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

Business Insights
161 Seiten

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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

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