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

Nature Biotechnology volume 32, pages 583–591 (2014)Cite this article

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Abstract

Addressing the safety aspects of drugs and environmental chemicals has historically been undertaken through animal testing. However, the quantity of chemicals in need of assessment and the challenges of species extrapolation require the development of alternative approaches. Our approach, the US Environmental Protection Agency's ToxCast program, utilizes a large suite of in vitro and model organism assays to interrogate important chemical libraries and computationally analyze bioactivity profiles. Here we evaluated one component of the ToxCast program, the use of primary human cell systems, by screening for chemicals that disrupt physiologically important pathways. Chemical-response signatures for 87 endpoints covering molecular functions relevant to toxic and therapeutic pathways were generated in eight cell systems for 641 environmental chemicals and 135 reference pharmaceuticals and failed drugs. Computational clustering of the profiling data provided insights into the polypharmacology and potential off-target effects for many chemicals that have limited or no toxicity information. The endpoints measured can be closely linked to in vivo outcomes, such as the upregulation of tissue factor in endothelial cell systems by compounds linked to the risk of thrombosis in vivo. Our results demonstrate that assaying complex biological pathways in primary human cells can identify potential chemical targets, toxicological liabilities and mechanisms useful for elucidating adverse outcome pathways.

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Figure 1: Overview of responses for all chemicals and endpoints.
Figure 2: Comparison of endpoints from the 3C system for cluster 28 containing estrogen receptor (ER) agonists (blue) and cluster 48 containing estrogen receptor antagonists/selective estrogen receptor modulators (red).
Figure 3: Function similarity map for 135 failed pharmaceutical compounds.
Figure 4: Distribution of mechanism class decision values (DV) for all test concentrations of all compounds shown as self-organizing maps in a trellis plot conditioned by mechanism class DV.

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Acknowledgements

We thank our pharmaceutical industry partners Pfizer, GlaxoSmithKline, Roche, Merck, Sanofi and Astellas for donating 135 failed drugs and providing feedback on the manuscript, and W. Casey, L. Urban, C. Wood and K. Crofton for helpful suggestions on the manuscript. The EPA, through its Office of Research and Development, funded and managed the research described here. The views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of the US Environmental Protection Agency.

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Authors and Affiliations

  1. National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina, USA

    Nicole C Kleinstreuer, Thomas B Knudsen, Ann M Richard, Matthew T Martin, David M Reif, Richard S Judson, David J Dix, Robert J Kavlock & Keith A Houck

  2. a division of DiscoveRx, BioSeek, Inc., S. San Francisco, California, USA

    Jian Yang, Ellen L Berg & Mark Polokoff

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Contributions

R.J.K., D.J.D. and K.A.H. conceived and supervised the ToxCast project. A.M.R. oversaw chemical management. D.M.R., M.T.M. and R.S.J. designed and operated the data analysis workflow. N.C.K. and K.A.H. wrote the manuscript with editing by T.B.K., R.J.K., J.Y. and E.L.B. J.Y., M.P. and E.L.B. performed the experimental work. N.C.K., J.Y., K.A.H. and E.L.B. carried out the data analysis specific to this manuscript.

Corresponding author

Correspondence to Keith A Houck.

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Competing interests

J.Y., M.P. and E.L.B. are employees of BioSeek, a Division of DiscoveRx, Inc. and performed the bioassays and assisted with analysis of the results.

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Kleinstreuer, N., Yang, J., Berg, E. et al. Phenotypic screening of the ToxCast chemical library to classify toxic and therapeutic mechanisms. Nat Biotechnol 32, 583–591 (2014). https://doi.org/10.1038/nbt.2914

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