Literature DB >> 21565999

Predictive model of rat reproductive toxicity from ToxCast high throughput screening.

Matthew T Martin1, Thomas B Knudsen, David M Reif, Keith A Houck, Richard S Judson, Robert J Kavlock, David J Dix.   

Abstract

The U.S. Environmental Protection Agency's ToxCast research program uses high throughput screening (HTS) for profiling bioactivity and predicting the toxicity of large numbers of chemicals. ToxCast Phase I tested 309 well-characterized chemicals in more than 500 assays for a wide range of molecular targets and cellular responses. Of the 309 environmental chemicals in Phase I, 256 were linked to high-quality rat multigeneration reproductive toxicity studies in the relational Toxicity Reference Database. Reproductive toxicants were defined here as having achieved a reproductive lowest-observed-adverse-effect level of less than 500 mg kg(-1) day(-1). Eight-six chemicals were identified as reproductive toxicants in the rat, and 68 of those had sufficient in vitro bioactivity to model. Each assay was assessed for univariate association with the identified reproductive toxicants. Significantly associated assays were linked to gene sets and used for the subsequent predictive modeling. Using linear discriminant analysis and fivefold cross-validation, a robust and stable predictive model was produced capable of identifying rodent reproductive toxicants with 77% ± 2% and 74% ± 5% (mean ± SEM) training and test cross-validation balanced accuracies, respectively. With a 21-chemical external validation set, the model was 76% accurate, further indicating the model's potential for prioritizing the many thousands of environmental chemicals with little to no hazard information. The biological features of the model include steroidal and nonsteroidal nuclear receptors, cytochrome P450 enzyme inhibition, G protein-coupled receptors, and cell signaling pathway readouts-mechanistic information suggesting additional targeted, integrated testing strategies and potential applications of in vitro HTS to risk assessment.

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Year:  2011        PMID: 21565999     DOI: 10.1095/biolreprod.111.090977

Source DB:  PubMed          Journal:  Biol Reprod        ISSN: 0006-3363            Impact factor:   4.285


  40 in total

1.  Predictive modeling of chemical hazard by integrating numerical descriptors of chemical structures and short-term toxicity assay data.

Authors:  Ivan Rusyn; Alexander Sedykh; Yen Low; Kathryn Z Guyton; Alexander Tropsha
Journal:  Toxicol Sci       Date:  2012-03-02       Impact factor: 4.849

2.  QSAR modeling: where have you been? Where are you going to?

Authors:  Artem Cherkasov; Eugene N Muratov; Denis Fourches; Alexandre Varnek; Igor I Baskin; Mark Cronin; John Dearden; Paola Gramatica; Yvonne C Martin; Roberto Todeschini; Viviana Consonni; Victor E Kuz'min; Richard Cramer; Romualdo Benigni; Chihae Yang; James Rathman; Lothar Terfloth; Johann Gasteiger; Ann Richard; Alexander Tropsha
Journal:  J Med Chem       Date:  2014-01-06       Impact factor: 7.446

Review 3.  Chemical compounds from anthropogenic environment and immune evasion mechanisms: potential interactions.

Authors:  Julia Kravchenko; Emanuela Corsini; Marc A Williams; William Decker; Masoud H Manjili; Takemi Otsuki; Neetu Singh; Faha Al-Mulla; Rabeah Al-Temaimi; Amedeo Amedei; Anna Maria Colacci; Monica Vaccari; Chiara Mondello; A Ivana Scovassi; Jayadev Raju; Roslida A Hamid; Lorenzo Memeo; Stefano Forte; Rabindra Roy; Jordan Woodrick; Hosni K Salem; Elizabeth P Ryan; Dustin G Brown; William H Bisson; Leroy Lowe; H Kim Lyerly
Journal:  Carcinogenesis       Date:  2015-05-22       Impact factor: 4.944

4.  Mechanism-Driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data.

Authors:  Linlin Zhao; Daniel P Russo; Wenyi Wang; Lauren M Aleksunes; Hao Zhu
Journal:  Toxicol Sci       Date:  2020-04-01       Impact factor: 4.849

5.  Microengineered cell and tissue systems for drug screening and toxicology applications: Evolution of in-vitro liver technologies.

Authors:  O B Usta; W J McCarty; S Bale; M Hegde; R Jindal; A Bhushan; I Golberg; M L Yarmush
Journal:  Technology (Singap World Sci)       Date:  2015-03

6.  An Intuitive Approach for Predicting Potential Human Health Risk with the Tox21 10k Library.

Authors:  Nisha S Sipes; John F Wambaugh; Robert Pearce; Scott S Auerbach; Barbara A Wetmore; Jui-Hua Hsieh; Andrew J Shapiro; Daniel Svoboda; Michael J DeVito; Stephen S Ferguson
Journal:  Environ Sci Technol       Date:  2017-09-06       Impact factor: 9.028

7.  Phenotypic screening of the ToxCast chemical library to classify toxic and therapeutic mechanisms.

Authors:  Nicole C Kleinstreuer; Jian Yang; Ellen L Berg; Thomas B Knudsen; Ann M Richard; Matthew T Martin; David M Reif; Richard S Judson; Mark Polokoff; David J Dix; Robert J Kavlock; Keith A Houck
Journal:  Nat Biotechnol       Date:  2014-05-18       Impact factor: 54.908

8.  Screening for angiogenic inhibitors in zebrafish to evaluate a predictive model for developmental vascular toxicity.

Authors:  Tamara Tal; Claire Kilty; Andrew Smith; Carlie LaLone; Brendán Kennedy; Alan Tennant; Catherine W McCollum; Maria Bondesson; Thomas Knudsen; Stephanie Padilla; Nicole Kleinstreuer
Journal:  Reprod Toxicol       Date:  2016-12-19       Impact factor: 3.143

9.  From the Cover: An Animal-Free In Vitro Three-Dimensional Testicular Cell Coculture Model for Evaluating Male Reproductive Toxicants.

Authors:  Lei Yin; Hongye Wei; Shenxuan Liang; Xiaozhong Yu
Journal:  Toxicol Sci       Date:  2017-10-01       Impact factor: 4.849

Review 10.  Integrative approaches for predicting in vivo effects of chemicals from their structural descriptors and the results of short-term biological assays.

Authors:  Yen Sia Low; Alexander Yeugenyevich Sedykh; Ivan Rusyn; Alexander Tropsha
Journal:  Curr Top Med Chem       Date:  2014       Impact factor: 3.295

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