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Research Articles

Quantitative structure activity relationship studies of androgen receptor binding affinity of endocrine disruptor chemicals with index of ideality of correlation, their molecular docking, molecular dynamics and ADME studies

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Pages 13616-13631 | Received 11 Oct 2022, Accepted 03 Feb 2023, Published online: 03 Apr 2023

References

  • Achary, P. G. R., Toropova, A. P., & Toropov, A. A. (2019). Combinations of graph invariants and attributes of simplified molecular input-line entry system (SMILES) to build up models for sweetness. Food Research International, 122, 40–46. https://doi.org/10.1016/j.foodres.2019.03.067
  • Adler, S., Basketter, D., Creton, S., Pelkonen, O., van Benthem, J., Zuang, V., Andersen, K. E., Angers-Loustau, A., Aptula, A., Bal-Price, A., Benfenati, E., Bernauer, U., Bessems, J., Bois, F. Y., Boobis, A., Brandon, E., Bremer, S., Broschard, T., Casati, S., … Zaldivar, J.-M. (2011). Alternative (non-animal) methods for cosmetics testing: Current status and future prospects—2010. Archives of Toxicology, 85(5), 367–485. https://doi.org/10.1007/s00204-011-0693-2
  • Aher, R. B., & Roy, K. (2017). Exploring the structural requirements in multiple chemical scaffolds for the selective inhibition of Plasmodium falciparum calcium-dependent protein kinase-1 (PfCDPK-1) by 3D-pharmacophore modelling, and docking studies. SAR and QSAR in Environmental Research, 28(5), 390–414. https://doi.org/10.1080/1062936X.2017.1326401
  • Ahmadi, S. (2020). Mathematical modeling of cytotoxicity of metal oxide nanoparticles using the index of ideality correlation criteria. Chemosphere, 242, 125192. https://doi.org/10.1016/j.chemosphere.2019.125192
  • Ahmadi, S., Lotfi, S., Afshari, S., Kumar, P., & Ghasemi, E. (2021). CORAL: Monte Carlo based global QSAR modelling of Bruton tyrosine kinase inhibitors using hybrid descriptors. SAR and QSAR in Environmental Research, 32(12), 1013–1031. https://doi.org/10.1080/1062936X.2021.2003429
  • Banerjee, A., De, P., Kumar, V., Kar, S., Roy, K., Theoretics, D., Sciences, A., & States, U. (2022a). Quick and efficient quantitative predictions of androgen receptor binding affinity for screening endocrine disruptor chemicals using 2D-QSAR and chemical read across. Chemosphere, 309(Pt 1), 136579. https://doi.org/10.1016/j.chemosphere.2022.136579
  • Begum, S., & Achary, P. G. R. (2015). Simplified molecular input line entry system-based: QSAR modelling for MAP kinase-interacting protein kinase (MNK1). SAR and QSAR in Environmental Research, 26(5), 343–361. https://doi.org/10.1080/1062936X.2015.1039577
  • Bhayye, S. S., Roy, K., & Saha, A. (2016). Pharmacophore generation, atom-based 3D-QSAR, HQSAR and activity cliff analyses of benzothiazine and deazaxanthine derivatives as dual A2A antagonists/MAO‑B inhibitors. SAR and QSAR in Environmental Research, 27(3), 183–202. https://doi.org/10.1080/1062936X.2015.1136840
  • Bouhedjar, K., Manganelli, S., Gini, G., Toropov, A., Toropova, A., Ali-Mokhnache, S., & Messadi, D. (2017). QSAR modeling useful in anti-cancer drug discovery: Prediction of V600EBRAF-dependent p-ERK using Monte Carlo Method. Journal of Medicinal Chemistry and Toxicology, 2, 34-39 https://doi.org/10.15436/2575-808X.17.1308
  • Bowers, K. J., Chow, D. E., Xu, H., Dror, R. O., Eastwood, M. P., Gregersen, B. A., Klepeis, J. L., Kolossvary, I., Moraes, M. A., Sacerdoti, F. D., Salmon, J. K., Shan, Y., & Shaw, D. E. (2006). Scalable algorithms for molecular dynamics simulations on commodity clusters. SC ’06: Proceedings of the 2006 ACM/IEEE Conference on Supercomputing, 43. https://doi.org/10.1109/SC.2006.54
  • Casals-Casas, C., & Desvergne, B. (2011). Endocrine disruptors: From endocrine to metabolic disruption. Annual Review of Physiology, 73, 135–162. https://doi.org/10.1146/annurev-physiol-012110-142200
