drug approval.

Clinical trials have grown longer and more complex, while volunteer enrolment and retention rates have fallen. More than 30% of experimental drugs that reach Phase III fail at this point. Late stage failures are costly to the industry - Pfizer's torcetrapib, which failed its Phase III trial in 2006, led to the value of Pfizer dropping by $21 billion over night, and 10,000 job losses being announced the following year.

Recent analyses show that this problem has grown in recent years. Overall, clinical approval success rates have fallen to approximately 16%, though this figure varies widely by therapeutic area. Pivotal trial and regulatory failures were recorded for 31 drugs in 2011, with Eli Lilly, Bristol-Myers Squibb, AstraZeneca, Merck, Sanofi, Novartis and GlaxoSmithKline all suffering at least one setback.

The most common reason for drug failure during Phase II development is a lack of efficacy as determined by primary endpoints. Phase III failures tend to be in therapeutic areas that may provide a higher chance of approval and reimbursement, such as cancer or neuroscience. Whilst it is tempting to speculate that drug development is more difficult for drugs with novel mechanisms of action in these areas of unmet need, this data implies that the pressure on companies to keep development pipelines full may also have led to compounds advancing into Phase III on the basis of inadequate or marginal proof-of-concept.

Others have argued that the majority of failures in clinical trials are due to the inadequacy of animal models to predict success in humans, implying a fundamental flaw in drug development processes which use animal testing methods.

It is essential that R&D productivity is improved, and the 'quick win, fast fail' model is being touted as a possible way to achieve this. This model argues that investments made early in the process increase the information available on which to base key decisions, enabling the earlier termination of projects prior to huge investments being made for the Phase III program.

Pathways to Efficient Drug Development - Advances in Modeling and Simulation Outcomes to Fuel Pipeline Productivity

This report examines the reasons why the pharmaceutical industry is looking for improvements in efficiency, whilst acknowledging that pharmaceutical R&D remains a long and risky process. It looks in detail at precompetitive research and evaluates how the industry is pulling together to research solutions to problems that are common to all companies. The report investigates innovation in the clinical drug development arena, documenting modeling and simulation based approaches to improving efficiency, as well as novel clinical trial designs. Lastly, the report examines innovation in business models within the industry that aim to help the industry to achieve its mantra of "doing more with less", which will be critical for its future success.

The report is built using data and information sourced from proprietary databases, primary and secondary research and in-house analysis by GBI Research's team of industry experts.

Abstract

Pathways to Efficient Drug Development - Advances in Modeling and Simulation Outcomes to Fuel Pipeline Productivity

Summary

GBI Research, the leading business intelligence provider, has released its latest research report, "Pathways to Efficient Drug Development - Advances in Modeling and Simulation Outcomes to Fuel Pipeline Productivity". The report examines the reasons why the pharmaceutical industry is looking for improvements in efficiency whilst acknowledging that pharmaceutical R&D remains a long and risky process. It looks in detail at precompetitive research and evaluates how the industry is pulling together to research solutions to problems that are common to all companies. The report investigates innovation in the clinical drug development arena, documenting modeling and simulation based approaches to improving efficiency, as well as novel clinical trial designs. Lastly, the report examines innovation in business models within the industry that aim to help the industry to achieve its mantra of "doing more with less", which will be critical for its future success.

The report is built using information from primary and secondary research including interviews with experts in the field.

GBI Research's analysis shows that collaboration and open innovation will play increasingly important roles in the future by enabling research that would not be possible for companies to undertake individually. Many examples exist, driven in large part by the FDA's Critical Path Initiative and the European Innovative Medicines Initiative, and experiences gained by early consortia will help facilitate the logistical challenges of setting up new collaborations. Within companies, innovations including the increased use of modeling and simulation throughout the drug development process, adaptive clinical trials and exploratory clinical trials have all been studied for some years, suggesting that innovation is hard, but important. Innovation is also occurring in the business models applied within individual companies to enable them to achieve "more with less". Through adoption of new scientific approaches and business models, companies are hoping not only to refuel their pipelines but to regain the confidence of investors and the public in their ability to deliver meaningful treatments for patients at the same time as generating profits in the coming years.

