3. Using State-of-the Art IT Systems to Integrate Preclinical Evaluations into
Discovery
Part I - Dealing with the Data Deluge
3.1 Dealing with the Quantum Jump in Amounts of
Data
3.2 Challenges in Discovery and Informatics
3.3 The Scale of the Data Deluge
3.4 Responses to Challenges in Discovery and
Informatics
3.5 Data Visualization
3.6 Enabling Technologies
Part II - Materials Management - Pitfalls and Bottlenecks
3.7 Compound Management Challenge
3.8 Solutions to Materials and Substance Handling
3.9 New Technologies Create Compound Overload
3.10 Bottlenecks and the "Hurry-Up-and-Wait"
Syndrome
3.11 Compound Management: The Hub of Drug
Research
3.12 A Typical Screening Procedure
3.13 Inventory Management Software: The Missing
Link
3.14 OPTIMA - A Compound Management Solution
3.15 Compound Management Solutions Drive Process
Improvement
3.16 Open And Integrated Operations
3.17 Conclusion
3.18 Summary
4. Better Decision-Making Through Data-Mining and Visualization
4.1 Managing the Increasing Volume and Complexity
of Data
4.2 Moving from "Analog" to "Digital" Exchange of
Knowledge
4.3 Scalable Systems for Data Storage and Analysis
4.4 Capturing Lessons and Knowledge
4.5 Evolution from Static Data Repositories to
Interactive Data-Mining and Visualization
4.6 MineSet as a Tool for Data-Mining and Visualization
4.7 Building Predictive Models Using Visualization of
Data
4.8 Building Predictive Models
4.9 Applications of Data-Mining and Visualization to
Discovery Research
4.10 Visualization of Genomics Data
4.11 Visualization of Chemical Data
4.12 Visualization of Clinical Trial Data
4.13 Estimating Errors and Testing Assumptions in
Model Design
4.14 From Information Overload to Insight
5. Discovery - Solving Developmental Challenges Before They Arise
5.1 Technological versus Organizational Development
Issues
5.2 Analysis of the Decision-Making Process During
R&D
5.3 Designing an Improved Discovery Template
5.4 Building Fully Integrated Teams
5.5 Strengthen Overall Management of the Process
5.6 Benefits Resulting from a Restructured R&D
Process
5.7 Key Success Factors for Performance Improvement
5.8 Questions & Answers
6. Informed Early Attrition
Part I - Pulling Clinical Risk Forward in Drug Development
6.1 The Clinical Discovery Stage
6.3 A Process Performance Model for Clinical Discovery
6.4 New Configurations During Early Clinical
Development
6.5 A Productivity Measure for Clinical Discovery
Part II - Modeling Resource Consumption and Probability of Technical Success
6.6 Introduction
6.7 The Rationale for Process Modeling in R&D
6.8 Evolution of the R&D Process
6.9 Approaches to Modeling the R&D Process
6.10 Models to Estimate Resource Requirements
6.11 Useful Models for Decision-Making
6.12 Two Case Studies
6.13 Chemistry Resource Allocation Model
6.14 Models to Evaluate the Economic Impact of
Pulling Risk Forward
6.15 Conclusion
6.16 Questions & Answers
7. Cost-Effective Development of Protein Therapeutics
7.1 Introduction
7.2 Challenges in the Development of Biotech Products
7.3 Large Molecule-Specific Challenges Result in
Altered Patterns of Clinical Success
7.4 Potential Cost Savings from Terminating Projects
Earlier
7.5 Improving Survivability of Drug Candidates
7.6 Preclinical Development of Recombinant Molecules
7.7 Case Study: NGF for the Treatment of Diabetic
Peripheral Neuropathy
7.8 Risk Management Strategy - Lessons Learned
7.9 Determination of the Dose Range Based on PK/PD
7.10 Determination of the Therapeutic Window Based
on Toxicity
7.11 Analyzing and Minimizing Immunogenicity
7.12 Implications for Clinical Development
7.13 Defining Early Go/No-Go Criteria
7.14 Conclusions
7.15 Questions & Answers
8. Creating Value via Better Resource Allocation
8.1 The Creative and the Analytical in Decision-Making
8.2 Balanced Resource Allocation to Improve the
Decision-Making Process
8.3 Resource Constraints Require Difficult Decisions
8.4 SmithKline Beecham's Approaches to Resource
Allocation
8.5 SmithKline Beecham's Approaches to Asset
Evaluation
8.6 Portfolio Management in a Smart Organization
8.7 Case Study: The Oncology Project
8.8 Increasing Shareholder Value from Portfolio
Investment
8.9 Organizational Buy-In
8.10 Effective Portfolio Management
Pricing:
All 3 Volumes - $2,490
Volume 1 - High Throughput Screening Assays- $1,290
Volume 2 - In Silico Biology - $1,290
Volume 3 - Early Compound Attrition - $1,290
