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Combinatorial Chemistry & High Throughput Screening

Author(s): Mayara S. Maia, Natália F. de Sousa, Gabriela C.S. Rodrigues, Alex F.M. Monteiro, Marcus T. Scotti and Luciana Scotti*

DOI: 10.2174/1386207323666200226094940

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Lignans and Neolignans Anti-tuberculosis Identified by QSAR and Molecular Modeling

Page: [504 - 516] Pages: 13

  • * (Excluding Mailing and Handling)

Abstract

Background: Tuberculosis is a disease with high incidence and high mortality rate, especially in Brazil. Although there are several medications available for treatment, in cases of resistance, there is a need to use more than one medication.

Objective: Therefore, cases of toxicity increase and reports of resistance have been worrying the population. In addition, some medications have a short period of effectiveness. To achieve the goal, ligand-based and structure-based approaches were used.

Methods: Thus, in an attempt to discover potent inhibitors against Mycobacterium tuberculosis enzymes, we sought to identify natural products with high therapeutic potential for the treatment of Tuberculosis through QSAR, Molecular Modeling and ADMET studies.

Results: The results showed that the models generated from two sets of molecules with known activity against M. tuberculosis enzymes InhA and PS were able to select 11 and 8 compounds, respectively, between Lignans and Neolignans with 50 to 60% activity probability. In addition, molecular docking contributed to confirm the mechanism of action of compounds and increase the accuracy of methodologies. All molecules showed higher binding energy values for the drug Isoniazid. We conclude that compounds 33, 34, 110, 114 and 133 are promising for InhA target and compounds 07, 08, 19, 21, 42, 48, 75 and 141 for target PS. In addition, most molecules did not show any toxicity according to the evaluated parameters.

Conclusion: Therefore, Lignans and Neolignans may be an alternative for the treatment of Tuberculosis.

Keywords: Lignans, neolignans, QSAR, molecular modeling, tuberculosis, molecular docking.

