Abstract
Recent reports have noted that a number of compounds that block the human Ether-à-go-go related gene (hERG) ion channel also induce phospholipidosis (PLD). To explore a hypothesis explaining why most PLD inducers are also hERG inhibitors, a modeling approach was undertaken with data sets comprised of 4096 compounds assayed for hERG inhibition and 5490 compounds assayed for PLD induction. To eliminate the chemical domain effect, a filtered data set of 567 compounds tested in quantitative high-throughput screening (qHTS) format for both hERG inhibition and PLD induction was constructed. Partial least squares (PLS) modeling followed by 3D-SDAR mapping of the most frequently occurring bins and projection on to the chemical structure suggested that both adverse effects are driven by similar structural features, namely two aromatic rings and an amino group forming a three-center toxicophore. Non-parametric U-tests performed on the original 3D-SDAR bins indicated that the distance between the two aromatic rings is the main factor determining the differences in activity; at distances of up to about 5.5 Å, a phospholipidotic compound would also inhibit hERG, while at longer distances, a sharp reduction of the PLD-inducing potential leaves only a well-pronounced hERG blocking effect. The hERG activity itself diminishes after the distance between the centroids of the two aromatic rings exceeds 12.5 Å. Further comparison of the two toxicophores revealed that the almost identical aromatic rings to amino group distances play no significant role in distinguishing between PLD and hERG activity. The hypothesis that the PLD toxicophore appears to be a subset of the hERG toxicophore explains why about 80% of all phospholipidotic chemicals (the remaining 20% are thought to act via a different mechanism) also inhibit the hERG ion channel. These models were further validated in large-scale qHTS assays testing 1085 chemicals for their PLD-inducing potential and 1570 compounds for hERG inhibition. After removal of the modeling and experimental inconclusive compounds, the area under the receiver-operating characteristic (ROC) curve was 0.92 for the PLD model and 0.87 for the hERG model. Due to the exceptional ability of these models to recognize safe compounds (negative predictive values of 0.99 for PLD and 0.94 for hERG were achieved), their use in regulatory settings might be particularly useful.
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References
ACD/NMR Predictor Release 12.00, version 12.5; advanced chemistry development. Toronto, Canada, 2011
Bajusz D, Rácz A, Héberger K (2015) Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? J Cheminform 7:20–33
Bauch C, Bevan S, Woodhouse H, Dilworth C, Walker P (2015) Predicting in vivo phospholipidosis-inducing potential of drugs by a combined high content screening and in silico modelling approach. Toxicol In Vitro 29:621–630
Cavalli A, Poluzzi E, De Ponti F, Recanatini M (2002) Toward a pharmacophore for drugs inducing the long QT syndrome: insights from a CoMFA study of HERG K+ channel blockers. J Med Chem 45:3844–3853
Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V, Kuz’min VE, Cramer R, Benigni R, Yang C, Rathman J, Terfloth L, Gasteiger J, Richard A, Tropsha A (2014) QSAR modeling: where have you been? Where are you going to? J Med Chem 57:4977–5010
Goracci L, Buratta S, Urbanelli L, Ferrara G, Di Guida R, Emiliani C, Cross S (2015) Evaluating the risk of phospholipidosis using a new multidisciplinary pipeline approach. Eur J Med Chem 92:49–63
He H, Garcia E (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21:1263–1284
Kruhlak NL, Choi SS, Contrera JF, Weaver JL, Willard JM, Hastings KL, Sancilio LF (2008) Development of a phospholipidosis database and predictive quantitative structure-activity relationship (QSAR) models. Toxicol Mech Methods 18:217–227
NCATS (2016) “Tox21 Data Browser.” https://tripod.nih.gov/tox21. Accessed 12 Feb 2017
PubChem (2013) “Tox21 Phase II compound collection” https://www.ncbi.nlm.nih.gov/pcsubstance/?term=tox21. Accessed 12 Feb 2017
Reasor MJ, Kacew S (2001) Drug-induced phospholipidosis: are there functional consequences? Exp Biol Med (Maywood) 226:825–830
Shahane SA, Huang R, Gerhold D, Baxa U, Austin CP, Xia M (2014) Detection of phospholipidosis induction: a cell-based assay in high-throughput and high-content format. J Biomol Screen 19:66–76
Slavov S, Pearce B, Buzatu D, Wilkes J, Beger R (2013) Complementary PLS and KNN algorithms for improved 3D-QSDAR consensus modeling of AhR binding. J Cheminform 5:47–62
Slavov SH, Wilkes JG, Buzatu DA, Kruhlak NL, Willard JM, Hanig JP, Beger RD (2014) Computational identification of a phospholipidosistoxicophore using 13C and 15N NMR-distance based fingerprints. Bioorg Med Chem 22:6706–6714
Stoyanova-Slavova IB, Slavov SH, Buzatu DA, Beger RD, Wilkes JG (2017) 3D-SDAR modeling of hERG potassium channel affinity: a case study in model design and toxicophore identification. J Mol Graph Model 72:246–255
Sun H, Xia M, Shahane SA, Jadhav A, Austin CP, Huang R (2013) Are hERG channel blockers also phospholipidosis inducers? Bioorg Med Chem Lett 23:4587–4590
Titus SA, Beacham D, Shahane SA, Southall N, Xia M, Huang R, Hooten E, Zhao Y, Shou L, Austin CP, Zheng W (2009) A new homogeneous high-throughput screening assay for profiling compound activity on the human ether-a-go-go-related gene channel. Anal Biochem 394:30–38
Villoutreix BO, Taboureau O (2015) Computational investigations of hERG channel blockers: new insights and current predictive models. Adv Drug Deliv Rev 86:72–82
Wang Y, Huang R (2016) Correction of microplate data from high throughput screening. In: Zhu H (ed) High-throughput screening assays in toxicology. Humana Press, Springer Science + Business Media, New York, pp 123–134
Wang Y, Jadhav A, Southal N, Huang R, Nguyen DT (2010) A grid algorithm for high throughput fitting of dose-response curve data. Curr Chem Genomics 4:57–66
Witchel HJ (2011) Drug-induced hERG block and long QT syndrome. Cardiovasc Ther 29:251–259
Xia M, Shahane SA, Huang R, Titus SA, Shum E, Zhao Y, Southall N, Zheng W, Witt KL, Tice RR, Austin CP (2011) Identification of quaternary ammonium compounds as potent inhibitors of hERG potassium channels. Toxicol Appl Pharmacol 252:250–258
Acknowledgements
This work was partially supported by the U.S. Environmental Protection Agency (Interagency Agreement #Y3-HG-7026-03) and the interagency agreement IAG #NTR 12003 from the National Institute of Environmental Health Sciences/Division of the National Toxicology Program to the National Center for Advancing Translational Sciences, National Institutes of Health. We would like to thank Sampada Shahane for her technical support.
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Slavov, S., Stoyanova-Slavova, I., Li, S. et al. Why are most phospholipidosis inducers also hERG blockers?. Arch Toxicol 91, 3885–3895 (2017). https://doi.org/10.1007/s00204-017-1995-9
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DOI: https://doi.org/10.1007/s00204-017-1995-9