Research Informatics for Drug Discovery

Published by: AdvanceTech Monitor

Published: Oct. 1, 2000 - 230 Pages


Table of Contents


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 


Abstract

This report addresses information technologies to optimize the discovery-development interface, knowledge-driven R&D, the information spine, data integration, data manipulation, the data-warehouse controversy and other debates, such as discovery (exploratory) versus development (confirmatory) data.

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