Avoiding hERG-liability in drug design via synergetic combinations of different (Q)SAR methodologies and data sources: a case study in an industrial setting
Tóm tắt
In this paper, we explore the impact of combining different in silico prediction approaches and data sources on the predictive performance of the resulting system. We use inhibition of the hERG ion channel target as the endpoint for this study as it constitutes a key safety concern in drug development and a potential cause of attrition. We will show that combining data sources can improve the relevance of the training set in regard of the target chemical space, leading to improved performance. Similarly we will demonstrate that combining multiple statistical models together, and with expert systems, can lead to positive synergistic effects when taking into account the confidence in the predictions of the merged systems. The best combinations analyzed display a good hERG predictivity. Finally, this work demonstrates the suitability of the SOHN methodology for building models in the context of receptor based endpoints like hERG inhibition when using the appropriate pharmacophoric descriptors.
Tài liệu tham khảo
Roden DM (1993) Torsade de pointes. Clin Cardiol 16(9):683–686. https://doi.org/10.1002/clc.4960160910
Warmke JW, Ganetzky B (1994) A family of potassium channel genes related to eag in Drosophila and mammals. Proc Natl Acad Sci 91(8):3438–3442. https://doi.org/10.1073/pnas.91.8.3438
Kaplan WD, Trout WE (1969) The behavior of four neurological mutants of Drosophila. Genetics 61(2):399–409
Sanguinetti MC, Tristani-Firouzi M (2006) HERG potassium channels and cardiac arrhythmia. Nature 440:463–469
Rampe D, Roy M-L, Dennis A, Brown AM (1997) A mechanism for the proarrhythmic effects of cisapride (Propulsid): high affinity blockade of the human cardiac potassium channel HERG. FEBS Lett 417:28–32. https://doi.org/10.1016/S0014-5793(97)01249-0
Roy M-L, Dumaine R, Brown AM (1996) HERG, a primary human ventricular target of the nonsedating antihistamine terfenadine. Circulation 94(4):817. https://doi.org/10.1161/01.CIR.94.4.817
Priest B, Bell IM, Garcia M (2008) Role of HERG potassium channel assays in drug development. Channels 2(2):87–93. https://doi.org/10.4161/chan.2.2.6004
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(18):3844–3853. https://doi.org/10.1021/jm0208875
Aronov AM (2005) Predictive in silico modeling for HERG channel blockers. Drug Discov Today 10(2):149–155. https://doi.org/10.1016/S1359-6446(04)03278-7
Wang S, Sun H, Liu H, Li D, Li Y, Hou T (2016) ADMET evaluation in drug discovery. 16. Predicting HERG blockers by combining multiple pharmacophores and machine learning approaches. Mol Pharm. https://doi.org/10.1021/acs.molpharmaceut.6b00471
Schyman P, Liu R, Wallqvist A (2016) General purpose 2D and 3D similarity approach to identify HERG blockers. J Chem Inf Model 56(1):213–222. https://doi.org/10.1021/acs.jcim.5b00616
Anwar-Mohamed A, Barakat K, Bhat R, Noskov S, Lorne Tyrrell D, Tuszynski J, Houghton M (2014) A human ether-á-go-go-related (HERG) ion channel atomistic model generated by long supercomputer molecular dynamics simulations and its use in predicting drug cardiotoxicity. Toxicol Lett 230:382–392. https://doi.org/10.1016/j.toxlet.2014.08.007
Czodrowski P (2013) HERG me out. J Chem Inf Model 53(9):2240–2251. https://doi.org/10.1021/ci400308z
Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B et al (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40(D1):D1100–D1107. https://doi.org/10.1093/nar/gkr777
Li X, Zhang Y, Li H, Zhao Y (2017) Modeling of the HERG K+ channel blockage using online chemical database and modeling environment (OCHEM). Mol Inform. https://doi.org/10.1002/minf.201700074
Su B-H, Shen M, Esposito EX, Hopfinger AJ, Tseng YJ (2010) In silico binary classification QSAR models based on 4D-fingerprints and MOE descriptors for prediction of HERG blockage. J Chem Inf Model 50(7):1304–1318. https://doi.org/10.1021/ci100081j
Senese CL, Duca J, Pan D, Hopfinger AJ, Tseng YJ (2004) 4D-fingerprints, universal QSAR and QSPR descriptors. J Chem Inf Comput Sci 44(5):1526–1539. https://doi.org/10.1021/ci049898s
Gavaghan CL, Arnby CH, Blomberg N, Strandlund G, Boyer S (2007) Development, interpretation and temporal evaluation of a global QSAR of HERG electrophysiology screening data. J Comput Aided Mol Des 21(4):189–206. https://doi.org/10.1007/s10822-006-9095-6