  • De, P., Kar, S., Ambure, P., & Roy, K. (2022). Prediction reliability of QSAR models: an overview of various validation tools. Archives of Toxicology, 96(5), 1279–1295. https://doi.org/10.1007/s00204-022-03252-y
  • Duhan, M., Kumar, P., Sindhu, J., Singh, R., Devi, M., Kumar, A., Kumar, R., & Lal, S. (2021). Exploring biological efficacy of novel benzothiazole linked 2,5-disubstituted-1,3,4-oxadiazole hybrids as efficient α-amylase inhibitors: Synthesis, characterization, inhibition, molecular docking, molecular dynamics and Monte Carlo based QSAR studies. Computers in Biology and Medicine, 138, 104876. https://doi.org/10.1016/j.compbiomed.2021.104876
  • Duhan, M., Sindhu, J., Kumar, P., Devi, M., Singh, R., Kumar, R., Lal, S., Kumar, A., Kumar, S., & Hussain, K. (2022). Quantitative structure activity relationship studies of novel hydrazone derivatives as α-amylase inhibitors with index of ideality of correlation. Journal of Biomolecular Structure and Dynamics, 40(11), 4933–4953. https://doi.org/10.1080/07391102.2020.1863861
  • Duhan, M., Singh, R., Devi, M., Sindhu, J., Bhatia, R., Kumar, A., & Kumar, P. (2021). Synthesis, molecular docking and QSAR study of thiazole clubbed pyrazole hybrid as α-amylase inhibitor. Journal of Biomolecular Structure and Dynamics, 39(1), 91–107. https://doi.org/10.1080/07391102.2019.1704885
  • Goddard, T. D., Huang, C. C., Meng, E. C., Pettersen, E. F., Couch, G. S., Morris, J. H., & Ferrin, T. E. (2018). UCSF ChimeraX: Meeting modern challenges in visualization and analysis. Protein Science, 27(1), 14–25. https://doi.org/10.1002/pro.3235
  • Golbraikh, A., & Tropsha, A. (2002). Beware of q2!. Journal of Molecular Graphics and Modelling, 20(4), 269–276. https://doi.org/10.1016/S1093-3263(01)00123-1
  • Golubović, M., Lazarević, M., Zlatanović, D., Krtinić, D., Stoičkov, V., Mladenović, B., Milić, D. J., Sokolović, D., & Veselinović, A. M. (2018). The anesthetic action of some polyhalogenated ethers—Monte Carlo method based QSAR study. Computational Biology and Chemistry, 75, 32–38. https://doi.org/10.1016/j.compbiolchem.2018.04.009
  • Gramatica, P., & Sangion, A. (2016). A historical excursus on the statistical validation parameters for QSAR models: A clarification concerning metrics and terminology. Journal of Chemical Information and Modeling, 56(6), 1127–1131. https://doi.org/10.1021/acs.jcim.6b00088
  • Halder, A. K., Moura, A. S., & Cordeiro, M. N. D. S. (2018). QSAR modelling: A therapeutic patent review 2010-present. Expert Opinion on Therapeutic Patents, 28(6), 467–476. https://doi.org/10.1080/13543776.2018.1475560
  • Hong, H., Fang, H., Xie, Q., Perkins, R., Sheehan, D. M., & Tong, W. (2003). Comparative molecular field analysis (CoMFA) model using a large diverse set of natural, synthetic and environmental chemicals for binding to the androgen receptor. SAR and QSAR in Environmental Research, 14(5–6), 373–388. https://doi.org/10.1080/10629360310001623962
  • Huang, T., Sun, G., Zhao, L., Zhang, N., Zhong, R., & Peng, Y. (2021). Quantitative structure-activity relationship (QSAR) studies on the toxic effects of nitroaromatic compounds (NACs): A systematic review. International Journal of Molecular Sciences, 22(16), 8557. https://doi.org/10.3390/ijms22168557
  • Jain, S., Amin, S. A., Adhikari, N., Jha, T., & Gayen, S. (2020). Good and bad molecular fingerprints for human rhinovirus 3C protease inhibition: identification, validation, and application in designing of new inhibitors through Monte Carlo-based QSAR study. Journal of Biomolecular Structure and Dynamics, 38(1), 66–77. https://doi.org/10.1080/07391102.2019.1566093
  • Kabir, E. R., Rahman, M. S., & Rahman, I. (2015). A review on endocrine disruptors and their possible impacts on human health. Environmental Toxicology and Pharmacology, 40(1), 241–258. https://doi.org/10.1016/j.etap.2015.06.009
  • Kar, S., Roy, K., & Leszczynski, J. (2018). Applicability domain: a step toward confident predictions and decidability for QSAR modeling. Computational Toxicology, 1800, 141–169. https://doi.org/10.1007/978-1-4939-7899-1_6
