Virtual Screening of Anti-Cancer Compounds: Application of Monte Carlo Technique

Author(s): Alla P. Toropova* , Andrey A. Toropov , Emilio Benfenati , Danuta Leszczynska , Jerzy Leszczynski .

Journal Name: Anti-Cancer Agents in Medicinal Chemistry

Volume 19 , Issue 2 , 2019

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Abstract:

Possibility and necessity of standardization of predictive models for anti-cancer activity are discussed. The hypothesis about rationality of common quantitative analysis of anti-cancer activity and carcinogenicity is developed. Potential of optimal descriptors to be used as a tool to build up predictive models for anti-cancer activity is examined from practical point of view. Various perspectives of application of optimal descriptors are reviewed. Stochastic nature of phenomena which are related to carcinogenic potential of various substances can be successfully detected and interpreted by the Monte Carlo technique. Hypothesises related to practical strategy and tactics of the searching for new anticancer agents are suggested.

Keywords: QSAR, SMILES, anti-cancer activity, Monte Carlo method, CORAL software, virtual screening.

[1]
Amin, S.A.; Adhikari, N.; Baidya, S.K.; Gayen, S.; Jha, T. Structural refinement and prediction of potential CCR2 antagonists through validated multi-QSAR modeling studies. J. Biomol. Struct. Dyn., 2018, 1-20.
[2]
Toropova, A.P.; Toropov, A.A. CORAL: QSAR models for carcinogenicity of organic compounds for male and female rats. Comput. Biol. Chem., 2018, 72, 26-32.
[3]
Toropova, A.P.; Toropov, A.A.; Veselinović, A.M.; Veselinović, J.B.; Leszczynska, D.; Leszczynski, J. Semi-correlations combined with the Index of Ideality of Correlation: A tool to build up model of mutagenic potential. Mol. Cell. Biochem., 2018, 452(1-2), 1-8.
[4]
Sokolović, D.; Ranković, J.; Stanković, V.; Stefanović, R.; Karaleić, S.; Mekić, B.; Milenković, V.; Kocić, J.; Veselinović, A.M. QSAR study of dipeptidyl peptidase-4 inhibitors based on the Monte Carlo method. Med. Chem. Res., 2017, 26, 796-804.
[5]
Sokolović, D.; Stanković, V.; Toskić, D.; Lilić, L.; Ranković, G.; Ranković, J.; Nedin-Ranković, G.; Veselinović, A.M. Monte Carlo-based QSAR modeling of dimeric pyridinium compounds and drug design of new potent acetylcholine esterase inhibitors for potential therapy of myasthenia gravis. Struct. Chem., 2016, 27, 1511-1519.
[6]
Islam, M.A.; Pillay, T.S. Simplified molecular input line entry system-based descriptors in QSAR modeling for HIV-protease inhibitors. Chemom. Intell. Lab. Syst., 2016, 153, 67-74.
[7]
Živković, J.V.; Trutić, N.V.; Veselinović, J.B.; Nikolić, G.M.; Veselinović, A.M. Monte Carlo method based QSAR modeling of maleimide derivatives as glycogen synthase kinase-3β inhibitors. Comput. Biol. Med., 2015, 64, 276-282.
[8]
Veselinović, A.M.; Veselinović, J.B.; Živković, J.V.; Nikolić, G.M. Application of smiles notation based optimal descriptors in drug discovery and design. Curr. Top. Med. Chem., 2015, 15, 1768-1779.
[9]
Begum, S.; Achary, P.G.R. Simplified molecular input line entry system-based: QSAR modelling for MAP kinase-interacting protein kinase (MNK1). SAR QSAR Environ. Res., 2015, 26, 343-361.
[10]
Veselinović, J.B.; Nikolić, G.M.; Trutić, N.V.; Živković, J.V.; Veselinović, A.M. Monte Carlo QSAR models for predicting organophosphate inhibition of acetylcholinesterase. SAR QSAR Environ. Res., 2015, 26, 449-460.
[11]
Fatemi, M.H.; Malekzadeh, H. CORAL: Predictions of retention indices of volatiles in cooking rice using representation of the molecular structure obtained by combination of SMILES and graph approaches. J. Iran. Chem. Soc, 2015, 12, 405-412.
[12]
Li, Q.; Ding, X.; Si, H.; Gao, H. QSAR model based on SMILES of inhibitory rate of 2,3-diarylpropenoic acids on AKR1C3. Chemom. Intell. Lab. Syst., 2014, 139, 132-138.
