QSAR analysis of the acute toxicity of avermectins towards Tetrahymena pyriformis

References

• W.L. Shoop, H. Mrozik, and M.H. Fisher, Structure and activity of avermectins and milbemycins in animal health, Vet. Parasitol. 59 (1995), pp. 139–156. doi:10.1016/0304-4017(94)00743-v.
   PubMed Web of Science ®Google Scholar
• Y. Zhuo, T. Zhang, Q. Wang, P. Cruz-Morales, B. Zhang, M. Liu, F. Barona-Gómez, and L. Zhang, Synthetic biology of avermectin for production improvement and structure diversification, Biotechnol. J. 9 (2014), pp. 316–325. doi:10.1002/biot.201200383.
   PubMed Web of Science ®Google Scholar
• A. Crump and S. Ōmura, Ivermectin, ‘wonder drug’ from Japan: The human use perspective, Proc. Jpn. Acad. Ser B Phys. Biol. Sci. 87 (2011), pp. 13–28. doi:10.2183/pjab.87.13.
   PubMed Web of Science ®Google Scholar
• The nobel prize in physiology or medicine; Available at https://www.nobelprize.org/prizes/medicine/
   Google Scholar
• R. Prichard, C. Ménez, and A. Lespine, Moxidectin and the avermectins: Consanguinity but not identity, Int. J. Parasitol. Drugs Drug Resist. 2 (2012), pp. 134–153. doi:10.1016/j.ijpddr.2012.04.001.
   PubMed Web of Science ®Google Scholar
• G. Dominguez-Gomez, A. Chavez-Blanco, J.L. Medina-Franco, F. Saldivar-Gonzalez, Y. Flores-Torrontegui, M. Juarez, J. Díaz-Chávez, A. Gonzalez-Fierro, and A. Dueñas-González, Ivermectin as an inhibitor of cancer stem‑like cells, Mol. Med. Rep. 17 (2018), pp. 3397–3403. doi:10.3892/mmr.2017.8231.
   PubMed Web of Science ®Google Scholar
• K.M. Wagstaff, H. Sivakumaran, S.M. Heaton, D. Harrich, and D.A. Jans, Ivermectin is a specific inhibitor of importin α/β-mediated nuclear import able to inhibit replication of HIV-1 and dengue virus, Biochem. J. 443 (2012), pp. 851–856. doi:10.1042/BJ20120150.
   PubMed Web of Science ®Google Scholar
• L. Calya, J.D. Drucea, M.G. Cattona, D.A. Jansb, and K.M. Wagstaff, The FDA-approved drug ivermectin inhibits the replication of SARS-CoV-2 in vitro, Antiviral Res. 178 (2020), pp. 104787. doi:10.1016/j.antiviral.2020.104787.
   PubMed Web of Science ®Google Scholar
• C. Chaccour, F. Hammann, S. Ramón-García, and N.R. Rabinovich, Ivermectin and COVID-19: Keeping rigor in times of urgency, Am. J. Trop. Med. Hyg. 102 (2020), pp. 1156–1157. doi:10.4269/ajtmh.20-0271.
   PubMed Web of Science ®Google Scholar
• G. Momekov and D. Momekova, Ivermectin as a potential COVID-19 treatment from the pharmacokinetic point of view, Biotechnol. Biotechnol. Equip. 34 (2020), pp. 469–474. doi:10.1080/13102818.2020.1775118.
   Web of Science ®Google Scholar
• COVID-19 Treatment Guidelines, National institutes of health of the United States government; Available at https://www.covid19treatmentguidelines.nih.gov/antiviral-therapy/ivermectin/
   Google Scholar
• European Parliament, Council of the European Union. European Commission, Regulation (EC) no 1907/2006 of the European parliament and of the council of 18 December 2006 concerning the registration, evaluation, authorisation and restriction of chemicals (REACH), establishing a European chemicals agency, amending directive 1999/45/ECC and repealing council regulation (EEC) No 793/93 and commission regulation (EC) No 1488/94 as well as council directive 76/769/EEC and commission directives 91/155/EEC, 93/67/EEC, 93/105/EEC and 2000/21/EC, Off, J. Eur. Union. 50 (2007), pp. 1–281.
