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Machine learning models for rat multigeneration reproductive toxicity prediction

Reproductive toxicity is one of the prominent endpoints in the risk assessment of environmental and industrial chemicals. Due to the complexity of the reproductive system, traditional reproductive toxicity testing in animals, especially guideline multigeneration reproductive toxicity studies, take a...

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Autores principales: Liu, Jie, Guo, Wenjing, Dong, Fan, Aungst, Jason, Fitzpatrick, Suzanne, Patterson, Tucker A., Hong, Huixiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9552001/
https://www.ncbi.nlm.nih.gov/pubmed/36238576
http://dx.doi.org/10.3389/fphar.2022.1018226
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author Liu, Jie
Guo, Wenjing
Dong, Fan
Aungst, Jason
Fitzpatrick, Suzanne
Patterson, Tucker A.
Hong, Huixiao
author_facet Liu, Jie
Guo, Wenjing
Dong, Fan
Aungst, Jason
Fitzpatrick, Suzanne
Patterson, Tucker A.
Hong, Huixiao
author_sort Liu, Jie
collection PubMed
description Reproductive toxicity is one of the prominent endpoints in the risk assessment of environmental and industrial chemicals. Due to the complexity of the reproductive system, traditional reproductive toxicity testing in animals, especially guideline multigeneration reproductive toxicity studies, take a long time and are expensive. Therefore, machine learning, as a promising alternative approach, should be considered when evaluating the reproductive toxicity of chemicals. We curated rat multigeneration reproductive toxicity testing data of 275 chemicals from ToxRefDB (Toxicity Reference Database) and developed predictive models using seven machine learning algorithms (decision tree, decision forest, random forest, k-nearest neighbors, support vector machine, linear discriminant analysis, and logistic regression). A consensus model was built based on the seven individual models. An external validation set was curated from the COSMOS database and the literature. The performances of individual and consensus models were evaluated using 500 iterations of 5-fold cross-validations and the external validation data set. The balanced accuracy of the models ranged from 58% to 65% in the 5-fold cross-validations and 45%–61% in the external validations. Prediction confidence analysis was conducted to provide additional information for more appropriate applications of the developed models. The impact of our findings is in increasing confidence in machine learning models. We demonstrate the importance of using consensus models for harnessing the benefits of multiple machine learning models (i.e., using redundant systems to check validity of outcomes). While we continue to build upon the models to better characterize weak toxicants, there is current utility in saving resources by being able to screen out strong reproductive toxicants before investing in vivo testing. The modeling approach (machine learning models) is offered for assessing the rat multigeneration reproductive toxicity of chemicals. Our results suggest that machine learning may be a promising alternative approach to evaluate the potential reproductive toxicity of chemicals.
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spelling pubmed-95520012022-10-12 Machine learning models for rat multigeneration reproductive toxicity prediction Liu, Jie Guo, Wenjing Dong, Fan Aungst, Jason Fitzpatrick, Suzanne Patterson, Tucker A. Hong, Huixiao Front Pharmacol Pharmacology Reproductive toxicity is one of the prominent endpoints in the risk assessment of environmental and industrial chemicals. Due to the complexity of the reproductive system, traditional reproductive toxicity testing in animals, especially guideline multigeneration reproductive toxicity studies, take a long time and are expensive. Therefore, machine learning, as a promising alternative approach, should be considered when evaluating the reproductive toxicity of chemicals. We curated rat multigeneration reproductive toxicity testing data of 275 chemicals from ToxRefDB (Toxicity Reference Database) and developed predictive models using seven machine learning algorithms (decision tree, decision forest, random forest, k-nearest neighbors, support vector machine, linear discriminant analysis, and logistic regression). A consensus model was built based on the seven individual models. An external validation set was curated from the COSMOS database and the literature. The performances of individual and consensus models were evaluated using 500 iterations of 5-fold cross-validations and the external validation data set. The balanced accuracy of the models ranged from 58% to 65% in the 5-fold cross-validations and 45%–61% in the external validations. Prediction confidence analysis was conducted to provide additional information for more appropriate applications of the developed models. The impact of our findings is in increasing confidence in machine learning models. We demonstrate the importance of using consensus models for harnessing the benefits of multiple machine learning models (i.e., using redundant systems to check validity of outcomes). While we continue to build upon the models to better characterize weak toxicants, there is current utility in saving resources by being able to screen out strong reproductive toxicants before investing in vivo testing. The modeling approach (machine learning models) is offered for assessing the rat multigeneration reproductive toxicity of chemicals. Our results suggest that machine learning may be a promising alternative approach to evaluate the potential reproductive toxicity of chemicals. Frontiers Media S.A. 2022-09-27 /pmc/articles/PMC9552001/ /pubmed/36238576 http://dx.doi.org/10.3389/fphar.2022.1018226 Text en Copyright © 2022 Liu, Guo, Dong, Aungst, Fitzpatrick, Patterson and Hong. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pharmacology
Liu, Jie
Guo, Wenjing
Dong, Fan
Aungst, Jason
Fitzpatrick, Suzanne
Patterson, Tucker A.
Hong, Huixiao
Machine learning models for rat multigeneration reproductive toxicity prediction
title Machine learning models for rat multigeneration reproductive toxicity prediction
title_full Machine learning models for rat multigeneration reproductive toxicity prediction
title_fullStr Machine learning models for rat multigeneration reproductive toxicity prediction
title_full_unstemmed Machine learning models for rat multigeneration reproductive toxicity prediction
title_short Machine learning models for rat multigeneration reproductive toxicity prediction
title_sort machine learning models for rat multigeneration reproductive toxicity prediction
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9552001/
https://www.ncbi.nlm.nih.gov/pubmed/36238576
http://dx.doi.org/10.3389/fphar.2022.1018226
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