Scope

  • Detailed analysis of the reasons for the industry to be looking closely at improving efficiency
  • Definition of precompetitive collaboration, analysis of areas in which precompetitive collaboration is occurring, and discussion of the expansion of this space in the future
  • Explorations of the key challenges facing consortia and the factors that make them successful
  • Case studies of key innovations in drug development including model-based drug development, adaptive clinical trials and exploratory clinical trials
  • Detailed insights into innovation in business models, including virtual networks, open innovation and extensive academic collaborations

Reasons to buy

  • Identify key projects in the precompetitive space
  • Learn the most important factors for successful precompetitive collaborations
  • Develop strategies and priorities for participating in precompetitive collaborations
  • Understand current thinking on innovative areas of drug development, including model-based drug development, adaptive clinical trials and exploratory clinical trials, from the viewpoints of companies and regulators
  • Explore the business models and partnerships of the largest pharmaceutical companies to support new drug development strategies

TOC

1 Table of Contents

1 Table of Contents 5

  • 1.1 List of Tables 7
  • 1.2 List of Figures 8

2 State of the Industry 9

  • 2.1 Introduction 9
    • 2.1.1 The Rising Costs of Drug Development 10
    • 2.1.2 Drug Attrition 12
    • 2.1.3 Patent Expiries 14
    • 2.1.4 Regulatory Hurdles 15
    • 2.1.5 The Fourth Hurdle: Reimbursement 16
  • 2.2 Innovation in the Drug Development Paradigm 16
    • 2.2.1 The Critical Path Initiative 16
    • 2.2.2 The Innovative Medicines Initiative 18
  • 2.3 Improving Drug Development 19
  • 2.4 2012 and Beyond 20

3 Collaboration in the Precompetitive Space 21

  • 3.1 Defining Precompetitive Research 21
  • 3.2 Building Successful Consortia 23
    • 3.2.1 Choosing the Research Topic 23
    • 3.2.2 The Set-Up Phase 23
    • 3.2.3 Project Management 24
    • 3.2.4 Measuring Success 25
    • 3.2.5 Case Study of a Successful Private Public Partnership: The Alzheimer's Disease Neuroimaging Initiative 26
  • 3.3 Qualification of Biomarkers of Efficacy or Safety 26
    • 3.3.1 Case Study: Biomarkers of Kidney Injury 29
    • 3.3.2 Case Study: The Biomarkers Consortium 31
  • 3.4 Open Innovation Platforms 32
    • 3.4.1 Case Study: OpenPHACTS 33
    • 3.4.2 Case Study: Sage Bionetworks 34
  • 3.5 Data Standards 35
    • 3.5.1 BioSharing: Standard Cooperating Procedures 35
    • 3.5.2 Clinical Data Standards 36
    • 3.5.3 Data Standards and the FDA 36
  • 3.6 Conclusions 37

4 Improving Drug Development Efficiency 38

  • 4.1 Modeling and Simulation 38
    • 4.1.1 Modeling and Simulation: A View from the Regulators 39
    • 4.1.2 Model Qualification 40
    • 4.1.3 Modeling and Simulation Expertise and Consultancy 40
  • 4.2 Innovative Approaches to Clinical Trials 41
    • 4.2.1 Adaptive Clinical Trials 42
    • 4.2.2 Case Study: The I-SPY 2 Trial 44
    • 4.2.3 Exploratory Clinical Trials 45
  • 4.3 Engaging Stakeholders 46
    • 4.3.1 Patients 46
    • 4.3.2 Regulators 47
    • 4.3.3 Payers 47
  • 4.4 Conclusions 47

5 Business Models 48

  • 5.1 Introduction 48
  • 5.2 R&D Reorganization 48
    • 5.2.1 Mimicking the Biotech Environment 48
    • 5.2.2 The Fully Integrated Pharmaceutical Network Model 49
  • 5.3 Open Innovation 50
    • 5.3.1 Case Study: An Open Innovation Incubator 51
    • 5.3.2 Case Study: Open Innovation Drug Discovery at Eli Lilly 52
  • 5.4 Funding for External Innovation 53
    • 5.4.1 Collaborative Commercialization 54
  • 5.5 Academic Partnerships and Translational Medicine 55
    • 5.5.1 Translational Science in the US 57
    • 5.5.2 Case study: Medical Research Council/AstraZeneca 58
  • 5.6 Conclusions 59

6 Appendix 60

  • 6.1 Abbreviations 60
  • 6.2 Methodology 61
    • 6.2.1 Primary Research 61
    • 6.2.2 Secondary Research 62
  • 6.3 References 62
  • 6.4 Contact Us 66
  • 6.5 Disclaimer 66

