Reportlinker Adds Data Mining in Drug Development and Translational Medicine
Advertisement
http://www.reportlinker.com/p0156559/Reportlinker-Adds-Data-Mining-in-Drug-Development-and-Translational-Medicine.html#utm_source=prnewswire&utm_medium=pr&utm_campaign=prnewswire
Advertisement
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:
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"
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
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
To order this report:
Reportlinker Adds Data Mining in Drug Development and Translational Medicine
http://www.reportlinker.com/p0156559/Reportlinker-Adds-Data-Mining-in-Drug-Development-and-Translational-Medicine.html#utm_source=prnewswire&utm_medium=pr&utm_campaign=prnewswire
More market research reports here!
Contact:
Nicolas Bombourg
Reportlinker
Email: [email protected]
US: (805)652-2626
Intl: +1 805-652-2626
-- 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
SOURCE Reportlinker
