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

Table of Contents

Executive Summary

• Introduction
• Technologies advancing translational research
• Biomarkers: Concepts and Case Studies
• Innovation in Clinical Trials
• Bioinformatics in translational medicine
• Implementing translational medicine

Chapter 1 Introduction

• Summary
• Defining translational medicine
• Translational medicine in the pharma industry
• Translational medicine in academia
• Drivers of translational research
• Rising costs
• Patent expiries
• Medicine' s transformation and consumer expectations
• Report Outline

Chapter 2 Technologies advancing translational research

• Summary
• Introduction
• ‘Omic technologies
• Genomics and transcriptomics
• Novel target identification with genomics
  • Candidate gene linkage studies
  • Whole genome association studies
• Proteomics and peptidomics
• Metabolomics
• Systems biology
• RNA interference (RNAi)
• RNAi knock-down in animals
• Imaging
• Imaging technologies
• Molecular imaging
• Imaging in clinical drug development
  • Imaging surrogate endpoints
  • Imaging in mechanistic studies
• Imaging service companies
• Animal models
• Tissue banking
• Conclusions

Chapter 3 Biomarkers: concepts and case studies

• Summary
• Introduction
• Biomarkers of response
• Biomarkers of efficacy and dose
• Safety biomarkers
• Preclinical safety biomarkers
• Clinical safety biomarkers
• Implementing a biomarker strategy
• Biomarker discovery companies
• Regulatory issues
• Validating biomarkers
• Interactions with regulators
• Conclusion

Chapter 4 Innovation in clinical trials

• Summary
• Introduction
• Microdosing
• Other applications of AMS
• Industry uptake
• Regulatory status
• The future for AMS-based studies
  • Technologies
  • Linking pharmacology data to microdose studies
• Adaptive clinical trial designs
• Adaptive dose-ranging studies
• Seamless adaptive trials
• Issues to be managed in adaptive clinical trials
  • Preplanning and simulations
  • Maintaining data confidentiality
  • Minimizing operational bias and assuring consistency between study
  • stages
  • Logistics
• Regulatory status
• The future
• Adaptive clinical trials: patient stratification
• Patient stratification - advantages
• Patient stratification - potential problems
• Regulatory status
• Conclusions

Chapter 5 Bioinformatics in translational medicine

• Summary
• Introduction
• Warehousing and integrating diverse data sources
• Data analytics for diverse personnel
• Use of an IT system to improve translational medicine - a case study
• Data Standards
• Companies providing IT solutions for translational medicine
• Conclusions

Chapter 6 Implementing translational medicine

• Summary
• Translational medicine will change the drug development paradigm
• Introduction of Phase 0
• Collapse of Phase 1 and 2A
• Adaptive trials in Phase 2B/3
• The learning and confirming model of drug research
• Implementing translational medicine in the pharma industry
• Organon NV, a division of Akzo-Nobel
• AstraZeneca
• Pfizer
• Wyeth and Novartis
• Challenges and opportunities in translational medicine
• Challenges
• Opportunities
• Potential cost savings of translational medicine
• Conclusion

Chapter 7 Appendix

• Primary research methodology

List of Figures

• Figure 1.1: The translational continuum
• Figure 1.2: Translational medicine in the pharma industry
• Figure 1.3: New drug approvals versus R&D costs: 1995-2005
• Figure 1.4: Medicine' s emerging transformation
• Figure 2.5: Technological innovations underpinning translational medicine
• Figure 2.6: Technologies for genomics, transcriptomics, proteomics and metabolomics
• Figure 2.7: 1H NMR spectrum of urine showing functional windows
• Figure 2.8: Imaging techniques and their uses
• Figure 3.9: Types of biomarker and their uses in drug development and disease management
• Figure 3.10: Development of linked preclinical and clinical biomarkers for BPH
• Figure 3.11: Biomarkers and assay development process
• Figure 3.12: Proposed biomarker validation in preclinical drug safety assessment
• Figure 4.13: Innovative clinical trials enable translational medicine
• Figure 4.14: Comparison of midazolam pharmacokinetics at microdose and therapeutic dose levels in the CREAM study
• Figure 4.15: Seamless adaptive trial design
• Figure 4.16: Targeted study designs
• Figure 5.17: IT systems for translational medicine
• Figure 5.18: Key attributes of an IT solution for translational medicine
• Figure 5.19: Users of translational medicine IT systems
• Figure 5.20: Case study: using InforSense KDE®
• Figure 6.21: The ' learn and confirm' model of drug development
• Figure 6.22: Cost reductions from higher clinical success rates
• Figure 6.23: Pre-approval out-of-pocket and capitalized costs per approved new molecule

List of Tables

• Table 1.1: Selection of academic translational research centers
• Table 1.2: Attrition rates in drug development
• Table 1.3: Major drug patent expiries: 2007-2009
• Table 2.4: Kinetic markers available from KineMed
• Table 2.5: Manufacturers of molecular imaging equipment and probes
• Table 2.6: Selected biobanking resources
• Table 3.7: Examples of associations between drug response and genetic variants
• Table 3.8: Examples of valid genomic biomarkers in drug labels
• Table 3.9: Calculating biomarker ROI
• Table 3.10: Definitions and examples of biomarkers with different levels of qualification
• Table 4.11: Companies offering AMS services
• Table 4.12: Advantages and disadvantages of AMS-based microdosing studies
• Table 4.13: Advantages and disadvantages of using AMS for mass balance and absolute bioavailability studied
• Table 4.14: Advantages and disadvantages of using adaptive clinical trial designs
• Table 4.15: Integrity and validity in adaptive clinical trials
• Table 4.16: Comparison of targeted and untargeted study designs
• Table 5.17: Bioinformatics companies with an interest in translational medicine
• Table 6.18: Companies using biochemical and systems biology tools for biomarker discovery: A-C
• Table 6.19: Companies using biochemical and systems biology tools for biomarker discovery: C-E
• Table 6.20: Companies using biochemical and systems biology tools for biomarker discovery: E-H
• Table 6.21: Companies using biochemical and systems biology tools for biomarker discovery: H-J
• Table 6.22: Companies using biochemical and systems biology tools for biomarker discovery: K-O
• Table 6.23: Companies using biochemical and systems biology tools for biomarker discovery: O-Z