Home > Back to Publisher > Report Information

Data Mining in Drug Development and Translational Medicine

CHI Insight Pharma Reports
July 1, 2009
114 Pages - SKU: CHI2390510

Abstract Table of Contents Related Reports

The biopharmaceutical industry is grappling not only with sheer data volume but with the ability of researchers to extract information through identification and contextual analysis of those data that are relevant to a particular set of investigations. This report examines:

Techniques, technology, and software used in life science data mining
Data mining for early preclinical safety assessments
Data mining in clinical trials
Data mining in pharmacovigilance
Business models and solutions in drug development bioinformatics

The mountain of data generated and stored is growing ever-higher. The information content of life science data is multidimensional and not readily accessible by merely looking at the output. Unless such data can be put into proper context and interpreted—i.e., mined—their value is only in their potential. Data Mining in Drug Development and Translational Medicine examines data mining challenges and approaches in pharmaceutical R&D.

The pharmaceutical industry has made decisive moves to improve the predictiveness of early-stage drug safety testing. These efforts generate large amounts of data, in which the clue to safety-related, potential “red flags” can be buried. In this context we examine options for mining types of text data, “pathway mining” for pathway-related effects of a compound, and the multidimensional output of high-content screening methods. Also examined are approaches to mining data generated in preclinical trials for identification of toxicity signatures.

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”; 1.3. Who Mines Data Today…And For What?; 1.4. The Challenge of Life Science's Own Data Avalanche
Chapter 2 TECHNIQUES, TECHNOLOGY, AND SOFTWARE: 2.1. Capturing Data and Knowing Their Bias; 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
Chapter 4 DATA MINING IN CLINICAL TRIALS: 4.1. The Clinical Trial Database: Much More Than Meets the Eye; 4.2. Mining for Safety Signals in Clinical Trials; 4.3. Clinical Trial Data Mining for Drug 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; 5.3. Mining Adverse Event Databases; 5.4. Developments Shaping the Data Mining Environment in Pharmacovigilance
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

Data Mining in Drug Development and Translational Medicine

Share this report

Other tasks

Research Assistance

Related Markets

Drug Discovery Reports

Free Alert Me service