Data Mining in Drug Development and Translational Medicine Overview

Table of Contents

Chapter 1

• THE NEED FOR DATA MINING IN DRUG DEVELOPMENT: NATURE AND OBJECTIVES

• 1.1. The Exponential Growth of Humankind' s Data Volume
• 1.2. Making Sense of Data: Ascent to the “Grand Picture”
• Learning About the Unexpected: Exploratory Data Analyses for Hypothesis Generation
• Seeking Specific Signatures: Data Mining for Hypothesis Testing
• 1.3. Who Mines Data Today...And For What?
• Strategic Marketing
• Financial Services and Tax Offices
• Military and Security Assessments
• Other Users of Data Mining Solutions
• 1.4. The Challenge of Life Science' s Own Data Avalanche
• Literature and Patent Texts
• Cheminformatics
• Sequence and Biomarker Information
• Modeling of Market Dynamics and Competitor Behavior

Chapter 2

• TECHNIQUES, TECHNOLOGY, AND SOFTWARE

• 2.1. Capturing Data and Knowing Their Bias
• Experimental and External Data
• 2.2. Building Data Warehouses from Disparate Sources
• 2.3. Text Mining: Semantics and Artificial Intelligence
• 2.4. Structure Searches in Digital Chemical Libraries
• 2.5. Image Mining: The Greatest Challenge
• 2.6. Machine Learning with Pharmaceutical and Biological Data
• 2.7. Visualization of Results: The Challenge of Meaningful Reporting
• 2.8. Standardization and Regulatory Compliance: CDISC' s SDTM and SEND

Chapter 3

• DATA MINING FOR EARLY PRECLINICAL SAFETY ASSESSMENTS

• 3.1. A Close Look at Text Data: Literature, Patents, and Databases
• 3.2. “Pathway Mining” for Model Building and Matching
• 3.3. High-Content Screening as a Data Feed
• 3.4. Seeking Signatures of Toxicity in Animal Data
• Behavioral Data: From Automated Counts to Video Mining
• Biomarker Response Assessments in Animal Studies
• Seeking Out and Interpreting Digital Pathology Data

Chapter 4

• DATA MINING IN CLINICAL TRIALS

• 4.1. The Clinical Trial Database: Much More Than Meets the Eye
• The “E-Trial”: The Key to Patient Record Mining in Near-Real Time
• Retrospective Mining of Completed Trials: The “Paper Legacy”
• Case Study: Statins and Amyotrophic Lateral Sclerosis
• 4.2. Mining for Safety Signals in Clinical Trials
• Premarket Safety Data Mining by Regulatory Agencies
• Hepatotoxicity
• QT Interval Prolongation
• 4.3. Clinical Trial Data Mining for Drug Response Signatures
• Genotype versus Phenotype: Identifying Potential Responders
• Image Registration: Mining Imaging Data for Response Signatures
• 4.4. Detection of Data Bias and Fraud
• 4.5. Correcting for Non-Compliance in Outpatient Trials
• 4.6. Mining the Clinical Literature for Optimizing Scientific Approaches and Business Development

Chapter 5

• DATA MINING IN PHARMACOVIGILANCE

• 5.1. The Challenges of Assessing Post-Marketing Drug Performance
• 5.2. Databases Supporting the Push for Post-Market Safety Evaluation
• AERS and VAERS: The FDA Adverse Event Reporting System
• VigiBase: The WHO Drug Safety Database
• The EudraVigilance Post-Authorization Module
• Prescription-Event Monitoring Databases
• Corporate Pharmacovigilance Databases
• 5.3. Mining Adverse Event Databases
• Basic Types of Mining Algorithms
• The Influence of Coding Terms and Direct Patient Reporting
• Case Studies and Promising Objectives
• Oseltamivir and Hallucinations
• Antipsychotics and Diabetic Events: An Effect of Chemical Structure?
• Statins and Psychiatry: A Confusing Story with a Long History
• Bisphosphonate Drugs and Osteonecrosis of the Jaw
• 5.4. Developments Shaping the Data Mining Environment in Pharmacovigilance
• The FDA' s Sentinel Initiative and the Reagan-Udall Foundation
• PROTECT - Method Development for Pharmacovigilance in Europe
• Electronic Health Records: A Future Key Factor for Data Collection

Chapter 6

• BUSINESS MODELS AND SOLUTIONS IN DRUG DEVELOPMENT BIOINFORMATICS

• 6.1. Phase Forward
• 6.2. ProSanos
• 6.3. AltraBio
• 6.4. ID Business Solutions (IDBS)
• 6.5. Strand Life Sciences
• 6.6. SPSS
• 6.7. PointCross
• 6.8. Aperio Technologies
• 6.9. Molecular Devices
• 6.10. Cambridge Cell Networks (CCNet)
• 6.11. InforSense
• 6.12. SAS Institute
• 6.13. Temis
• 6.14. Search Technology
• 6.15. TIBCO Software
• 6.16. Salford Systems

References

Company Index with Web Addresses

FIGURES

• The Knowledge Extraction Pyramid
• Development of Searches in the PubMed Internet Database, 1997 - 2007
• The Data Warehouse as a Hub in Translational Drug Research and Development
• Visualization of a Mining Query of the PubMed Literature Database
• Visualization of a Data Mining Result Using the Landscape Map Approach
• The Decision Tree for the Study by Ebbels et al.
• Representation of a Typical Clinical Data Mining Workflow
• Individual Case Safety Report (ICSR) Submissions to the EudraVigilance Clinical Trial Module (EVCTM) From Inception to January 2007
• The Data Integration Challenge in Clinical Data Mining
• Workflow Schematic for the Data Mining System Described by Cao et al.
• Vaccine Trials Activity Relative to Cancer Prevalence and Survival
• Reports Received (Solid Bars) and Entered (Patterned Bars) Into the AERS Database by Type of Report, 2000 - 2009 (Q1)
• Data Processing for Safety Signal Detection in the WHO VigiBase System
• Adverse Event Reports for Oseltamivir vs. Unexpectedness, 1997 - Q1/2008
• Specific Symptoms In Influenza Patients Treated with Oseltamivir for Whom “Abnormal Behavior” Had Been Reported
• Screenshot of an Analysis with Cambridge Cell Networks' ToxWiz Software
• TIBCO Spotfire DecisionSite Software for Preclinical Research
• A Window from the TIBCO Spotfire Clinical Trials Analysis Software