1. Introduction
1.1 From Corporate Organization to Organism
1.2 The Drivers of R&D Transformation
1.3 The Information Spine
1.4 Changing the Wheels of a Moving Car
1.5 Democratizing Business Strategy
2. The Information Spine for Pharmaceutical Companies
2.1 Informatics Decreasing Project Risk in the Pharmaceutical Industry
2.2 Enterprise Software Solutions Create a Framework for Modernized R&D
2.3 Requirements for a Bioinformatics Framework
2.4 Integration of Data Sources and Analytical Tools
2.5 Enterprise Software Solution Architecture
2.6 Conclusion
3. Building Data Management and Analysis through Definition of Business Dimensions
3.1 Introduction
3.2 Integrated Teams Create Competitive Advantage
3.3 The Business Dimensional Life Cycle
3.4 Understanding the Organization
3.5 Defining Project Scope
3.6 Managing the Scope
3.7 The Integrated Team
3.8 Define the Business Requirements
3.9 Approaches to Data Modeling
3.10 How to Build a Database System on the Business Dimensional Model
3.11 Impact of Data Access Tools
3.12 Last Words on Implementation
4. Research Execution Systems: Integrating the Research Supply Chain
4.1 Introduction
4.2 Material Management Process
4.3 Common Bottlenecks and a Novel Solution
4.4 Reagent Procurement and Management
4.5 E-Commerce and RES
4.6 Inventory Control
4.7 Regulatory Compliance and Waste Management
4.8 Procurement Costs
4.9 Substance Management
4.10 Conclusion
5. Research Informatics Capabilities as a Tool for Creating Competitive Advantage in R&D in the New Millennium
5.1 Introduction
5.2 Sustaining Productivity of Drug Discovery
Number of NCEs in the Pipeline is Accelerating
Explosion of Data: It Really is Different
Focused Strategies - Choosing Compounds and Therapeutic Areas
5.3 Increasing Commercial Value Requires a Comprehensive Performance Metrics & Management Framework
5.4 Leveraging Research Informatics to Improve Innovation, Productivity and Speed
5.5 Key Strategic Issues in Research and Discovery
- Why Promising Candidates Fail
What Technologies Can Reduce Bottlenecks?
5.6 Redesigning the Discovery Development Interface
5.7 Discovery IT Strategy for Creating Competitive Advantage
Solutions Remain Fragmented at Most Companies-Integration is the Trend
5.8 Central Role of Knowledge Management in Discovery
5.9 Conclusion
5.10 Questions & Answers
6. Delivering Research Informatics to Support the Knowledge-Led Discovery and Development Process
6.1 Introduction
6.2 Implications of New Pharmaceutical Discovery Technologies
6.3 Pharmaceutical Company Project Integration
6.4 Two-Tiered Pharmaceutical IT Infrastructure
6.5 Powerful "Live" Report Capability
6.6 Exploiting Information toward Project Management and Competitive Advantage
6.7 Challenges Facing Informatics
6.8 Change Management Issues
7. Informatics and Visual Data Analysis as a Catalyst to Innovation and Protecting Intellectual Property
7.1 Patents are Becoming the Largest Body of Technical Knowledge
7.2 Licensing IP is an Important Part of Development
7.3 Typical Development Project Review Questions
7.4 IP Visualization to Improve Discovery, Development and Licensing Performance
7.5 Conclusion
8. Web-Based Systems for Decision Support in Lead Discovery
8.1 Limiting Factors in R&D Raise a Call for Decision Support
8.2 Cornerstones of Decision Support
8.3 Access
8.4 Analysis
8.5 Publish
8.6 Product Intelligence for the Extended Enterprise
8.7 Benefits of Data Integration, Mining and Visualization
8.8 Partnerships to Expand the Capability of Analytical Tools
8.9 Conclusion
9. The Discovery of Hidden Relationships by Data Visualization
9.1 The Rationale for Data Mining and Visualization
9.2 Text Visualization
9.3 Numerical Data Analysis
9.4 Coupling Experiments to Each Other and to the Literature
9.5 Value Proposition for Data Visualization
9.6 Conclusion
10. Automating Chemoinformatics Business Practices
10.1 The Pharmaceutical Industry Races Against Itself
10.2 Growth is Attended by Inefficiencies