[13]
Worachartcheewan, A.; Nantasenamat, C.; Isarankura-Na-Ayudhya, C.; Prachayasittikul, V. QSAR study of H1N1 neuraminidase inhibitors from influenza a virus. Lett. Drug Des. Discov., 2014, 11, 420-427.
[14]
Kumar, A.; Chauhan, S. Use of simplified molecular input line entry system and molecular graph based descriptors in prediction and design of pancreatic lipase inhibitors. Future Med. Chem., 2018, 10(13), 1603-1622.
[15]
Speck-Planche, A.; Kleandrova, V.V.; Luan, F.; Cordeiro, M.N.D.S. Unified multi-target approach for the rational in silico design of anti-bladder cancer agents. Anticancer. Agents Med. Chem., 2013, 13, 791-800.
[16]
Toropov, A.A.; Toropova, A.P.; Benfenati, E.; Gini, G.; Leszczynska, D.; Leszczynski, J. Calculation of molecular features with apparent impact on both activity of mutagens and activity of anticancer agents. Anticancer. Agents Med. Chem., 2012, 12, 807-817.
[17]
Kleandrova, V.V.; Luan, F.; Speck-Planche, A.; Cordeiro, M.N.D.S. In silico assessment of the acute toxicity of chemicals: Recent advances and new model for multitasking prediction of toxic effect. Mini Rev. Med. Chem., 2015, 15, 677-686.
[18]
Speck-Planche, A.; Cordeiro, M.N.D.S. A general ann-based multitasking model for the discovery of potent and safer antibacterial agents. Methods Mol. Biol., 2015, 1260, 45-64.
[19]
Speck-Planche, A.; Cordeiro, M.N.D.S. Multi-target QSAR approaches for modeling protein inhibitors. Simultaneous prediction of activities against biomacromolecules present in gram-negative bacteria. Curr. Top. Med. Chem., 2015, 15, 1801-1813.
[20]
Speck-Planche, A.; Cordeiro, M.N.D.S. Multitasking models for quantitative structure-biological effect relationships: Current status and future perspectives to speed up drug discovery. Expert Opin. Drug Discov., 2015, 10, 245-256.
[21]
Speck-Planche, A.; Kleandrova, V.V.; Luan, F.; Cordeiro, M.N.D.S. Computational modeling in nanomedicine: Prediction of multiple antibacterial profiles of nanoparticles using a quantitative structure-activity relationship perturbation model. Nanomedicine., 2015, 10, 193-204.
[22]
Scotti, L.; Scotti, M.T. In silico studies applied to natural products with potential activity against Alzheimer’s disease. Neuromethods, 2018, 132, 513-531.
[23]
Scotti, M.T.; Scotti, L.; Ishiki, H.M.; Peron, L.M.; de Rezende, L.; do Amaral, A.T. Variable-selection approaches to generate QSAR models for a set of antichagasic semicarbazones and analogues. Chemom. Intell. Lab. Syst., 2016, 154, 137-149.
[24]
Speck-Planche, A.; Kleandrova, V.V.; Scotti, M.T.; Cordeiro, M.N.D.S. 3D-QSAR methodologies and molecular modeling in bioinformatics for the search of novel anti-HIV therapies: Rational design of entry inhibitors. Curr. Bioinform., 2013, 8(4), 452-464.
[25]
Toropova, A.P.; Toropov, A.A.; Beeg, M.; Gobbi, M.; Salmona, M. Utilization of the monte carlo method to build up QSAR models for hemolysis and cytotoxicity of antimicrobial peptides. Curr. Drug Discov. Technol., 2017, 14(4), 229-243.
[26]
Duchowicz, P.R.; Bacelo, D.E.; Fioressi, S.E.; Palermo, V.; Ibezim, N.E.; Romanelli, G.P. QSAR studies of indoyl aryl sulfides and sulfones as reverse transcriptase inhibitors. Med. Chem. Res., 2018, 27(2), 420-428.
[27]
Toropov, A.A.; Toropova, A.P.; Benfenati, E.; Salmona, M. Mutagenicity, anticancer activity, and blood brain barrier: Similarity and dissimilarity of molecular alerts. Toxicol. Mech. Methods, 2018, 28(5), 321-327.
[28]
Toropov, A.A.; Toropova, A.P.; Raska, I.; Leszczynska, D.; Leszczynski, J. Comprehension of drug toxicity: Software and databases. Comput. Biol. Med., 2014, 45, 20-25.
[29]
TOXNET, U.S. National Library of Medicine. https://toxnet.nlm. nih.gov/ (Accessed May 11, 2017).
