Abstract
Compound activity identification is the primary goal in high-throughput screening (HTS) assays. However, assay artifacts including both systematic (e.g., compound auto-fluorescence) and nonsystematic (e.g., noise) complicate activity interpretation. In addition, other than the traditional potency parameter, half-maximal effect concentration (EC50), additional activity parameters (e.g., point-of-departure, POD) could be derived from HTS data for activity profiling. A data analysis pipeline has been developed to handle the artifacts and to provide compound activity characterization with either binary or continuous metrics. This chapter outlines the steps in the pipeline using Tox21 glucocorticoid receptor (GR) β-lactamase assays, including the formats to identify either agonists or antagonists, as well as the counter-screen assays for identifying artifacts as examples. The steps can be applied to other lower-throughput assays with concentration-response data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Macarron R, Banks MN, Bojanic D et al (2011) Impact of high-throughput screening in biomedical research. Nat Rev Drug Discov 10:188–195. doi:10.1038/nrd3368
Judson RS, Houck KA, Kavlock RJ et al (2009) In vitro screening of environmental chemicals for targeted testing prioritization: the ToxCast project. Environ Health Perspect 118:485–492. doi:10.1289/ehp.0901392
Tice RR, Austin CP, Kavlock RJ, Bucher JR (2013) Improving the human hazard characterization of chemicals: a Tox21 update. Environ Health Perspect 121:756–765. doi:10.1289/ehp.1205784
Shockley KR (2014) Quantitative high-throughput screening data analysis: challenges and recent advances. Drug Discov Today. doi:10.1016/j.drudis.2014.10.005
Sedykh A, Zhu H, Tang H et al (2011) Use of in vitro HTS-derived concentration–response data as biological descriptors improves the accuracy of QSAR models of in vivo toxicity. Environ Health Perspect 119:364–370. doi:10.1289/ehp.1002476
Simeonov A, Jadhav A, Thomas CJ et al (2008) Fluorescence spectroscopic profiling of compound libraries. J Med Chem 51:2363–2371. doi:10.1021/jm701301m
Imbert P-E, Unterreiner V, Siebert D et al (2007) Recommendations for the reduction of compound artifacts in time-resolved fluorescence resonance energy transfer assays. Assay Drug Dev Technol 5:363–372. doi:10.1089/adt.2007.073
Thorne N, Shen M, Lea WA et al (2012) Firefly luciferase in chemical biology: a compendium of inhibitors, mechanistic evaluation of chemotypes, and suggested use as a reporter. Chem Biol 19:1060–1072. doi:10.1016/j.chembiol.2012.07.015
Feng BY, Simeonov A, Jadhav A et al (2007) A high-throughput screen for aggregation-based inhibition in a large compound library. J Med Chem 50:2385–2390. doi:10.1021/jm061317y
Hsieh J-H, Sedykh A, Huang R et al (2015) A data analysis pipeline accounting for artifacts in Tox21 quantitative high-throughput screening assays. J Biomol Screen 1087057115581317. doi: 10.1177/1087057115581317
Sedykh A (2015) Curvep. https://github.com/sedykh/curvep. Accessed 3 June 2016.
Qureshi SA (2007) Beta-lactamase: an ideal reporter system for monitoring gene expression in live eukaryotic cells. BioTechniques 42:91–96
Huang R, Xia M, Cho M-H et al (2011) Chemical genomics profiling of environmental chemical modulation of human nuclear receptors. Environ Health Perspect 119:1142–1148. doi:10.1289/ehp.1002952
Filer DL, Kothiya P, Setzer WR et al (2015) EPA pipeline overview. https://www.epa.gov/sites/production/files/2015-08/documents/pipeline_overview.pdf. Accessed 25 March 2016
Behl M, Hsieh J-H, Shafer TJ, Mundy WR, Rice JR, Boyd WA et al Use of alternative assays to identify and prioritize organophosphorus flame retardants for potential developmental and neurotoxicity. Neurotoxicology and Teratology; doi:10.1016/j.ntt.2015.09.003
Huang R, Sakamuru S, Martin MT et al (2014) Profiling of the Tox21 10K compound library for agonists and antagonists of the estrogen receptor alpha signaling pathway. Sci Rep. doi:10.1038/srep05664
Judson RS (2014) ToxCast data processing overview. http://epa.gov/comptox/download_files/chemical_prioritization/Judson_CoP_Dec2014.pdf.Accessed 14 Aug. 2015
Schorpp K, Rothenaigner I, Salmina E et al (2014) Identification of small-molecule frequent hitters from alphascreen high-throughput screens. J Biomol Screen 19:715–726. doi:10.1177/1087057113516861
Baell JB, Holloway GA (2010) New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem 53:2719–2740. doi:10.1021/jm901137j
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media New York
About this protocol
Cite this protocol
Hsieh, JH. (2016). Accounting Artifacts in High-Throughput Toxicity Assays. In: Zhu, H., Xia, M. (eds) High-Throughput Screening Assays in Toxicology. Methods in Molecular Biology, vol 1473. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6346-1_15
Download citation
DOI: https://doi.org/10.1007/978-1-4939-6346-1_15
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-6344-7
Online ISBN: 978-1-4939-6346-1
eBook Packages: Springer Protocols