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Computer-aided rodent carcinogenicity prediction.

Alexey A Lagunin1, John C Dearden, Dmitri A Filimonov

  • 1Institute of Biomedical Chemistry RAMS, Pogodinskaya Str. 10, Moscow 119121, Russia. alexey.lagunin@ibmc.msk.ru

Mutation Research
|August 23, 2005
PubMed
Summary
This summary is machine-generated.

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The Prediction Activity Spectra for Substances (PASS) program accurately predicts rodent carcinogenicity using chemical structures. Incorporating animal sex data improved predictions for male subjects.

Area of Science:

  • Toxicology
  • Computational Chemistry
  • Bioinformatics

Background:

  • Chemical carcinogenicity prediction is crucial for risk assessment.
  • Structure-activity relationship (SAR) models offer a computational approach.
  • The Prediction Activity Spectra for Substances (PASS) program utilizes SAR for biological activity prediction.

Purpose of the Study:

  • To evaluate the accuracy of the PASS program in predicting rodent carcinogenicity.
  • To assess the impact of incorporating animal species and sex data on prediction accuracy.

Main Methods:

  • Utilized structural formulas and SAR analysis of known carcinogens and non-carcinogens.
  • Employed two cross-validation techniques: leave-one-out (LOO CV) and leave-20%-out CV.
  • Used data from the National Toxicological Program (NTP) and Carcinogenic Potency Database (CPDB).

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Main Results:

  • LOO CV achieved 73% accuracy for 'equivocal' and 80% for 'evidence' categories in NTP data.
  • CPDB data yielded 89.9% accuracy with LOO CV and 63.4% with leave-20%-out CV.
  • Prediction accuracy increased for male animal data when species and sex information was included.

Conclusions:

  • PASS demonstrates significant potential for predicting rodent carcinogenicity.
  • The model's accuracy is influenced by the inclusion of specific animal sex data, particularly for males.
  • Further refinement of PASS may enhance its utility in toxicological assessments.