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Related Experiment Videos

Computational methods to predict drug safety liabilities.

S K Durham1, G M Pearl

  • 1Lead Safety Assessment, Bristol-Myers Squibb PRI, PO Box 4000, Princeton, NJ 08543, USA. stephen.durham@bms.com

Current Opinion in Drug Discovery & Development
|December 1, 2001
PubMed
Summary
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Computational toxicology methods predict drug safety risks. This review evaluates commercial programs like TOPKAT, MULTICASE, and DEREK, suggesting improvements for early drug discovery liability assessment.

Area of Science:

  • Pharmacology and Toxicology
  • Computational Chemistry
  • Drug Discovery Science

Background:

  • Predicting drug safety liabilities early in development is crucial.
  • Computational toxicology offers a promising approach to identify potential risks.
  • Existing commercial predictive toxicology programs require thorough evaluation.

Purpose of the Study:

  • To review computational methods for predicting drug safety liabilities.
  • To critically assess the strengths and weaknesses of TOPKAT, MULTICASE, and DEREK.
  • To propose strategies for integrating these tools into early drug discovery.

Main Methods:

  • Review of existing literature on computational toxicology.
  • Analysis of commercial predictive toxicology software (TOPKAT, MULTICASE, DEREK).

Related Experiment Videos

  • Evaluation of program performance using pharmaceutical datasets.

    • Main Results:

    • Identification of specific strengths and limitations for each commercial program.
    • Experiences and evaluations incorporating multiple predictive toxicology programs.
    • Assessment of the utility of these programs as early-stage liability filters.

    Conclusions:

    • Commercial predictive toxicology programs can serve as valuable early filters.
    • Improvements are needed for individual programs to enhance accuracy and utility.
    • Integrated strategies utilizing multiple programs can optimize drug safety liability assessment.