   Google Scholar
• S.H. Bai and S. Ogbourne, Eco-toxicological effects of the avermectin family with a focus on abamectin and ivermectin, Chemosphere 154 (2016), pp. 204–214. doi:10.1016/j.chemosphere.2016.03.113.
   PubMed Web of Science ®Google Scholar
• I. Kahn, S. Sild, and U. Maran, Modeling the toxicity of chemicals to Tetrahymena pyriformis using heuristic multilinear regression and heuristic back-propagation neural networks, J. Chem. Inf. Model. 47 (2007), pp. 2271–2279. doi:10.1021/ci700231c.
   PubMed Web of Science ®Google Scholar
• E.N. Muratov, J. Bajorath, R.P. Sheridan, I.V. Tetko, D. Filimonov, V. Poroikov, T.I. Oprea, I.I. Baskin, A. Varnek, A. Roitberg, O. Isayev, S. Curtalolo, D. Fourches, Y. Cohen, A. Aspuru-Guzik, D.A. Winkler, D. Agrafiotis, A. Cherkasov, and A. Tropsha, QSAR without borders, Chem. Soc. Rev. (2020), pp. 10. doi:10.1039/d0cs00098a.
   Web of Science ®Google Scholar
• T.I. Netzeva, T.W. Schultz, A.O. Aptula, and M.T. Cronin, Partial least squares modelling of the acute toxicity of aliphatic compounds to Tetrahymena pyriformis, SAR QSAR Environ. Res. 14 (2003), pp. 265–283. doi:10.1080/1062936032000101501.
   PubMed Web of Science ®Google Scholar
• J. Devillers, Linear versus nonlinear QSAR modeling of the toxicity of phenol derivatives to Tetrahymena pyriformis, SAR QSAR Environ. Res. 15 (2004), pp. 237–249. doi:10.1080/10629360410001724905.
   PubMed Web of Science ®Google Scholar
• A.A. Lagunin, A.V. Zakharov, D.A. Filimonov, and V.V. Poroikov, A new approach to QSAR modelling of acute toxicity, SAR QSAR Environ. Res. 18 (2007), pp. 285–298. doi:10.1080/10629360701304253.
   PubMed Web of Science ®Google Scholar
• S.J. Enoch, M.T.D. Cronin, T.W. Schultz, and J.C. Madden, An evaluation of global QSAR models for the prediction of the toxicity of phenols to Tetrahymena pyriformis, Chemosphere 71 (2008), pp. 1225–1232. doi:10.1016/j.chemosphere.2007.12.011.
   PubMed Web of Science ®Google Scholar
• M. Hewitt, M.T.D. Cronin, P.H. Rowe, and T.W. Schultz, Repeatability analysis of the Tetrahymena pyriformis population growth impairment assay, SAR QSAR Environ. Res. 22 (2011), pp. 621–637. doi:10.1080/1062936X.2011.604100.
   PubMed Web of Science ®Google Scholar
• J.A. Castillo-Garit, C. Abad, G.M. Casañola-Martin, S.J. Barigye, F. Torrens, and A. Torreblanca, Prediction of aquatic toxicity of benzene derivatives to Tetrahymena pyriformis according to OECD principles, Curr. Pharm. Des. 22 (2016), pp. 5085–5094. doi:10.2174/1381612822666160804095107.
   PubMed Web of Science ®Google Scholar
• A.P. Toropova, T.W. Schultz, and A.A. Toropov, Building up a QSAR model for toxicity toward Tetrahymena pyriformis by the Monte Carlo method: A case of benzene derivatives, Environ. Toxicol. Pharmacol. 42 (2016), pp. 135–145. doi:10.1016/j.etap.2016.01.010.
   PubMed Web of Science ®Google Scholar
• F. Abbasitabar and V. Zare-Shahabadi, In silico prediction of toxicity of phenols to Tetrahymena pyriformis by using genetic algorithm and decision tree-based modeling approach, Chemosphere 172 (2017), pp. 249–259. doi:10.1016/j.chemosphere.2016.12.095.