[30]
CORAL software, 2016. Available from. http://www.insilico.eu/ coral/ (Accessed May 11, 2017).
[31]
Toropova, A.P.; Toropov, A.A. CORAL software: Prediction of carcinogenicity of drugs by means of the Monte Carlo method. Eur. J. Pharm. Sci., 2014, 52, 21-25.
[32]
Toropov, A.A.; Toropova, A.P.; Benfenati, E.; Gini, G.; Leszczynska, D.; Leszczynski, J. SMILES-based QSAR approaches for carcinogenicity and anticancer activity: Comparison of correlation weights for identical SMILES attributes. Anticancer. Agents Med. Chem., 2011, 11, 974-982.
[33]
Toropova, A.P.; Toropov, A.A.; Diaza, R.G.; Benfenati, E.; Gini, G. Analysis of the co-evolutions of correlations as a tool for QSAR-modeling of carcinogenicity: An unexpected good prediction based on a model that seems untrustworthy. Cent. Eur. J. Chem., 2011, 9, 165-174.
[34]
Toropov, A.A.; Toropova, A.P.; Benfenati, E. SMILES-based optimal descriptors: QSAR modeling of carcinogenicity by balance of correlations with ideal slopes. Eur. J. Med. Chem., 2010, 45, 3581-3587.
[35]
Toropov, A.A.; Toropova, A.P.; Benfenati, E.; Manganaro, A. QSAR modelling of carcinogenicity by balance of correlations. Mol. Divers., 2009, 13, 367-373.
[36]
Toropov, A.A.; Toropova, A.P.; Benfenati, E. Additive SMILES-based carcinogenicity models: Probabilistic principles in the search for robust predictions. Int. J. Mol. Sci., 2009, 10, 3106-3127.
[37]
Song, F.; Zhang, A.; Liang, H.; Cui, L.; Li, W.; Si, H.; Duan, Y.; Zhai, H. QSAR study for carcinogenic potency of aromatic amines based on GEP and MLPs. I. J. Environ. Res. Public Health, 2016, 13(11), 1141.
[38]
Harding, A.P.; Popelier, P.L.A.; Harvey, J.; Giddings, A.; Foster, G.; Kranz, M. Evaluation of aromatic amines with different purities and different solvent vehicles in the Ames test. Regul. Toxicol. Pharmacol., 2015, 71, 244-250.
[39]
Garrigós, M.C.; Reche, F.; Marín, M.L.; Pernías, K.; Jiménez, A. Optimization of the extraction of azo colorants used in toy products. J. Chromatogr. A, 2002, 963, 427-433.
[40]
Sanchis, Y.; Coscollà, C.; Roca, M.; Yusà, V. Target analysis of primary aromatic amines combined with a comprehensive screening of migrating substances in kitchen utensils by liquid chromatography-high resolution mass spectrometry. Talanta, 2015, 138, 290-297.
[41]
Petrescu, A-M.; Ilia, G. Molecular docking study to evaluate the carcinogenic potential and mammalian toxicity of thiophosphonate pesticides by cluster and discriminant analysis. Environ. Toxicol. Pharmacol., 2016, 47, 62-78.
[42]
Toropov, A.A.; Toropova, A.P. The Index of Ideality of Correlation: A criterion of predictive potential of QSPR/QSAR models? Mut. Res. Gen. Tox. En. Mut., 2017, 819, 31-37.
[43]
Toropova, A.P.; Toropov, A.A. CORAL: Monte carlo method to predict endpoints for medical chemistry. Mini Rev. Med. Chem., 2018, 18(5), 382-391.
[44]
Toropov, A.A.; Toropova, A.P.; Raitano, G.; Benfenati, E. CORAL: Building up QSAR models for the chromosome aberration test. Saudi J. Biol. Sci., 2018, 40(2)
[45]
Toropova, A.P.; Toropov, A.A. Quasi-SMILES: Quantitative structure-activity relationships to predict anti-cancer activity. Mol. Divers., 2018, 1-10.
[http://dx.doi.org/10.1007/s11030-018-9881-9]
[46]
Toropov, A.A.; Toropova, A.P. Use of the index of ideality of correlation to improve predictive potential for biochemical endpoints. Toxicol. Mech. Methods, 2018, 29(1), 1-23.
[47]
Bouhedjar, K.; Manganelli, S.; Gini, G.; Toropov, A.A.; Toropova, A.P.; Ali-Mokhnache, S.; Messadi, D. QSAR modeling useful in anti-cancer drug discovery: Prediction of V600EBRAF-Dependent P-ERK using monte carlo method. J. Med. Chem. Toxicol, 2017, 2(1), 1-6.
