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Related Experiment Videos

Data warehouse implementation with clinical pharmacokinetic/pharmacodynamic data.

S P Koprowski1, J S Barrett

  • 1Clinical Pharmacokinetics Group, Drug Metabolism and Pharmacokinetics Department, DuPont Pharmaceuticals Company, Bridgewater, NJ 08807-0800, USA. jeff.barrett@aventis.com

International Journal of Clinical Pharmacology and Therapeutics
|March 26, 2002
PubMed
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A new data warehouse integrates human pharmacokinetic (PK) and pharmacodynamic (PD) data, enabling efficient data mining and analysis for drug development. This system streamlines data processing and supports electronic submissions.

Area of Science:

  • Pharmacokinetics and Pharmacodynamics
  • Data Warehousing
  • Clinical Data Management

Background:

  • Clinical pharmacokinetic (PK) and pharmacodynamic (PD) data are crucial for drug development.
  • Existing data management systems may lack integration and efficiency.
  • DuPont Pharmaceuticals' Clinical PK Group generated significant PK/PD data.

Purpose of the Study:

  • To create a centralized data warehouse for human PK and PD data.
  • To improve the accessibility and analysis of clinical PK/PD data.
  • To enhance data processing efficiencies within the DM&PK department.

Main Methods:

  • Developed an Oracle-based data warehouse integrated with LIMS and chromatography systems.
  • Utilized SAS/PH.Kinetics for GLP-compliant data analysis and data integration from various software.

Related Experiment Videos

  • Implemented a star schema data model with summarized data structures (MDDB, infomarts, datamarts).
  • Managed the warehouse using SAS/Warehouse Administrator for environment definition, data structuring, and refresh scheduling.

    • Main Results:

    • The warehouse integrates laboratory, biometric, PK, and PD data streams.
    • Detailed and summarized data structures facilitate data mining and querying with drill-down capabilities.
    • Web-enabled applications provide broad access to current data.
    • Automated mechanisms support the creation of study data listings and SAS transport files for electronic submissions.

    Conclusions:

    • The data warehouse enables efficient online analytical processing with centralized administration.
    • Future expansion will integrate data from all R&D phases, maximizing ROI and improving data processing efficiencies.
    • This system offers advantages over previous legacy systems in data management and processing.