   PubMed Web of Science ®Google Scholar
• A.G. Artemenko, E.N. Muratov, V.E. Kuz’min, N.N. Muratov, E.V. Varlamova, A.V. Kuz’mina, L.G. Gorb, A. Golius, F.C. Hill, J. Leszczynski, and A. Tropsha, QSAR analysis of nitroaromatics’ toxicity in Tetrahymena pyriformis: Structural factors and possible modes of action, SAR QSAR Environ. Res. 22 (2011), pp. 575–601. doi:10.1080/1062936X.2011.569950.
   PubMed Web of Science ®Google Scholar
• A.R. Katritzky, P. Oliferenko, A. Oliferenko, A. Lomaka, and M. Karelson, Nitrobenzene toxicity: QSAR correlations and mechanistic interpretations, J. Phys. Org. Chem. 16 (2003), pp. 811–817. doi:10.1002/poc.643.
   Web of Science ®Google Scholar
• W.K. Agrawal and P.V. Khadikar, QSAR prediction of toxicity of nitrobenzenes, Bioorg. Med. Chem. 9 (2001), pp. 3035–3040. doi:10.1016/s0968-0896(01)00211-5.
   PubMed Web of Science ®Google Scholar
• H. Schmitt, R. Altenburger, B. Jastorff, and G. Schuurmann, Quantitative structure-activity analysis of the algae toxicity of nitroaromatic compounds, Chem. Res. Toxicol. 13 (2000), pp. 441–450.
   PubMed Web of Science ®Google Scholar
• I.V. Tetko, I. Sushko, A.K. Pandey, H. Zhu, A. Tropsha, E. Papa, T. Oberg, R. Todeschini, D. Fourches, and A. Varnek, Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: Focusing on applicability domain and overfitting by variable selection, J Chem Inf Model 48 (2008), pp. 1733–1746. doi:10.1021/ci800151m.
   PubMed Web of Science ®Google Scholar
• T. Martin, TEST (Toxicity Estimation Software Tool) version 5.1; US Environmental Protection Agency, Cincinnati, OH, USA; 2020. Available at https://www.epa.gov/chemical-research/toxicity-estimation-software-tool-test
   Google Scholar
• A. Golbraikh and A. Tropsha, Beware of q2!, J. Mol. Graph Model. 20 (2002), pp. 269–276. doi:10.1016/s1093-3263(01)00123-1.
   PubMed Web of Science ®Google Scholar
• G. Papadatos and N. Brown, In silico applications of bioisosterism in contemporary medicinal chemistry practice, WIREs Comput. Mol. Sci. 3 (2013), pp. 339–354. doi:10.1002/wcms.1148.
   Google Scholar
• X. Yu, Prediction of chemical toxicity to Tetrahymena pyriformis with four-descriptor models, Ecotoxicol. Environ. Saf. 190 (2020), pp. 110146. doi:10.1016/j.ecoenv.2019.110146.
   PubMed Web of Science ®Google Scholar
• OECD, Guidance Document on the Validation of (Quantitative) Structure Activity Relationship[(Q)SAR] Models, Tech. Rep. ENV/JM/MONO(2007) Vol. 2, Organisation for Economic Cooperation and Development, Paris, FR, 2007.
   Google Scholar
• P. Polishchuk, Interpretation of quantitative structure–activity relationship models: Past, present, and future, J. Chem. Inf. Model. 57 (2017), pp. 2618–2639. doi:10.1021/acs.jcim.7b00274.
   PubMed Web of Science ®Google Scholar
• O. Tinkov, P. Polishchuk, M. Matveieva, V. Grigorev, L. Grigoreva, and Y. Porozov, The influence of structural patterns on acute aquatic toxicity of organic compounds, Mol. Inf. 39 (2020), pp. 2000209. doi:10.1002/minf.202000209.
   Google Scholar
• O.V. Tinkov, V.Y. Grigorev, A.N. Razdolsky, L.D. Grigoryeva, and J.C. Dearden, Effect of the structural factors of organic compounds on the acute toxicity toward, Daphnia magna, SAR QSAR Environ. Res. 31 (2020), pp. 615–641. doi:10.1080/1062936X.2020.1791250.
   PubMed Web of Science ®Google Scholar
• S.J. Webb, T. Hanser, B. Howlin, P. Krause, and J.D. Vessey, Feature combination networks for the interpretation of statistical machine learning models: Application to Ames mutagenicity, J. Cheminform. 6 (2014), pp. 8. doi:10.1186/1758-2946-6-8.
