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市場調査レポート
薬剤開発におけるデータマイニングと展開医療
Data Mining in Drug Development and Translational Medicine
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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.
Much more clinical trial data are captured than are actually analyzed to build
the regulatory data file. Clinical databases can thus be mined for information
that the respective study was not explicitly designed to provide. Data Mining
in Drug Development and Translational Medicine describes how data mining from
investigational human trials can reveal hidden information that has the
potential to massively improve the understanding of drug mechanisms, the
efficacy and side effect behavior of drug candidates in various patient
subpopulations, and even the integrity of clinical investigators. We look at
text mining of literature and patent databases, which offers the possibility
for knowledge discovery concerning activity in a particular field of
therapeutic development from many different angles.
Pharmacovigilance is a field where large volumes of interconnected data have
to be analyzed in many dimensions. We describe various databases used in
support of post-market drug safety evaluation, including those maintained by
the FDA, WHO, and EMEA. Data mining algorithms applied to pharmacovigilance
databases and efforts to bring separate databases into full compatibility with
one another are described. Case studies illustrating the use of data mining
and analysis to investigate relationships between marketed drugs and adverse
events are presented.
Data Mining in Drug Development and Translational Medicine concludes by
profiling the most significant vendors that either offer dedicated solutions
for data mining in drug development and pharmacovigilance, or provide more
general commercial data mining solutions that have been successfully adapted
and applied to these endeavors.
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
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