10.3 History and Development of the Chemoinformatics Business Rules Manager
10.4 The CBRM System for Standardizing Chemical Databases
10.5 Conclusion
11. Advanced Informatics for the Pharmaceutical Industry
11.1 Unplanned and Planned Innovation
11.2 Information Integration
11.3 The Basics of "Good" Information
11.4 Data Integration
11.5 Informatics In Action
11.6 Tripos: Life Science Informatics
12. Extracting Value from the Deluge of Life Science Data
12.1 The Need to Transform R&D
12.2 The Key to R&D Transformation: Improving the Information Spine
12.3 The Virtual Database Solution
12.4 Focus on Infrastructure while Developing Partnerships for Application Development
12.5 Conclusion
13. Integration of Molecular Modeling into Research Informatics
13.1 The Role of Pharmaceutical Research Information Technology
13.2 Efficiencies Realized - The Greater Task Ahead
13.3 Creating Standards is Key to Realizing Data Integration
13.4 Answering the Standardization Challenge Will Pave the Way for Molecular Modeling Informatics
13.5 Demonstrating the Power of Data Integration in Molecular Modeling and Activity Prediction
13.6 A Web-based Format for Integrating Molecular Modeling into an Informatics Environment
13.7 Conclusion
14. Knowledge Management in Drug Discovery
14.1 Knowledge Management
14.2 Thought Experiment - Lost Knowledge
14.3 Evaluation of Knowledge Management Platforms
14.4 Pharmaceutical Data and Knowledge
14.5 Implementation of Knowledge Management
14.6 Knowledge Management for Workflow Support
14.7 Target Selection and Identification
14.8 Conclusion
15. Prospective Informatics Applications for the Human Genome Sequence-Optimizing the Earliest Stages of Discovery
15.1 Using Genomics to Optimize Drug Discovery
15.2 Applications of Genomics to Optimizing Target Selection
15.3 Complexity of Gene Expression Requires Use of Complete Genomic Information
15.4 Resolving Genetic Variation at the Genomic and Cellular Level
15.5 Conclusion
16. A Bridge Between Two Worlds: Connecting Chemical and Biological Data
16.1 Introduction
16.2 LION and bioSCOUT® Technology
16.3 SRS Technology
16.4 Corporate Data Integration and LION Consulting
16.5 Bridging Biological and Chemical Data
16.6 Conclusion
17. Clinical Informatics: Preserving the Crown Jewels in the Business of Drug Discovery and Development
17.1 Growing Complexity and Value of Clinical Research
17.2 Building a Multi-Disciplinary Design and Implementation Team
17.3 Planning
17.4 Workflow
17.5 Standards
17.6 Technology
17.7 Assuring Quality and Efficiency with Clinical Informatics
17.8 Conclusion
18. Bioinformatics and the Internet: Application of the ASP Model to Life Science R&D
18.1 Research Informatics to Support Drug Discovery
18.2 Information-Based Drug Discovery Value Chain
18.3 The Challenge of Research Informatics
18.4 Integrating New Technologies
18.5 The Role of Research Informatics
18.6 The Problem: IT Infrastructure
18.7 The Solution: Application Service Providers
18.8 Advantages of Full-Service ASP
18.9 A Life Science ASP Model
18.10 The Pharmaceutical R&D Business Environment
18.11 Genome Informatics Infrastructure
18.12 Cost Effectiveness of Off-Site Hosting
18.13 Conclusion
19. Biomedical Information and the Discovery Process: Challenges and Opportunities
19.1 Clinical Data Used for Target Validation
19.2 Pathway Modeling Objectives
19.3 Defining Biological Activity
19.4 Disease Progression Model
19.5 Stratification of Patients versus Diseases
19.6 Modeling Pathways and Disease Processes
19.7 Adding Literature to Clinical Data
19.8 Genomic Screening and Disease Management
20. Defining Information Asset Management for an Extended Collaboration
20.1 A Consortium for Integrated Drug Discovery
20.2 Consortium Architecture, Physical and Virtual, Sets the Stage for Collaboration
20.3 A Definition of Informatics from the Industry
20.4 Information Asset Management - Intellectual Property Example
20.5 Defining the Information Assets for the Discovery Consortium
20.6 Conclusion
21. Conclusion
