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Related Experiment Videos

From physicochemistry to absorption and distribution: predictive mechanistic modelling and computational tools.

Stefan Willmann1, Jörg Lippert, Walter Schmitt

  • 1Bayer Technology Services GmbH, Process Technology/Biophysics, 42096 Wuppertal, Germany. Stefan.Willmann@bayertechnology.com

Expert Opinion on Drug Metabolism & Toxicology
|August 23, 2006
PubMed
Summary

Pharmaceutical companies are using in silico models to predict drug absorption, distribution, metabolism, and excretion (ADME) properties early in discovery. These computational methods improve drug development efficiency and reliability.

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Area of Science:

  • Pharmacology and Drug Discovery
  • Computational Chemistry
  • Biopharmaceutical Sciences

Background:

  • The pharmaceutical industry invests heavily in high-throughput screening technologies for drug discovery.
  • Early and reliable determination of drug efficacy, availability, and safety is crucial for large compound libraries.
  • In silico methods are increasingly integrated to interpret and complement experimental in vitro data.

Purpose of the Study:

  • To discuss the contribution of in silico predictive models to optimizing the research and development process.
  • To highlight the advancements in rational predictive models for ADME properties.

Main Methods:

  • Development of rational predictive models for ADME properties.
  • Utilizing basic physicochemical input data and mechanistic descriptions of biophysical/biochemical processes.

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  • Implementation of in silico methods combining and interpreting experimental in vitro data.

    • Main Results:

    • Rational predictive models for ADME properties have been developed.
    • Some of these predictive models are commercially available (e.g., GastroPlus, PK-Map, PK-Sim).

    Conclusions:

    • In silico models offer a powerful approach to predict ADME properties early in drug discovery.
    • These computational tools contribute to a more optimized and reliable pharmaceutical research and development process.