Translational Medicine in Biopharmaceutical R&D: Enabling R&D optimization and early detection of potential failures

Translational Medicine in Biopharmaceutical R&D: Enabling R&D optimization and early detection of potential failures

As the pharma industry continues to experience rising research costs, drug failures and low returns on investment, companies who are increasingly facing major patent expiries and scarcely populated late-stage pipelines have accelerated efforts to enhance the speed and efficiency of drug research. Translational science has emerged as a concept that is set to revolutionise the traditional R&D paradigm by integrating drug discovery and development, areas with previously limited interaction. The primary goals of this approach are to terminate unsuccessful compounds earlier in their development, improve confidence in human drug targets and enhance cost-effective decision-making. Translational Medicine in Biopharmaceutical R&D is a new report published by Business Insights that examines how translational medicine can positively influence the impact of biomarkers, innovations in clinical trial designs and the IT systems that support these functions. The report will identify how data generated from pre-clinical studies and clinical trials can be used synergistically between lab and clinic to enhance success rates and improve ‘go/no-go’ decision making. The strategies adopted by pharma companies to incorporate such initiatives are evaluated and potential cost-savings are assessed. This report will also highlight recent regulatory shifts in biomarker development and application, in addition to investigating the development of adaptive and seamless trial designs and micro-dosing. Discover the benefits of translational medicine, evaluate the strategies used by R&D organisations to implement translational methods and identify the innovative technologies central to efficiency gains with this new report...