List of Tables

1.1 List of Tables

  • Table 1: Pathways to Efficient Drug Development, Clinical and FDA Approval Times across Therapeutic Classes (2005-2009) 11
  • Table 2: Pathways to Efficient Drug Development, Transition Probability at Each Stage of Clinical Drug Development 12
  • Table 3: Pathways to Efficient Drug Development, Overall FDA Approval Success Rate for New Chemical Entities by Therapeutic Area 12
  • Table 4: Pathways to Efficient Drug Development, Loss of US Sales Revenues Due to Patent Expiries ($m; 2010-2013) 14
  • Table 5: Pathways to Efficient Drug Development, Drugs Withdrawn from the Market in the US (1992-2010) 15
  • Table 6: Pathways to Efficient Drug Development, Ongoing Projects of the Critical Path Institute 17
  • Table 7: Pathways to Efficient Drug Development, Proposed Network for Evaluating PPPs in the Pharmaceutical Sciences 25
  • Table 8: Pathways to Efficient Drug Development, Biomarkers qualified by the FDA for use in drug development 27
  • Table 9: Pathways to Efficient Drug Development, Biomarkers qualified by the EMA for use in drug development 27
  • Table 10: Pathways to Efficient Drug Development, Ongoing Public-Private Partnerships for Biomarker Identification and Qualification 28
  • Table 11: Pathways to Efficient Drug Development, Pharmaceutical Companies Involved in the Predictive Safety Testing Consortium, IMI SAFE-T Project and the Biomarkers Consortium Kidney Project 30
  • Table 12: Pathways to Efficient Drug Development, Ongoing and Completed Projects being Undertaken by The Biomarkers Consortium 31
  • Table 13: Pathways to Efficient Drug Development, Open Innovation Platforms to Enhance Drug Discovery 33
  • Table 14: Pathways to Efficient Drug Development, Disease Specific Models Developed by the FDA 39
  • Table 15: Pathways to Efficient Drug Development, Pharmacometric Consultancies 41
  • Table 16: Pathways to Efficient Drug Development, Examples of Companies Offering Accelerator Mass Spectrometry Services 45
  • Table 17: Pathways to Efficient Drug Development, Eli Lilly's Long-Term Service Providers 49
  • Table 18: Pathways to Efficient Drug Development, Open Innovation Business Models that Place Research Results in the Public Domain 51
  • Table 19: Pathways to Efficient Drug Development, Examples of Pharmaceutical Corporate Venture Capital Funds 53
  • Table 20: Pathways to Efficient Drug Development, New Companies Launched by Enlight Bioscience 54
  • Table 21: Pathways to Efficient Drug Development, Projects Funded by Pfizer's Centers for Therapeutic Innovation 55
  • Table 22: Pathways to Efficient Drug Development, Examples of Recent Collaborations Between Academia and the Pharmaceutical Industry 56
  • Table 23: Pathways to Efficient Drug Development, AstraZeneca Compounds Made Available for Research (December 2011) 58

List of Figures

1.2 List of Figures

  • Figure 1: Pathways to Efficient Drug Development, Number of New Drug and Biologic FDA Approvals and Global R&D Expenditure by the Pharmaceutical Industry (2004-2011) 9
  • Figure 2: Pathways to Efficient Drug Development, Drivers for Innovation in the Pharmaceutical Industry 10
  • Figure 3: Pathways to Efficient Drug Development, The Rising Cost of Drug Development 1975-2005 10
  • Figure 4: Pathways to Efficient Drug Development, Changes in Clinical Trial Parameters between 2000-2003 and 2004-2007 11
  • Figure 5: Pathways to Efficient Drug Development, Failure Rates According to Therapeutic Area in Phase II and Phase III/Submission 13
  • Figure 6: Pathways to Efficient Drug Development, History of the European Innovative Medicines Initiative and its Strategic Research Agenda 18
  • Figure 7: Pathways to Efficient Drug Development, European Innovative Medicines Initiative: Strategic Research Agenda (updated 2012) 19
  • Figure 8: Pathways to Efficient Drug Development, New Drug Applications Filed with the FDA Centre for Drug Evaluation and Research (1996-2011) 20
  • Figure 9: Pathways to Efficient Drug Development, Key Areas of Precompetitive Research 21
  • Figure 10: Pathways to Efficient Drug Development, Disease, Drug and Trial Models: Pharmacometrics in Drug Development 39
  • Figure 11: Pathways to Efficient Drug Development, The I-SPY2 Adaptive Clinical Trial 44
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