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A model validation and consensus building environment.

T Abshear1, G M Banik, M L D'Souza

  • 1Bio-Rad Laboratories, 3316 Spring Garden Street, Philadelphia, PA 19104, USA. Michelle_D'Souza@Bio-Rad.com

SAR and QSAR in Environmental Research
|July 4, 2006
PubMed
Summary
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In silico tools can predict drug absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, reducing drug development failures. Bio-Rad

Area of Science:

  • Drug Discovery and Development
  • Computational Chemistry
  • Pharmacokinetics and Toxicology

Background:

  • Over 50% of drug development failures stem from poor absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties.
  • In silico prediction of ADME/Tox and physicochemical properties offers a strategy to mitigate attrition rates in pharmaceutical R&D.
  • Reliability of in silico ADME/Tox predictions remains a significant challenge in the field.

Purpose of the Study:

  • To introduce a computational environment, KnowItAll, designed to enhance the reliability of in silico ADME/Tox predictions.
  • To provide tools for validating existing ADME/Tox predictors and developing consensus models.
  • To address the critical need for dependable in silico tools in early-stage drug discovery.

Main Methods:

Related Experiment Videos

  • Development of the KnowItAll computational platform by Bio-Rad Laboratories, Inc.
  • Integration of multiple ADME/Tox predictors within the KnowItAll environment.
  • Implementation of validation capabilities for predictors using in-house data and the creation of consensus models.
  • Support for both real number and categorical classification predictions.

Main Results:

  • The KnowItAll system provides a validated computational environment for in silico ADME/Tox prediction.
  • Users can validate individual predictors and build superior consensus models.
  • The platform supports diverse prediction types, including real number and categorical classification.

Conclusions:

  • The KnowItAll system offers a robust solution to improve the reliability of in silico ADME/Tox predictions.
  • This technology has the potential to significantly reduce drug development attrition rates.
  • KnowItAll empowers researchers to prioritize drug candidates more effectively through reliable property predictions.