   PubMed Web of Science ®Google Scholar
• Y.S. Low, V.M. Alves, D. Fourches, A. Sedykh, C.H. Andrade, E.N. Muratov, I. Rusyn, and A. Tropsha, Chemistry-wide association studies (CWAS): A novel framework for identifying and interpreting structure-activity relationships, J. Chem. Inf. Model. 58 (2018), pp. 2203–2213. doi:10.1021/acs.jcim.8b00450.
   PubMed Web of Science ®Google Scholar
• G. Marcou, D. Horvath, V. Solov’ev, A. Arrault, P. Vayer, and A. Varnek, Interpretability of SAR/QSAR models of any complexity by atomic contributions, Mol. Inf. 31 (2012), pp. 639–642. doi:10.1002/minf.201100136.
   PubMed Web of Science ®Google Scholar
• A.G. Leach, H.D. Jones, D.A. Cosgrove, P.W. Kenny, L. Ruston, P. MacFaul, J.M. Wood, N. Colclough, and B. Law, Matched molecular pairs as a guide in the optimization of pharmaceutical properties; a study of aqueous solubility, plasma protein binding and oral exposure, J. Med. Chem. 49 (2006), pp. 6672–6682. doi:10.1021/jm0605233.
   PubMed Web of Science ®Google Scholar
• S. Riniker and G.A. Landrum, Similarity maps - A visualization strategy for molecular fingerprints and machine-learning methods, J. Cheminf. 5 (2013), pp. 43. doi:10.1186/1758-2946-5-43.
   PubMed Web of Science ®Google Scholar
• Y. Sushko, S. Novotarskyi, R. Korner, J. Vogt, A. Abdelaziz, and I.V. Tetko, Prediction-driven matched molecular pairs to interpret QSARs and aid the molecular optimization process, J. Cheminform. 6 (2014), pp. 48. doi:10.1186/s13321-014-0048-0.
   PubMed Web of Science ®Google Scholar
• M. Matveieva, M. Cronin, and P. Polishchuk, Interpretation of QSAR models: Mining structural patterns taking into account molecular context, Mol. Inf. 38 (2019), pp. e1800084. doi:10.1002/minf.201800084.
   PubMed Web of Science ®Google Scholar
• P.G. Polishchuk, V.E. Kuz’min, A.G. Artemenko, and E.N. Muratov, Universal approach for structural interpretation of QSAR/QSPR models, Mol. Inf. 32 (2013), pp. 843–853. doi:10.1002/minf.201300029.
   PubMed Web of Science ®Google Scholar
• F. Cheng, J. Shen, Y. Yu, W. Li, G. Liu, P.W. Lee, and Y. Tang, In silico prediction of Tetrahymena pyriformis toxicity for diverse industrial chemicals with substructure pattern recognition and machine learning methods, Chemosphere 82 (2011), pp. 1636–1643. doi:10.1016/j.chemosphere.2010.11.043.
   PubMed Web of Science ®Google Scholar
• I. Sushko, A.K. Pandey, S. Novotarskyi, R. Körner, M. Rupp, W. Teetz, S. Brandmaier, A. Abdelaziz, V.V. Prokopenko, V.Y. Tanchuk, R. Todeschini, A. Varnek, G. Marcou, P. Ertl, V. Potemkin, M. Grishina, J. Gasteiger, C. Schwab, I.I. Baskin, V.A. Palyulin, E.V. Radchenko, W.J. Welsh, V. Kholodovych, D. Chekmarev, A. Cherkasov, J. Aires-de-sousa, Q.Y. Zhang, A. Bender, F. Nigsch, L. Patiny, and I.V. Tetko, Online chemical modeling environment (OCHEM): Web platform for data storage, model development and publishing of chemical information, J. Comput.-Aided Mol. Des. 25 (2011), pp. 533–554. doi:10.1007/s10822-011-9440-2.
   PubMed Web of Science ®Google Scholar
• R. Todeschini and V. Consonni, Handbook of Molecular Descriptors, Weinheim: Wiley-VCH, 2000.
   Google Scholar
• V.H. Masand and V. Rastija, PyDescriptor: A new PyMOL plugin for calculating thousands of easily understandable molecular descriptors, Chemom. Intel. Lab. Syst. 169 (2017), pp. 12–18. doi:10.1016/j.chemolab.2017.08.003.
