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市場調查報告書
生物藥劑研發之發展醫療
Translational Medicine in Biopharmaceutical R&D: Enabling R&D optimization and early detection of potential failures
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生物藥劑研發之發展醫療 是由出版商Business Insights在2007年10月所出版的。
這份英文市場調查報告書包含161 pages 價格從美金2875起跳。
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...
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
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