Passini E, Britton OJ, Lu HR, Rohrbacher J, Hermans AN, Gallacher DJ, Greig RJH, Bueno-Orovio A, Rodriguez B (2017) Human in silico drug trials demonstrate higher accuracy than animal models in predicting clinical pro-arrhythmic cardiotoxicity. Front Physiol. https://doi.org/10.3389/fphys.2017.00668
Munawar S, Windley MJ, Tse EG, Todd MH, Hill AP, Vandenberg JI, Jabeen I (2018) Experimentally validated pharmacoinformatics approach to predict hERG inhibition potential of new chemical entities. Front Pharmacol. https://doi.org/10.3389/fphar.2018.01035
Chemi G, Gemma S, Campiani G, Brogi S, Butini S, Brindisi M (2017) Computational tool for fast in silico evaluation of HERG K+ channel affinity. Front Chem. https://doi.org/10.3389/fchem.2017.00007
Bashir Surfraz M, Fowkes A, Plante JP (2017) A semi-automated approach to create purposeful mechanistic datasets from heterogeneous data: data mining towards the in silico predictions for oestrogen receptor modulation and teratogenicity. Mol Inform. https://doi.org/10.1002/minf.201600154
Steinmetz FP, Mellor CL, Meinl T, Cronin MTD (2015) Screening chemicals for receptor-mediated toxicological and pharmacological endpoints: using public data to build screening tools within a KNIME workflow. Mol Inform 34(2–3):171–178. https://doi.org/10.1002/minf.201400188
Sato T, Yuki H, Ogura K, Honma T (2018) Construction of an integrated database for HERG blocking small molecules. PLoS ONE 13(7):e0199348. https://doi.org/10.1371/journal.pone.0199348
Klimisch H-J, Andreae M, Tillmann U (1997) A systematic approach for evaluating the quality of experimental toxicological and ecotoxicological data. Regul Toxicol Pharmacol 25(1):1–5. https://doi.org/10.1006/rtph.1996.1076
Hanser T, Barber C, Rosser E, Vessey JD, Webb SJ, Werner S (2014) Self organising hypothesis networks: a new approach for representing and structuring SAR knowledge. J Cheminform 6(1):21. https://doi.org/10.1186/1758-2946-6-21
Derek Nexus, Lhasa Limited. https://www.lhasalimited.org/products/derek-nexus.htm
Sheridan RP (2013) Time-split cross-validation as a method for estimating the goodness of prospective prediction. J Chem Inf Model 53(4):783–790. https://doi.org/10.1021/ci400084k
Plante J, Werner S (2018) JPlogP: an improved LogP predictor trained using predicted data. J Cheminform. https://doi.org/10.1186/s13321-018-0316-5
Ghose AK, Crippen GM (1986) Atomic physicochemical parameters for three-dimensional structure—directed quantitative structure-activity relationships I. Partition coefficients as a measure of hydrophobicity. J Comput Chem 7(4):565–577. https://doi.org/10.1002/jcc.540070419
Landrum G (2006) RDKit: Open-Source Cheminformatics
Polonchuk L (2012) Toward a new gold standard for early safety: automated temperature-controlled HERG test on the PatchLiner. Front Pharmacol 3:3. https://doi.org/10.3389/fphar.2012.00003
Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27(8):861–874. https://doi.org/10.1016/j.patrec.2005.10.010
Altman DG, Bland JM (1994) Statistics notes: diagnostic tests 2: predictive values. BMJ 309(6947):102. https://doi.org/10.1136/bmj.309.6947.102
Brodersen KH, Ong CS, Stephan KE, Buhmann JM (2010) The balanced accuracy and its posterior distribution. IEEE, pp 3121–3124. https://doi.org/10.1109/ICPR.2010.764
Matthews BW (1975) Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta BBA Protein Struct 405(2):442–451. https://doi.org/10.1016/0005-2795(75)90109-9
Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20(1):37–46. https://doi.org/10.1177/001316446002000104
Briggs K, Barber C, Cases M, Marc P, Steger-Hartmann T (2015) Value of shared preclinical safety studies—the ETOX database. Toxicol Rep 2:210–221. https://doi.org/10.1016/j.toxrep.2014.12.004
Merget B, Turk S, Eid S, Rippmann F, Fulle S (2017) Profiling prediction of kinase inhibitors: toward the virtual assay. J Med Chem 60(1):474–485. https://doi.org/10.1021/acs.jmedchem.6b01611
Food and Drug Administration (FDA) (2018) M7(R1) assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals to limit potential carcinogenic risk guidance for industry, p 131. https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM347725.pdf
Hanser T, Barber C, Marchaland JF, Werner S (2016) Applicability domain: towards a more formal definition. SAR QSAR Environ Res 27(11):865–881. https://doi.org/10.1080/1062936X.2016.1250229