  • Kataria, R., Vashisht, D., Rani, P., Sindhu, J., Kumar, S., Sharma, S., Sahoo, S. C., Kumar, V., & Kumar Mehta, S. (2021). Experimental and computational validation of structural features and BSA binding tendency of 5-hydroxy-5-trifluoromethyl-3-arylpyrazolines**. ChemistrySelect, 6(38), 10324–10335. https://doi.org/10.1002/slct.202102669
  • Kumar, P., & Kumar, A. (2018). Monte Carlo method based QSAR studies of mer kinase inhibitors in compliance with OECD principles. Drug Research, 68(4), 189–195. https://doi.org/10.1055/s-0043-119288
  • Kumar, P., & Kumar, A. (2020a). CORAL: QSAR models of CB1 cannabinoid receptor inhibitors based on local and global SMILES attributes with the index of ideality of correlation and the correlation contradiction index. Chemometrics and Intelligent Laboratory Systems, 200, 103982. https://doi.org/10.1016/j.chemolab.2020.103982
  • Kumar, P., & Kumar, A. (2020b). Nucleobase sequence based building up of reliable QSAR models with the index of ideality correlation using Monte Carlo method. Journal of Biomolecular Structure and Dynamics, 38(11), 3296–3306. https://doi.org/10.1080/07391102.2019.1656109
  • Kumar, P., Kumar, A., & Sindhu, J. (2019). Design and development of novel focal adhesion kinase (FAK) inhibitors using Monte Carlo method with index of ideality of correlation to validate QSAR. SAR and QSAR in Environmental Research, 30(2), 63–80. https://doi.org/10.1080/1062936X.2018.1564067
  • Kumar, P., Kumar, A., Sindhu, J., & Lal, S. (2019). QSAR models for nitrogen containing monophosphonate and bisphosphonate derivatives as human farnesyl pyrophosphate synthase inhibitors based on Monte Carlo method. Drug Research, 69(3), 159–167. https://doi.org/10.1055/a-0652-5290
  • Liman, W., Oubahmane, M., Hdoufane, I., Bjij, I., Villemin, D., Daoud, R., Cherqaoui, D., & El Allali, A. (2022). Monte Carlo method and GA-MLR-based QSAR modeling of NS5A inhibitors against the hepatitis C virus. Molecules, 27(9), 2729. https://doi.org/10.3390/molecules27092729
  • Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 46(1-3), 3–26. https://doi.org/10.1016/S0169-409X(00)00129-0
  • Luccio-Camelo, D. C., & Prins, G. S. (2011). Disruption of androgen receptor signaling in males by environmental chemicals. The Journal of Steroid Biochemistry and Molecular Biology, 127(1), 74–82. https://doi.org/10.1016/j.jsbmb.2011.04.004
  • Manisha, Chauhan, S., Kumar, P., & Kumar, A. (2019). Development of prediction model for fructose- 1,6- bisphosphatase inhibitors using the Monte Carlo method. SAR and QSAR in Environmental Research, 30(3), 145–159. https://doi.org/10.1080/1062936X.2019.1568299
  • Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009). AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry, 30(16), 2785–2791. https://doi.org/10.1002/jcc.21256
  • Nesmerak, K., Toropov, A., Toropova, A., Ertan-Bolelli, T., & Yildiz, I. (2017). QSAR of antimycobacterial activity of benzoxazoles by optimal SMILES-based descriptors. Medicinal Chemistry Research, 26, 3203-3208. https://doi.org/10.1007/s00044-017-2013-8
  • Nimbhal, M., Bagri, K., Kumar, P., & Kumar, A. (2020). The index of ideality of correlation: A statistical yardstick for better QSAR modeling of glucokinase activators. Structural Chemistry, 31(2), 831–839. https://doi.org/10.1007/s11224-019-01468-w
  • Ojha, P. K., Mitra, I., Das, R. N., & Roy, K. (2011). Further exploring rm2 metrics for validation of QSPR models. Chemometrics and Intelligent Laboratory Systems, 107(1), 194–205. https://doi.org/10.1016/j.chemolab.2011.03.011
  • Piir, G., Sild, S., & Maran, U. (2021). Binary and multi-class classification for androgen receptor agonists, antagonists and binders. Chemosphere, 262, 128313. https://doi.org/10.1016/j.chemosphere.2020.128313
  • Pratim Roy, P., Paul, S., Mitra, I., & Roy, K. (2009). On two novel parameters for validation of predictive QSAR models. Molecules, 14(5), 1660–1701. https://doi.org/10.3390/molecules14051660