   Web of Science ®Google Scholar
• alvaDesc KNIME Plugin – Tool for calculates alvaDesc molecular descriptors; Available at https://www.alvascience.com/knime-alvadesc/
   Google Scholar
• V.P. Solov’ev, A. Varnek, and G. Wipff, Modeling of ion complexation and extraction using substructural molecular fragments, J. Chem. Inf. Comput. Sci. 40 (2000), pp. 847–858. doi:10.1021/ci9901340.
   PubMedGoogle Scholar
• Chemistry development kit - Open source modular java libraries for cheminformatics; Available at https://cdk.github.io/
   Google Scholar
• V.E. Kuz’min, A.G. Artemenko, and E.N. Muratov, Hierarchical QSAR technology based on the simplex representation of molecular structure, J. Comput. Aided Mol. Des. 22 (2008), pp. 403–421. doi:10.1007/s10822-008-9179-6.
   PubMed Web of Science ®Google Scholar
• J.A.K. Suykens and J. Vandewalle, Least squares support vector machine classifiers, Neural Process. Lett. 9 (1999), pp. 293–300. doi:10.1023/A:1018628609742.
   Web of Science ®Google Scholar
• P. Karpov, G. Godin, and I.V. Tetko, Transformer-CNN: Swiss knife for QSAR modeling and interpretation, J Cheminform. 12 (2020). Article number 17. doi:10.1186/s13321-020-00423-w.
   PubMed Web of Science ®Google Scholar
• Standardizer, version 5.4; ChemAxon: Budapest, hungary; Available at https://docs.chemaxon.com/display/docs/Standardizer+Command-line+Application
   Google Scholar
• D. Fourches, E. Muratov, and A. Tropsha, Trust, but verify: On the importance of chemical structure curation in cheminformatics and QSAR modeling research, J. Chem. Inf. Model. 50 (2010), pp. 1189–1204. doi:10.1021/ci100176x.
   PubMed Web of Science ®Google Scholar
• D. Fourches, E. Muratov, and A. Tropsha, Trust, but verify II: A practical guide to chemogenomics data curation, J. Chem. Inf. Model. 56 (2016), pp. 1243–1252. doi:10.1021/acs.jcim.6b00129.
   PubMed Web of Science ®Google Scholar
• J. Sadowski, J. Gasteiger, and G. Klebe, Comparison of automatic three-dimensional model builders using 639 x-ray structures, J. Chem. Inf. Comput. Sci. 34 (1994), pp. 1000–1008.
   Google Scholar
• I.V. Tetko, M.D. Lowe, and A.J. Williams, The development of models to predict melting and pyrolysis point data associated with several hundred thousand compounds mined from PATENTS, J Cheminform 8 (2016). article number: 2. doi:10.1186/s13321-016-0113-y.
   PubMed Web of Science ®Google Scholar
• RBF SVM parameters — Scikit-learn 0.24.1 documentation; Available at https://scikit-learn.org/stable/auto_examples/svm/plot_rbf_parameters.html
   Google Scholar
• RAdam – Optimizer of neural network; Available at https://github.com/LiyuanLucasLiu/RAdam
   Google Scholar
• I.V. Tetko, P. Bruneau, H.W. Mewes, D.C. Rohrer, and G.I. Poda, Can we estimate the accuracy of ADME–Tox predictions?, Drug Discov. Today 11 (2006), pp. 700–707. doi:10.1016/j.drudis.2006.06.013.
   PubMed Web of Science ®Google Scholar
• I. Sushko, S. Novotarskyi, R. Körner, A.K. Pandey, V.V. Kovalishyn, V.V. Prokopenko, and I.V. Tetko, Applicability domain for in silico models to achieve accuracy of experimental measurements, J. Chemom. 24 (2010), pp. 202–208. doi:10.1002/cem.1296.