[48]
Toropova, M.A.; Raska, Jr, I.; Toropova, A.P.; Raskova, M. CORAL software: Analysis of impacts of pharmaceutical agents upon metabolism via the optimal descriptors. Curr. Drug Metab., 2017, 18(6), 500-510.
[49]
Toropova, M.A. Drug metabolism as an object of computational analysis by the monte carlo method. Curr. Drug Metab., 2017, 18(12), 1123-1131.
[50]
Pradeep, P.; Povinelli, R.J.; White, S.; Merrill, S.J. An ensemble model of QSAR tools for regulatory risk assessment. J. Cheminform., 2016, 8, 1-9.
[51]
Toropov, A.A.; Toropova, A.P.; Benfenati, E.; Gini, G. OCWLGI Descriptors: Theory and praxis. Curr. Comput. Aid. Drug Des, 2013, 9, 226-232.
[52]
Weininger, D.; Weininger, A.; Weininger, J.L. SMILES. 2. Algorithm for generation of unique SMILES notation. J. Chem. Inf. Comput. Sci., 1989, 29, 97-101.
[53]
Toropova, A.P.; Toropov, A.A.; Martyanov, S.E.; Benfenati, E.; Gini, G.; Leszczynska, D.; Leszczynksy, J. CORAL: Monte carlo method as a tool for the prediction of the bioconcentration factor of industrial pollutants. Mol. Inform., 2013, 32, 145-154.
[54]
Toropov, A.A.; Toropova, A.P.; Martyanov, S.E.; Benfenati, E.; Gini, G.; Leszczynska, D.; Leszczynksy, J. CORAL: Predictions of rate constants of hydroxyl radical reaction using representation of the molecular structure obtained by combination of SMILES and graph approaches. Chemom. Intell. Lab. Syst., 2012, 112, 65-70.
[55]
United States Environmental Protection Agency. Available at. https://www.epa.gov/chemical-research/distributed-structure-searchable-toxicity-dsstox-database (Accessed May 11, 2017).
[56]
Toropov, A.A.; Toropova, A.P.; Voropaeva, N.L.; Ruban, I.N.; Rashidova, S.S.H. Approval of the random-mutual-orientation statistics index as a basis for searching for “structure property” relationships in coordination compounds. Russ. J. Coord. Chem., 1996, 22, 578-580.
[57]
Toropov, A.A.; Toropova, A.P.; Ismailov, T.T.; Voropaeva, N.L.; Ruban, I.N.; Rashidova, S.S.H. The use of deformation indices of the ideal symmetry model in calculations of the thermodynamic properties of organic compounds. Russ. J. Phys. Chem. A, 1996, 70, 1081-1084.
[58]
Garkani-Nejad, Z.; Shahhoseini, M. Prediction of the anti-cancer activity of spiro derivatives of parthenin based on molecular modeling methods and docking. Med. Chem. Res., 2014, 23, 3403-3417.
[59]
Pogorzelska, A.; Slawinski, J.; Brozewicz, K.; Ulenberg, S.; Baczek, T. Novel 3-amino-6-chloro-7-(azol-2 or 5-yl)-1,1-dioxo-1,4,2-benzodithiazine derivatives with anticancer activity: Synthesis and QSAR study. Molecules, 2015, 20, 21960-21970.
[60]
Qian, J-Z.; Wang, B-C.; Fan, Y.; Tan, J.; Yang, X. QSAR study of flavonoid-metal complexes and their anticancer activities. J. Struct. Chem., 2015, 56, 338-345.
[61]
Ghanbari, Z.; Housaindokht, M.R.; Izadyar, M.; Bozorgmehr, M.R.; Eshtiagh-Hosseini, H.; Bahrami, A.R.; Matin, M.M.; Khoshkholgh, M.J. Structure-activity relationship for Fe(III)-salen-like complexes as potent anticancer agents. Sci. World J., 2014, 745649.
[62]
Ivkovic, B.M.; Nikolic, K.; Ilic, B.B.; Žižak, Ž.S.; Novakovic, R.B.; Cudina, O.A.; Vladimirov, S.M. Phenylpropiophenone derivatives as potential anticancer agents: Synthesis, biological evaluation and quantitative structure-activity relationship study. Eur. J. Med. Chem., 2013, 63, 239-255.
[63]
Toropov, A.A.; Toropova, A.P.; Benfenati, E.; Leszczynska, D.; Leszczynski, J. QSAR analysis of 1,4-dihydro-4-oxo-1-(2-thiazolyl)-1,8-naphthyridines exhibiting anticancer activity by optimal SMILES-based descriptors. J. Math. Chem., 2010, 47, 647-666.
