Repeated dose toxicity is of the utmost importance to characterize the toxicological profile of a chemical after repeated administration. Its evaluation refers to the Lowest-Observed-(Adverse)-Effect-Level (LO(A)EL) explicitly requested in several regulatory contexts, such as REACH and EC Regulation 1223/2009 on cosmetic products. So far in vivo tests have been the sole viable option to assess repeated dose toxicity. We report a customized k-nearest neighbors (k-NN) approach for predicting sub-chronic oral toxicity in rats. A training set of 254 chemicals was used to derive models whose robustness was challenged through leave-one-out cross-validation. Their predictive power was evaluated on an external dataset comprising 179 chemicals. Despite the intrinsically heterogeneous nature of the data, our models give promising results, with q<sup>2</sup> ≥ 0.632 and external r<sup>2</sup> ≥ 0.543. The confidence in prediction was ensured by implementing restrictive user-adjustable rules, excluding suspicious chemicals irrespective of the goodness in their prediction. Comparison with the very few LO(A)EL predictive models in the literature indicates that the results of the present analysis can be valuable in prioritizing the safety assessment of chemicals and thus making safe decisions and justifying waiving animal tests according to current regulations concerning chemical safety.

A k-NN algorithm for predicting oral sub-chronic toxicity in the rat

GADALETA, DOMENICO;CAROTTI, Angelo;NICOLOTTI, ORAZIO;
2014-01-01

Abstract

Repeated dose toxicity is of the utmost importance to characterize the toxicological profile of a chemical after repeated administration. Its evaluation refers to the Lowest-Observed-(Adverse)-Effect-Level (LO(A)EL) explicitly requested in several regulatory contexts, such as REACH and EC Regulation 1223/2009 on cosmetic products. So far in vivo tests have been the sole viable option to assess repeated dose toxicity. We report a customized k-nearest neighbors (k-NN) approach for predicting sub-chronic oral toxicity in rats. A training set of 254 chemicals was used to derive models whose robustness was challenged through leave-one-out cross-validation. Their predictive power was evaluated on an external dataset comprising 179 chemicals. Despite the intrinsically heterogeneous nature of the data, our models give promising results, with q2 ≥ 0.632 and external r2 ≥ 0.543. The confidence in prediction was ensured by implementing restrictive user-adjustable rules, excluding suspicious chemicals irrespective of the goodness in their prediction. Comparison with the very few LO(A)EL predictive models in the literature indicates that the results of the present analysis can be valuable in prioritizing the safety assessment of chemicals and thus making safe decisions and justifying waiving animal tests according to current regulations concerning chemical safety.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/185275
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