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Quantitative and qualitative models for carcinogenicity prediction for non-congeneric chemicals using CP ANN method for regulatory uses

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Abstract

The new European chemicals regulation Registration, Evaluation, Authorization and Restriction of Chemicals entered into force in June 2007 and accelerated the development of quantitative structure–activity relationship (QSAR) models for a variety of endpoints, including carcinogenicity. Here, we would like to present quantitative (continuous) and qualitative (categorical) models for non-congeneric chemicals for prediction of carcinogenic potency. A dataset of 805 substances was obtained after a preliminary screening of findings of rodent carcinogenicity for 1,481 chemicals accessible via Distributed Structure-Searchable Toxicity (DSSTox) Public Database Network originated from the Lois Gold Carcinogenic Potency Database (CPDB). Twenty seven two-dimensional MDL descriptors were selected using Kohonen mapping and principal component analysis. The counter propagation artificial neural network (CP ANN) technique was applied. Quantitative models were developed exploring the relationship between the experimental and predicted carcinogenic potency expressed as a tumorgenic dose TD50 for rats. The obtained models showed low prediction power with correlation coefficient less than 0.5 for the test set. In the next step, qualitative models were developed. We found that the qualitative models exhibit good accuracy for the training set (92%). The model demonstrated good predicted performance for the test set. It was obtained accuracy (68%), sensitivity (73%), and specificity (63%). We believe that CP ANN method is a good in silico approach for modeling and predicting rodent carcinogenicity for non-congeneric chemicals and may find application for o ther toxicological endpoints.

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Abbreviations

REACH:

Registration, Evaluation, Authorization and Restriction of Chemicals

QSAR:

Quantitative structure–activity relationship

DSSTox:

Distributed Structure-Searchable Toxicity

CPDB:

Lois Gold Carcinogenic Potency Database

PCA:

Principal component analysis

CP ANN:

Counter propagation artificial neural network

TD50 :

Tumorgenic dose

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Authors and Affiliations

  1. National Institute of Chemistry, Hajdrihova 19, 1001, Ljubljana, Slovenia

    Natalja Fjodorova, Marjan Vračko, Marjan Tušar, Aneta Jezierska & Marjana Novič

  2. Faculty of Chemistry, University of Wrocław, 14 F. Joliot-Curie, 50-383, Wrocław, Poland

    Aneta Jezierska

  3. UFZ Department of Ecological Chemistry, Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318, Leipzig, Germany

    Ralph Kühne & Gerrit Schüürmann

  4. Institute for Organic Chemistry, Technical University Bergakademie Freiberg, Leipziger Strasse 29, 09596, Freiberg, Germany

    Gerrit Schüürmann

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  1. Natalja Fjodorova
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  2. Marjan Vračko
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Correspondence to Natalja Fjodorova.

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Fjodorova, N., Vračko, M., Tušar, M. et al. Quantitative and qualitative models for carcinogenicity prediction for non-congeneric chemicals using CP ANN method for regulatory uses. Mol Divers 14, 581–594 (2010). https://doi.org/10.1007/s11030-009-9190-4

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