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Related Experiment Videos

Computational methods to estimate drug development parameters.

B L Podlogar1, I Muegge, L J Brice

  • 1Paratek Pharmaceuticals Inc, 75 Kneeland Street, Boston, MA 02111, USA. bpodlogar@Paratekpharm.com

Current Opinion in Drug Discovery & Development
|December 1, 2001
PubMed
Summary
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Computational methods can predict drug properties like bioavailability and toxicity. Choosing the right tools depends on project goals, timelines, and budget for effective drug discovery.

Area of Science:

  • Computational chemistry
  • Drug discovery
  • Pharmacokinetics

Background:

  • Numerous computational methods exist for predicting Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADME/Tox) properties.
  • These include estimations for oral bioavailability, solubility, metabolism, toxicity, pKa, and blood-brain barrier permeability.

Purpose of the Study:

  • To guide drug discovery organizations in selecting appropriate computational methods for ADME/Tox prediction.
  • To align method selection with organizational goals, timelines, and budget constraints.

Main Methods:

  • Discussion of practical limitations, ease of use, result utility, scope, and limitations of various computational methods.
  • Comparison of commercial software versus in-house developed solutions for ADME/Tox parameter estimation.

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  • Focus on methods integrable into high-throughput screening (HTS) paradigms.

    • Main Results:

    • Certain methods are better suited for early-stage drug design and prioritization within pharmaceutical teams.
    • Recommendations are provided for choosing between commercial and in-house computational tools.
    • Emphasis on a 'zero-infrastructure' approach for rapid ADME/Tox information generation.

    Conclusions:

    • Strategic selection of computational tools is crucial for efficient drug discovery.
    • Integration of selected methods into existing workflows can accelerate the design process.
    • A 'zero-infrastructure' approach facilitates early-stage ADME/Tox assessment, optimizing resource allocation.