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Data Mining in Drug Development and Translational Medicine

Published by: CHI Insight Pharma Reports

Published: Jul. 1, 2009 - 114 Pages


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



Abstract

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.

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