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Related Experiment Videos

Quantitative structure-activity relationships for predicting mutagenicity and carcinogenicity.

Grace Patlewicz1, Rosemary Rodford, John D Walker

  • 1Safety and Environmental Assurance Centre, SEAC, Unilever Colworth, Colworth House, Sharnbrook, Bedford, Bedfordshire MK44 1LQ, United Kingdom. grace.patlewicz@unilever.com

Environmental Toxicology and Chemistry
|August 20, 2003
PubMed
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Quantitative structure-activity relationships (QSARs) are crucial for predicting chemical mutagenicity and carcinogenicity. This review covers key QSAR models and expert systems used for chemical safety assessment.

Area of Science:

  • Toxicology
  • Computational Chemistry
  • Cheminformatics

Background:

  • Quantitative Structure-Activity Relationships (QSARs) are essential for predicting chemical mutagenicity and carcinogenicity.
  • Existing QSAR models are often limited to specific chemical classes like aromatic amines and heteroaromatic nitro compounds.

Purpose of the Study:

  • To review existing QSARs for predicting mutagenicity and carcinogenicity.
  • To provide an overview of major computerized systems and expert systems developed for this purpose.

Main Methods:

  • Literature review of QSAR models for mutagenicity and carcinogenicity prediction.
  • Identification and summary of commercially available and other expert systems.

Main Results:

Related Experiment Videos

  • Four major commercial systems (DEREK, TOPKAT, CASE, Multicase) exist for predicting these endpoints.
  • Other expert systems include ADAPT, QSAR-ES, COMPACT, and COREPA.

Conclusions:

  • QSARs and expert systems are vital tools for screening chemical inventories for mutagenicity and carcinogenicity.
  • The development of these systems facilitates broader chemical safety assessments.