  • Rani, P., Kiran, Chahal, S., Priyanka, Kataria, R., Kumar, P., Kumar, S., & Sindhu, J. (2022). Unravelling the thermodynamics and binding interactions of bovine serum albumin (BSA) with thiazole based carbohydrazide: Multi-spectroscopic, DFT and molecular dynamics approach. Journal of Molecular Structure, 1270, 133939. https://doi.org/10.1016/j.molstruc.2022.133939
  • Rescifina, A., Floresta, G., Marrazzo, A., Parenti, C., Prezzavento, O., Nastasi, G., Dichiara, M., & Amata, E. (2017). Sigma-2 receptor ligands QSAR model dataset. Data in Brief, 13, 514–535. https://doi.org/10.1016/j.dib.2017.06.022
  • Roy, K., Chakraborty, P., Mitra, I., Ojha, P. K., Kar, S., & Das, R. N. (2013). Some case studies on application of “rm2” metrics for judging quality of quantitative structure-activity relationship predictions: Emphasis on scaling of response data. Journal of Computational Chemistry, 34(12), 1071–1082. https://doi.org/10.1002/jcc.23231
  • Roy, K., Kar, S., & Ambure, P. (2015). On a simple approach for determining applicability domain of QSAR models. Chemometrics and Intelligent Laboratory Systems, 145, 22–29. https://doi.org/10.1016/j.chemolab.2015.04.013
  • Roy, P. P., Leonard, J. T., & Roy, K. (2008). Exploring the impact of size of training sets for the development of predictive QSAR models. Chemometrics and Intelligent Laboratory Systems, 90(1), 31–42.
  • Hansch, C., Sammes, P. G., & Taylor, J. B. (1990). Computers and the Medicinal Chemist. In: Comprehensive medicinal chemistry. Pregamon Press, Oxford, 4, 33-58
  • Schug, T. T., Janesick, A., Blumberg, B., & Heindel, J. J. (2011). Endocrine disrupting chemicals and disease susceptibility. The Journal of Steroid Biochemistry and Molecular Biology, 127(3), 204–215. https://doi.org/10.1016/j.jsbmb.2011.08.007
  • Schüürmann, G., Ebert, R.-U., Chen, J., Wang, B., & Kühne, R. (2008). External validation and prediction employing the predictive squared correlation coefficient-test set activity mean vs training set activity mean. Journal of Chemical Information and Modeling, 48(11), 2140–2145. https://doi.org/10.1021/ci800253u
  • Sharma, S., Kumar, P., & Chandra, R. (2019). Chapter 7 - Applications of BIOVIA materials studio, LAMMPS, and GROMACS in various fields of science and engineering. Micro and Nano Technologies, 329–341. https://doi.org/10.1016/B978-0-12-816954-4.00007-3
  • Simon, L., Imane, A., Srinivasan, K. K., Pathak, L., & Daoud, I. (2017). In silico drug-designing studies on flavanoids as anticolon cancer agents: pharmacophore mapping, molecular docking, and Monte Carlo method-based QSAR modeling. Interdisciplinary Sciences-Computational Life Sciences, 9(3), 445–458. https://doi.org/10.1007/s12539-016-0169-4
  • Sokolović, D., Ranković, J., Stanković, V., Stefanović, R., Karaleić, S., Mekić, B., Milenković, V., Kocić, J., & Veselinović, A. M. (2017). QSAR study of dipeptidyl peptidase-4 inhibitors based on the Monte Carlo method. Medicinal Chemistry Research, 26(4), 796–804. https://doi.org/10.1007/s00044-017-1792-2
  • Sokolović, D., Stanković, V., Toskić, D., Lilić, L., Ranković, G., Ranković, J., Nedin-Ranković, G., & Veselinović, A. M. (2016). Monte Carlo-based QSAR modeling of dimeric pyridinium compounds and drug design of new potent acetylcholine esterase inhibitors for potential therapy of myasthenia gravis. Structural Chemistry, 27(5), 1511–1519. https://doi.org/10.1007/s11224-016-0776-z
  • Stoičkov, V., Stojanović, D., Tasić, I., Šarić, S., Radenković, D., Babović, P., Sokolović, D., & Veselinović, A. M. (2018). QSAR study of 2,4-dihydro-3H-1,2,4-triazol-3-ones derivatives as angiotensin II AT1 receptor antagonists based on the Monte Carlo method. Structural Chemistry, 29(2), 441–449. https://doi.org/10.1007/s11224-017-1041-9
