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

Published by	Business Insights
Published	October, 2007	Product code	57638
Content info	161 pages
Price	US $ 2875 PDF by E-mail (Single User License) US $ 10782 PDF by E-mail (Global License)

Introduction
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Translational Medicine in Biopharmaceutical R&D: Enabling R&D optimization and early detection of potential failures published by Business Insights in October, 2007. This report consists of 161 pages and the price starts from US $ 2875.

Introduction

Abstract

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...

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