   Web of Science ®Google Scholar
• I. Sushko, S. Novotarskyi, R. Körner, A.K. Pandey, A. Cherkasov, J. Li, P. Gramatica, K. Hansen, T. Schroeter, K.R. Muller, L. Xi, H. Liu, X. Yao, T. Oberg, F. Hormozdiari, P. Dao, C. Sahinalp, R. Todeschini, P. Polishchuk, A. Artemenko, V. Kuz’min, T.M. Martin, D.M. Young, D. Fourches, E. Muratov, A. Tropsha, I. Baskin, D. Horvath, G. Marcou, C. Muller, A. Varnek, V.V. Prokopenko, and I.V. Tetko, Applicability domains for classification problems: Benchmarking of distance to models for Ames mutagenicity set, J. Chem. Inf. Model. 50 (2010), pp. 2094–2111. doi:10.1021/ci100253r.
   PubMed Web of Science ®Google Scholar
• OCHEM user’s manual; Available at http://docs.ochem.eu/display/MAN/OCHEM+Introduction
   Google Scholar
• I. Mitra, A. Saha, and K. Roy, Exploring quantitative structure–activity relationship studies of antioxidant phenolic compounds obtained from traditional Chinese medicinal plants, Molec. Simul. 36 (2010), pp. 1067–1079. doi:10.1080/08927022.2010.503326.
   Web of Science ®Google Scholar
• O.A. Raevsky, V.Y. Grigor’ev, D.B. Kireev, and N.S. Zefirov, Complete thermodynamic description of h-bonding in the framework of multiplicative approach, Quant. Struct.-Act. Relat. 11 (1992), pp. 49–63. doi:10.1002/qsar.19920110109.
   Google Scholar
• V. Yu. and L.D. Grigorev, Calculation and properties of fractal descriptors for C2-C9 alkanes, Moscow Univ. Chem. Bull. 71 (2016), pp. 199–204. doi:10.3103/S0027131416030056.
   Google Scholar
• L. Breiman, RRforest; Available at http://www.stat.berkeley.edu/~breiman/RandomForests/reg_examples/RFR.f
   Google Scholar
• H. Kubinyi, Variable selection in QSAR studies. I. an evolutionary algorithm, Quant. Struct.-Act. Relat. 13 (1994), pp. 285–294. doi:10.1002/qsar.19940130306.
   Google Scholar
• M. Asadollahi-Baboli, In silico prediction of the aniline derivatives toxicities to Tetrahymena pyriformis using chemometrics tools, Toxicol. Environ. Chem. 94 (2012), pp. 2019–2034. doi:10.1080/02772248.2012.740046.
   Web of Science ®Google Scholar
• E. Griffen, A.G. Leach, G.R. Robb, and D.J. Warner, Matched molecular pairs as a medicinal chemistry tool, J. Med. Chem. 54 (2011), pp. 7739–7750. doi:10.1021/jm200452d.
   PubMed Web of Science ®Google Scholar
• S.J. Enoch, C.M. Ellison, T.W. Schultz, and M.T.D. Cronin, A review of the electrophilic reaction chemistry involved in covalent protein binding relevant to toxicity, Crit. Rev. Toxicol. 41 (2011), pp. 783–802. doi:10.3109/10408444.2011.598141.
   PubMed Web of Science ®Google Scholar
• H.J.M. Verhaar, C.J.V. Leeuwen, and J.L.M. Hermens, Classifying environmental pollutants part 1: Structural activity relationship for prediction of aquatic toxicity, Chemosphere 25 (1992), pp. 471–491.
   Web of Science ®Google Scholar
• J.L. Hermens, Electrophiles and acute toxicity to fish, Environ. Health Perspect. 87 (1990), pp. 219–225. doi:10.1289/ehp.9087219.
   PubMed Web of Science ®Google Scholar
• P.C. von der Ohe, R. Kühne, R.U. Ebert, R. Altenburger, M. Liess, and G. Schüürmann, Structural alerts-a new classification model to discriminate excess toxicity from narcotic effect levels of organic compounds in the acute daphnid assay, Chem. Res. Toxicol. 18 (2005), pp. 536–555. doi:10.1021/tx0497954.
   PubMed Web of Science ®Google Scholar
• T.W. Schultz, J.W. Yarbrough, and M. Woldemeske, Toxicity to Tetrahymena and abiotic thiol reactivity of aromatic isothiocyanates, Cell Biol. Toxicol. 21 (2005), pp. 181–189. doi:10.1007/s10565-005-0169-3.
   PubMed Web of Science ®Google Scholar