Translational Medicine In Biopharmaceutical R&D: Enabling R&D Optimization And Early Detection Of Potential Failures

Published by: Business Insights

Published: Oct. 1, 2007 - 161 Pages

Table of Contents

Translational Medicine in Biopharmaceutical R&D

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

Abstract

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