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Physiologically-based pharmacokinetic simulation modelling.

George M Grass1, Patrick J Sinko

  • 1LION bioscience, 9880 Campus Point Drive, San Diego, CA 92121, USA.

Advanced Drug Delivery Reviews
|March 30, 2002
PubMed
Summary
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Accurate ADME models are crucial for efficient drug selection, reducing costly failures. New genomic and proteomic advances necessitate rapid, reliable pharmacokinetic predictions for drug development.

Area of Science:

  • Pharmacology
  • Drug Discovery
  • Biotechnology

Background:

  • Drug selection is a major bottleneck in drug discovery and development.
  • Current methods for predicting drug performance in humans are unreliable, leading to high failure rates.
  • Advances in genomics, proteomics, and pharmacogenomics highlight the need for better predictive tools.

Purpose of the Study:

  • To emphasize the critical need for high-quality, rapid, and predictive ADME models.
  • To address the limitations of existing tools in supporting financial decisions during drug development.
  • To highlight the urgency for accurately computed pharmacokinetic properties in light of new biological insights.

Main Methods:

  • The abstract discusses the need for predictive ADME models.

Related Experiment Videos

  • It references advances in human genome sequencing, proteomics, and pharmacogenomics.
  • The focus is on the requirement for rapid computation of pharmacokinetic properties.

    • Main Results:

    • The abstract does not present specific results but outlines the problem and the need for solutions.
    • It implies that current systems for predicting human performance are inadequate.
    • The high historical failure rates in clinical trials are cited as evidence of this inadequacy.

    Conclusions:

    • There is an urgent need for reliable ADME models to improve drug selection.
    • Advances in omics technologies necessitate the development of rapid pharmacokinetic property computation.
    • Better predictive models are essential to support financial decisions and reduce drug development failures.