[64]
Benfenati, E.; Toropov, A.A.; Toropova, A.P.; Manganaro, A.; Gonella Diaza, R. CORAL software: QSAR for anticancer agents. Chem. Biol. Drug Des., 2011, 77, 471-476.
[65]
Worachartcheewan, A.; Mandi, P.; Prachayasittikul, V.; Toropova, A.P.; Toropov, A.A.; Nantasenamat, C. Large-scale QSAR study of aromatase inhibitors using SMILES-based descriptors. Chemom. Intell. Lab. Syst., 2014, 138, 120-126.
[66]
Trinh, T.X.; Choi, J.S.; Jeon, H.; Byun, H.G.; Yoon, T.H.; Kim, J. Quasi-SMILES-based nano-quantitative structure-activity relationship model to predict the cytotoxicity of multiwalled carbon nanotubes to human lung cells. Chem. Res. Toxicol., 2018, 31(3), 183-190.
[67]
OECD, 2007. (Organization for Economic Co-operation and Development). guidance document on the validation of (quantitative) structure-activity relationship [(Q)SAR] Models No. 69.
[68]
Toropova, M.A.; Raška, I., Jr; Toropov, A.A.; Rašková, M. The utilization of the Monte Carlo technique for rational drug discovery. Comb. Chem. High Throughput Screen., 2016, 19(8), 676-687.
[69]
OECD Environment, Health and Safety Publications Series on the Safety of Manufactured Nanomaterials No. 64. Available at http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/ ?cote=env/jm/mono(2016)3&doclanguage=en (Accessed May 15, 2017).
[70]
Lebedeva, G.; Sorokin, A.; Faratian, D.; Mullen, P.; Goltsov, A.; Langdon, S.P.; Harrison, D.J.; Goryanin, I. Model-based global sensitivity analysis as applied to identification of anti-cancer drug targets and biomarkers of drug resistance in the ErbB2/3 network. Eur. J. Pharm. Sci., 2012, 46, 244-258.
[71]
Hettle, R.; Posnett, J.; Borrill, J. Challenges in economic modeling of anticancer therapies: An example of modeling the survival benefit of olaparib maintenance therapy for patients with BRCA-mutated platinum-sensitive relapsed ovarian cancer. J. Med. Econ., 2015, 18, 516-524.
[72]
Afantitis, A.; Melagraki, G.; Sarimveis, H.; Koutentis, P.A.; Markopoulos, J.; Igglessi-Markopoulou, O. Development and evaluation of a QSPR model for the prediction of diamagnetic susceptibility. QSAR Comb. Sci., 2008, 27, 432-436.
[73]
Melagraki, G.; Ntougkos, E.; Rinotas, V.; Papaneophytou, C.; Leonis, G.; Mavromoustakos, T.; Kontopidis, G.; Douni, E.; Afantitis, A.; Kollias, G. Cheminformatics-aided discovery of small-molecule Protein-Protein Interaction (PPI) dual inhibitors of Tumor Necrosis Factor (TNF) and Receptor Activator of NF-κB Ligand (RANKL). PLOS Comput. Biol., 2017, 13, e1005372.
[74]
Zhang, S.; Golbraikh, A.; Oloff, S.; Kohn, H.; Tropsha, A. A novel automated lazy learning QSAR (ALL-QSAR) approach: Method development, applications, and virtual screening of chemical databases using validated ALL-QSAR models. J. Chem. Inf. Model., 2006, 46, 1984-1995.
[75]
Melagraki, G.; Afantitis, A. A risk assessment tool for the virtual screening of metal oxide nanoparticles through enalos in silico nano platform. Curr. Top. Med. Chem., 2015, 15, 1827-1836.
[76]
Toropova, A.P.; Toropov, A.A.; Veselinović, J.B.; Veselinović, A.M. QSAR as a random event: A case of NOAEL. Environ. Sci. Pollut. Res. Int., 2015, 22, 8264-8271.
[77]
Toropova, A.P.; Toropov, A.A.; Rallo, R.; Leszczynska, D.; Leszczynski, J. Optimal descriptor as a translator of eclectic data into prediction of cytotoxicity for metal oxide nanoparticles under different conditions. Ecotoxicol. Environ. Saf., 2015, 112, 39-45.


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VOLUME: 19
ISSUE: 2
Year: 2019
Page: [148 - 153]
Pages: 6
DOI: 10.2174/1871520618666181025122318
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