Table of Contents
Translational Medicine in Biopharmaceutical R&D
Executive Summary 10
Introduction 10
Technologies advancing translational research 11
Biomarkers: Concepts and Case Studies 12
Innovation in Clinical Trials 13
Bioinformatics in translational medicine 14
Implementing translational medicine 14
Chapter 1 Introduction 18
Summary 18
Defining translational medicine 19
Translational medicine in the pharma industry 20
Translational medicine in academia 22
Drivers of translational research 24
Rising costs 24
Patent expiries 27
Medicine’s transformation and consumer expectations 27
Report Outline 29
Chapter 2 Technologies advancing
translational research 32
Summary 32
Introduction 33
‘Omic technologies 34
Genomics and transcriptomics 35
Novel target identification with genomics 37
Candidate gene linkage studies 37
Whole genome association studies 38
Proteomics and peptidomics 38
Metabolomics 40
Systems biology 43
RNA interference (RNAi) 43
RNAi knock-down in animals 45
Imaging 45
Imaging technologies 45
Molecular imaging 47
Imaging in clinical drug development 50
Imaging surrogate endpoints 50
Imaging in mechanistic studies 51
Imaging service companies 52
Animal models 53
Tissue banking 54
Conclusions 56
Chapter 3 Biomarkers: concepts and case
studies 58
Summary 58
Introduction 59
Biomarkers of response 60
Biomarkers of efficacy and dose 64
Safety biomarkers 66
Preclinical safety biomarkers 67
Clinical safety biomarkers 69
Implementing a biomarker strategy 71
Biomarker discovery companies 74
Regulatory issues 74
Validating biomarkers 75
Interactions with regulators 78
Conclusion 79
Chapter 4 Innovation in clinical trials 82
Summary 82
Introduction 83
Microdosing 84
Other applications of AMS 87
Industry uptake 88
Regulatory status 89
The future for AMS-based studies 89
Technologies 89
Linking pharmacology data to microdose studies 90
Adaptive clinical trial designs 90
Adaptive dose-ranging studies 92
Seamless adaptive trials 93
Issues to be managed in adaptive clinical trials 95
Preplanning and simulations 95
Maintaining data confidentiality 96
Minimizing operational bias and assuring consistency between study stages 97
Logistics 97
Regulatory status 99
The future 99
Adaptive clinical trials: patient stratification 100
Patient stratification – advantages 102
Patient stratification – potential problems 102
Regulatory status 103
Conclusions 104
Chapter 5 Bioinformatics in translational
medicine 108
Summary 108
Introduction 109
Warehousing and integrating diverse data sources 111
Data analytics for diverse personnel 112
Use of an IT system to improve translational medicine – a case study 114
Data Standards 115
Companies providing IT solutions for translational medicine 115
Conclusions 117
Chapter 6 Implementing translational
medicine 120
Summary 120
Translational medicine will change the drug development paradigm 121
Introduction of Phase 0 121
Collapse of Phase 1 and 2A 122
Adaptive trials in Phase 2B/3 122
The learning and confirming model of drug research 123
Implementing translational medicine in the pharma industry 124
Organon NV, a division of Akzo-Nobel 125
AstraZeneca 126
Pfizer 128
Wyeth and Novartis 129
Challenges and opportunities in translational medicine 129
Challenges 129
Opportunities 131
Potential cost savings of translational medicine 131
Conclusion 134
Chapter 7 Appendix 144
Primary research methodology 144
List of Figures
Figure 1.1: The translational continuum 20
Figure 1.2: Translational medicine in the pharma industry 21
Figure 1.3: New drug approvals versus R&D costs: 1995-2005 25
Figure 1.4: Medicine’s emerging transformation 28
Figure 2.5: Technological innovations underpinning translational medicine 33
Figure 2.6: Technologies for genomics, transcriptomics, proteomics and metabolomics 35
Figure 2.7: 1H NMR spectrum of urine showing functional windows 41
Figure 2.8: Imaging techniques and their uses 47
Figure 3.9: Types of biomarker and their uses in drug development and disease management 59
Figure 3.10: Development of linked preclinical and clinical biomarkers for BPH 66
Figure 3.11: Biomarkers and assay development process 72
Figure 3.12: Proposed biomarker validation in preclinical drug safety assessment 77
Figure 4.13: Innovative clinical trials enable translational medicine 83
Figure 4.14: Comparison of midazolam pharmacokinetics at microdose and therapeutic dose levels in the CREAM study 86
Figure 4.15: Seamless adaptive trial design 94
Figure 4.16: Targeted study designs 101
Figure 5.17: IT systems for translational medicine 110
Figure 5.18: Key attributes of an IT solution for translational medicine 111
Figure 5.19: Users of translational medicine IT systems 113
Figure 5.20: Case study: using InforSense KDE® 114
Figure 6.21: The ‘learn and confirm’ model of drug development 124
Figure 6.22: Cost reductions from higher clinical success rates 132
Figure 6.23: Pre-approval out-of-pocket and capitalized costs per approved new molecule 133
List of Tables
Table 1.1: Selection of academic translational research centers 23
Table 1.2: Attrition rates in drug development 26
Table 1.3: Major drug patent expiries: 2007-2009 27
Table 2.4: Kinetic markers available from KineMed 42
Table 2.5: Manufacturers of molecular imaging equipment and probes 49
Table 2.6: Selected biobanking resources 55
Table 3.7: Examples of associations between drug response and genetic variants 62
Table 3.8: Examples of valid genomic biomarkers in drug labels 70
Table 3.9: Calculating biomarker ROI 73
Table 3.10: Definitions and examples of biomarkers with different levels of qualification 76
Table 4.11: Companies offering AMS services 85
Table 4.12: Advantages and disadvantages of AMS-based microdosing studies 85
Table 4.13: Advantages and disadvantages of using AMS for mass balance and absolute bioavailability studied 88
Table 4.14: Advantages and disadvantages of using adaptive clinical trial designs 91
Table 4.15: Integrity and validity in adaptive clinical trials 95
Table 4.16: Comparison of targeted and untargeted study designs 102
Table 5.17: Bioinformatics companies with an interest in translational medicine 117
Table 6.18: Companies using biochemical and systems biology tools for biomarker discovery: A-C 136
Table 6.19: Companies using biochemical and systems biology tools for biomarker discovery: C - E 137
Table 6.20: Companies using biochemical and systems biology tools for biomarker discovery: E - H 138
Table 6.21: Companies using biochemical and systems biology tools for biomarker discovery: H - J 139
Table 6.22: Companies using biochemical and systems biology tools for biomarker discovery: K - O 140
Table 6.23: Companies using biochemical and systems biology tools for biomarker discovery: O - Z 141