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Toxicol Sci. 2019 Feb 1;167(2):484-495. doi: 10.1093/toxsci/kfy255.

Prediction of Acute Oral Systemic Toxicity Using a Multifingerprint Similarity Approach.

Author information

1
Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro," I-70126 Bari, Italy.
2
ScitoVation LLC, Research Triangle Park, North Carolina 27709.
3
Integrated Laboratory Systems, Morrisville, NC 27560.
4
Istituto Tumori IRCCS Giovanni Paolo II, 70124 Bari, Italy.

Abstract

The implementation of nonanimal approaches is of particular importance to regulatory agencies for the prediction of potential hazards associated with acute exposures to chemicals. This work was carried out in the framework of an international modeling initiative organized by the Acute Toxicity Workgroup (ATWG) of the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) with the participation of 32 international groups across government, industry, and academia. Our contribution was to develop a multifingerprints similarity approach for predicting five relevant toxicology endpoints related to the acute oral systemic toxicity that are: the median lethal dose (LD50) point prediction, the "nontoxic" (LD50 > 2000 mg/kg) and "very toxic" (LD50<50 mg/kg) binary classification, and the multiclass categorization of chemicals based on the United States Environmental Protection Agency and Globally Harmonized System of Classification and Labeling of Chemicals schemes. Provided by the ICCVAM's ATWG, the training set used to develop the models consisted of 8944 chemicals having high-quality rat acute oral lethality data. The proposed approach integrates the results coming from a similarity search based on 19 different fingerprint definitions to return a consensus prediction value. Moreover, the herein described algorithm is tailored to properly tackling the so-called toxicity cliffs alerting that a large gap in LD50 values exists despite a high structural similarity for a given molecular pair. An external validation set made available by ICCVAM and consisting in 2896 chemicals was employed to further evaluate the selected models. This work returned high-accuracy predictions based on the evaluations conducted by ICCVAM's ATWG.

PMID:
30371864
DOI:
10.1093/toxsci/kfy255

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