  • Tian, Y., Zhang, S., Yin, H., & Yan, A. (2020). Quantitative structure-activity relationship (QSAR) models and their applicability domain analysis on HIV-1 protease inhibitors by machine learning methods. Chemometrics and Intelligent Laboratory Systems, 196, 103888. https://doi.org/10.1016/j.chemolab.2019.103888
  • aToropov, A. A., & Toropova, A. P. (2019). Use of the index of ideality of correlation to improve predictive potential for biochemical endpoints. Toxicology Mechanisms and Methods, 29(1), 43–52. https://doi.org/10.1080/15376516.2018.1506851
  • bToropov, A. A., & Toropova, A. P. (2019). QSAR as a random event: criteria of predictive potential for a chance model. Structural Chemistry, 30(5), 1677–1683. https://doi.org/10.1007/s11224-019-01361-6
  • Toropov, A. A., Carbó-Dorca, R., & Toropova, A. P. (2018). Index of ideality of correlation: new possibilities to validate QSAR: a case study. Structural Chemistry, 29(1), 33–38. https://doi.org/10.1007/s11224-017-0997-9
  • Toropov, A. A., & Toropova, A. P. (2017). The index of ideality of correlation: a criterion of predictive potential of QSPR/QSAR models? Mutation Research/Genetic Toxicology and Environmental Mutagenesis, 819, 31–37. https://doi.org/10.1016/j.mrgentox.2017.05.008
  • Toropov, A. A., & Toropova, A. P. (2018). Predicting cytotoxicity of 2-phenylindole derivatives against breast cancer cells using index of ideality of correlation. Anticancer Research, 38(11), 6189–6194. https://doi.org/10.21873/anticanres.12972
  • Toropov, A. A., & Toropova, A. P. (2020). The Monte Carlo method as a tool to build up predictive QSPR/QSAR. Current Computer-Aided Drug Design, 16(3), 197–206. https://doi.org/10.2174/1573409915666190328123112
  • Toropov, A. A., Toropova, A. P., & Benfenati, E. (2009). Additive SMILES-Based Carcinogenicity Models: Probabilistic Principles in the Search for Robust Predictions. In International Journal of Molecular Sciences, 10(7), 3106–3127. https://doi.org/10.3390/ijms10073106
  • Toropova, A. P., & Toropov, A. A. (2017). The index of ideality of correlation: A criterion of predictability of QSAR models for skin permeability? Science of the Total Environment, 586, 466–472. https://doi.org/10.1016/j.scitotenv.2017.01.198
  • Toropova, A. P., & Toropov, A. A. (2018). CORAL: QSAR models for carcinogenicity of organic compounds for male and female rats. Computational Biology and Chemistry, 72, 26–32. https://doi.org/10.1016/j.compbiolchem.2017.12.012
  • Toropova, A. P., & Toropov, A. A. (2019). The index of ideality of correlation: improvement of models for toxicity to algae. Natural Product Research, 33(15), 2200–2207. https://doi.org/10.1080/14786419.2018.1493591
  • Toropova, A. P., Toropov, A. A., Carnesecchi, E., Benfenati, E., & Dorne, J. L. (2020). The index of ideality of correlation: models for flammability of binary liquid mixtures. Chemical Papers, 74(2), 601–609. https://doi.org/10.1007/s11696-019-00903-w
  • Toropova, A. P., Toropov, A. A., Leszczynska, D., & Leszczynski, J. (2017). CORAL and Nano-QFAR: quantitative feature-activity relationships (QFAR) for bioavailability of nanoparticles (ZnO, CuO, Co3O4, and TiO2). Ecotoxicology and Environmental Safety, 139, 404–407. https://doi.org/10.1016/j.ecoenv.2017.01.054
  • Toropova, A. P., Toropov, A. A., Veselinović, A. M., Veselinović, J. B., Leszczynska, D., & Leszczynski, J. (2019). Semi-correlations combined with the index of ideality of correlation: a tool to build up model of mutagenic potential. Molecular and Cellular Biochemistry, 452(1), 133–140. https://doi.org/10.1007/s11010-018-3419-4
  • Wiffen, P. J. (2013). Methadone for chronic noncancer pain (cncp) in adults. Journal of Pain and Palliative Care Pharmacotherapy, 27(2), 180. https://doi.org/10.3109/15360288.2013.810896
  • Xu, L.-C., Liu, L., Ren, X.-M., Zhang, M.-R., Cong, N., Xu, A.-Q., & Shao, J.-H. (2008). Evaluation of androgen receptor transcriptional activities of some pesticides in vitro. Toxicology, 243(1), 59–65. https://doi.org/10.1016/j.tox.2007.09.028

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