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1. Executive Summary
2. Introduction
3. In Silico Biology - Virtual Systems and Organs for the Assessment of
Drug Candidates
3.1 The Biology Data Chain
3.2 The Rationale for Biological Modeling
3.3 Single Cell Models
3.4 Identification of a Knowledge Gap
3.5 3-Dimensional Organ Models
3.6 Human Surface ECG
3.8 Clinical Issues to Detect QT Prolongation
3.9 Preclinical Issues to Detect QT Prolongation
3.10 The Challenge - Cardiac Toxicity
3.11 Computational Approach to Predict Cardiac
Toxicity
3.12 Case Study - Ventricular Response to an
Anti-Arrhythmic Compound
3.13 Single Cell Analysis
3.14 More Issues Related To Drug-Induced Cardiac
Toxicity
3.15 Conclusion
3.16 Questions & Answers
4. High-Performance Computing in Pharmaceutical R&D
4.1 Integration of High-Performance Computing into
R&D
4.2 Using High-Performance Computing to Reduce
Development Times and Risk
4.3 Case Studies of Two Anticancer Compounds
I. BNP7787
II. BNP1350
4.4 Mechanism-Based Drug Discovery: Role of
Physics-Based Supercomputer Simulations
4.5 BioNumerik's Supercomputing Center
4.6 Questions & Answers
5. Computational Pharmacokinetics for Drug Discovery
5.1 Interfacing High Throughput Pharmacokinetics
(HTPkS) and PK-Informatics
5.2 Discovery and Selection of Small Molecule Drug
Therapies
5.3 Revolutionizing Drug Discovery Through Early
Pharmacokinetic Studies
5.4 Gains from Early Pharmacokinetic Studies
5.5 In Vitro Pharmacokinetic Screening
5.6 Virtual or In Silico Pharmacokinetic Screening
5.7 In Vitro ADME Screening
5.8 In Silico Prediction of Pharmacokinetics - The
IDEA Model
5.9 A Consortium Approach - The IDEA Consortium
5.10 The IDEA Model - Benefits of Simulation
5.11 Questions & Answers
6. A Consortium Approach to Building a Toxicology Database from Proprietary
Compounds
6.1 Approaches to Predictive Toxicology
6.2 Predictive Toxicology Databases
6.3 Plans for a Shared Industry Database for
Toxicology
6.4 The Ideal Database for Predictive Toxicology
6.5 Globalization and International Harmonization
6.6 Approaches to Assembling and Sharing the
Toxicology Database
6.7 The High Production Volume Chemical Program
6.8 The IUCLID Candidate Database at the ECB
6.9 An Accelerated Approach to Predictive
Toxicology
6.10 Questions & Answers
7. Use of FDA Databases and Computational Toxicology to Predict Toxicity
7.1 FDA-CDER is a Unique Source of Scientific
Information
7.2 Mission of the Regulatory Research & Analysis
Staff (RRAS)
7.3 The Agency Information Cycle
7.4 The Carcinogenicity Database - The First
Database for Computational Toxicology
7.5 Collaborative R&D Agreement with Multicase
7.6 Factors Used to Predict Carcinogenicity
7.7 Validation of the Predictive Carcinogenicity
Model
7.8 Computational Toxicology Applications
7.9 FDA-RRAS Long-Term Objectives
7.10 Questions & Answers
8. Using State-of-the Art IT Systems to Integrate Preclinical Evaluations into
Discovery
Part I - Dealing with the Data Deluge
8.1 Dealing with the Quantum Jump in Amounts of
Data
8.2 Challenges in Discovery and Informatics
8.3 The Scale of the Data Deluge
8.4 Responses to Challenges in Discovery and
Informatics
8.5 Data Visualization
8.6 Enabling Technologies
Part II - Materials Management - Pitfalls and Bottlenecks
8.7 Compound Management Challenge
8.8 Solutions to Materials and Substance Handling
8.9 New Technologies Create Compound Overload
8.10 Bottlenecks and the "Hurry-Up-and-Wait"
Syndrome
8.11 Compound Management: The Hub of Drug
Research
8.12 A Typical Screening Procedure
8.13 Inventory Management Software: The Missing
Link
8.14 OPTIMA - A Compound Management Solution
8.15 Compound Management Solutions Drive
Process Improvement
8.16 Open And Integrated Operations
8.17 Conclusion
8.18 Summary
9. Better Decision-Making Through Data-Mining and Visualization
9.1 Managing the Increasing Volume and
Complexity of Data
9.2 Moving from "Analog" to "Digital" Exchange of
Knowledge
9.3 Scalable Systems for Data Storage and Analysis
9.4 Capturing Lessons and Knowledge
9.5 Evolution from Static Data Repositories to
Interactive Data-mining and Visualization
9.6 MineSet as a Tool for Data-mining and
Visualization
9.7 Building Predictive Models Using Visualization of
Data
9.8 Building Predictive Models
9.9 Applications of Data-mining and Visualization to
Discovery Research
9.10 Visualization of Genomics Data
9.11 Visualization of Chemical Data
9.12 Visualization of Clinical Trial Data
9.13 Estimating Errors and Testing Assumptions in
Model Design
9.14 From Information Overload to Insight
Table
of Contents
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
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