Journal Article

A Comprehensive Statistical Analysis of Predicting <i>In Vivo</i> Hazard Using High-Throughput <i>In Vitro</i> Screening

Russell S. Thomas, Michael B. Black, Lili Li, Eric Healy, Tzu-Ming Chu, Wenjun Bao, Melvin E. Andersen and Russell D. Wolfinger

in Toxicological Sciences

Volume 128, issue 2, pages 398-417
Published in print August 2012 | ISSN: 1096-6080
Published online April 2012 | e-ISSN: 1096-0929 | DOI:
A Comprehensive Statistical Analysis of Predicting In Vivo Hazard Using High-Throughput In Vitro Screening

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  • Medical Toxicology
  • Toxicology (Non-medical)


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Over the past 5 years, increased attention has been focused on using high-throughput in vitro screening for identifying chemical hazards and prioritizing chemicals for additional in vivo testing. The U.S. Environmental Protection Agency’s ToxCast program has generated a significant amount of high-throughput screening data allowing a broad-based assessment of the utility of these assays for predicting in vivo responses. In this study, a comprehensive cross-validation model comparison was performed to evaluate the predictive performance of the more than 600 in vitro assays from the ToxCast phase I screening effort across 60 in vivo endpoints using 84 different statistical classification methods. The predictive performance of the in vitro assays was compared and combined with that from chemical structure descriptors. With the exception of chronic in vivo cholinesterase inhibition, the overall predictive power of both the in vitro assays and the chemical descriptors was relatively low. The predictive power of the in vitro assays was not significantly different from that of the chemical descriptors and aggregating the assays based on genes reduced predictive performance. Prefiltering the in vitro assay data outside the cross-validation loop, as done in some previous studies, significantly biased estimates of model performance. The results suggest that the current ToxCast phase I assays and chemicals have limited applicability for predicting in vivo chemical hazards using standard statistical classification methods. However, if viewed as a survey of potential molecular initiating events and interpreted as risk factors for toxicity, the assays may still be useful for chemical prioritization.

Keywords: predictive toxicology; QSAR; chemical structure; high throughput screening; chronic toxicity; alternatives to animal testing; mode-of-action; ToxCast; developmental toxicity; reproductive toxicity

Journal Article.  12223 words.  Illustrated.

Subjects: Medical Toxicology ; Toxicology (Non-medical)

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