[1]
Kozakevich, G.V.; da Silva, R.M. Tuberculose – Revisão de Literatura. Arq. Catarin Med., 2015, 4(44), 34-47.
[2]
Bao, Y.; Zhao, X.; Wang, L.; Qian, W.; Sun, J. Morphology-based classification of mycobacteria-infected macrophages with convolutional neural network: reveal EsxA-induced morphologic changes indistinguishable by naked eyes. Transl. Res., 2019, 212, 1-13.
[http://dx.doi.org/10.1016/j.trsl.2019.06.001 ] [PMID: 31287998]
[3]
Baldin, V.P.; Bertin de Lima Scodro, R.; Mariano Fernandez, C.M.; Ieque, A.L.; Caleffi-Ferracioli, K.R.; Dias Siqueira, V.L.; de Almeida, A.L.; Gonçalves, J.E.; Garcia Cortez, D.A.; Cardoso, R.F. Ginger essential oil and fractions against Mycobacterium Spp. J. Ethnopharmacol., 2019, 244112095
[http://dx.doi.org/10.1016/j.jep.2019.112095]
[4]
Pelissari, D.M.; Diaz-quijano, F.A. Impact of incarceration on tuberculosis incidence and its interaction with income distribution inequality in Brazil., 2020, 114, 23-30.
[http://dx.doi.org/10.1093/trstmh/trz088]
[5]
Holden, I.K.; Lillebaek, T.; Andersen, P.H.; Bjerrum, S.; Wejse, C.; Johansen, I.S. Extrapulmonary tuberculosis in Denmark from 2009 to 2014; characteristics and predictors for treatment outcome. Open Forum Infect. Dis., 2019, 6(10)ofz388
[http://dx.doi.org/10.1093/ofid/ofz388 ] [PMID: 31660351]
[6]
Wada, T.; Hanibuchi, M.; Saijo, A. Acute hypercalcemia and hypervitaminosis D associated with pulmonary tuberculosis in an elderly patient: A case report and review of the literature. J. Med. Invest., 2019, 66(3.4), 351-354.
[http://dx.doi.org/10.2152/jmi.66.351] [PMID: 31656304]
[7]
Zhou, Z.; Zheng, Y.; Wang, L. A comparative study on the value of Xpert MTB/RIF and T-SPOT.TB tests in the diagnosis of bone and joint tuberculosis. Clin. Chim. Acta, 2020, 500, 115-119.
[http://dx.doi.org/10.1016/j.cca.2019.09.026 ] [PMID: 31654631]
[8]
Rabahi, M.F.; Laerte, J.; Júnior, S.; Carolina, A.; Ferreira, G.; Tannus-silva, D.G.S.; Conde, M.B.; De Medicina, F. Tratamento Da Tuberculose., 2017, 43(5), 472-486.
[9]
Siddique, A.A.; Schnitzer, M.E.; Bahamyirou, A.; Wang, G.; Holtz, T.H.; Migliori, G.B.; Sotgiu, G.; Gandhi, N.R.; Vargas, M.H.; Menzies, D.; Benedetti, A. Causal inference with multiple concurrent medications: a comparison of methods and an application in multidrug-resistant tuberculosis. Stat. Methods Med. Res., 2018, 28(12), 3534-3549.
[http://dx.doi.org/10.1177/0962280218808817 ] [PMID: 30381005]
[10]
Momin, M.A.M.; Rangnekar, B.; Larson, I.; Sinha, S.; Das, S.C. Dry powder formulation combining bedaquiline with pyrazinamide for latent and drug-resistant tuberculosis. Adv. Powder Technol., 2019, 30(11), 2473-2482.
[http://dx.doi.org/10.1016/j.apt.2019.07.016]
[11]
Arbex, M.A.; de Siqueira, H.R.; D’Ambrosio, L.; Migliori, G.B. The challenge of managing extensively drug-resistant tuberculosis at a referral hospital in the state of são paulo, brazil: a report of three cases. J. Bras. Pneumol., 2015, 41(6), 554-559.
[http://dx.doi.org/10.1590/s1806-37562015000000299 ] [PMID: 26785966]
[12]
Costa, P.R.R. Natural products as starting point for the discovery of new bioactive compounds: drug candidates with antiophidic, anticancer and antiparasitic properties. Rev. Virtual Química, 2009, 1(1), 58-66.
[http://dx.doi.org/10.5935/1984-6835.20090008]
[13]
De Souza, V.A.; Nakamura, C.V.; Corrêa, A.G. Antichagasic activity of lignans and neolignans. Rev. Virtual Quim, 2012, 4(3), 197-207.
[14]
Wu, S. Chemical constituents and biological activity profiles on pleione (Orchidaceae). 2019, 24(17), 3195.
[http://dx.doi.org/10.3390/molecules24173195] [PMID: 31484345]
[15]
Abdolmaleki, A.; Ghasemi, J.B.; Ghasemi, F. Computer aided drug design for multi-target drug design: SAR/QSAR, molecular docking and pharmacophore methods. Curr. Drug Targets, 2017, 18(5), 556-575.
[http://dx.doi.org/10.2174/1389450117666160101120822 ] [PMID: 26721410]
[16]
Li, Y.; Xie, S.; Ying, J.; Wei, W.; Gao, K. Chemical structures of lignans and neolignans isolated from lauraceae. Molecules, 2018, 23(12)E3164
[http://dx.doi.org/10.3390/molecules23123164 ] [PMID: 30513687]
[17]
Gaulton, A.; Bellis, L.J.; Bento, A.P.; Chambers, J.; Davies, M.; Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; Al-Lazikani, B.; Overington, J.P. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res., 2012, 40(Database issue), D1100-D1107.
[http://dx.doi.org/10.1093/nar/gkr777 ] [PMID: 21948594]
[18]
Tropsha, A. Best practices for QSAR model development, validation, and exploitation. Mol. Inform., 2010, 29(6-7), 476-488.
[19]
Fourches, D.; Muratov, E.; Tropsha, A. Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. J. Chem. Inf. Model., 2010, 50(7), 1189-1204.
[http://dx.doi.org/10.1021/ci100176x ] [PMID: 20572635]
[20]
Alves, V.M.; Braga, R.C.; Muratov, E.N.; Horta, C. Chemoinformatics: an introduction.Quim. Nova, 2018, 41(2), 202-212.
[http://dx.doi.org/10.21577/0100-4042.20170145]
[21]
Mauri, A.; Consonni, V.; Pavan, M.; Todeschini, R. DRAGON software: an easy approach to molecular descriptor calculations. Match (Mulh.), 2006, 56(2), 237-248.
[22]
Salzberg, S.L. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, Inc., 1993. Machine Learn., 1994, 16, 235-240.
[http://dx.doi.org/10.1023/A:1022645310020]
[23]
Hall, M.; National, H.; Frank, E.; Holmes, G.; Pfahringer, B.; Reutemann, P.; Witten, I.H. The WEKA Data Mining Software. An Update. SIGKDD Explorations, 11(1), 10-18.
[24]
Matthews, B.W. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta, 1975, 405(2), 442-451.
[http://dx.doi.org/10.1016/0005-2795(75)90109-9 ] [PMID: 1180967]
[25]
Roy, K.; Kar, S.; Ambure, P. On a simple approach for determining applicability domain of QSAR models. Chemom. Intell. Lab. Syst., 2015, 145, 22-29.
[http://dx.doi.org/10.1016/j.chemolab.2015.04.013]
[26]
Scotti, L.; Ferreira, E.I.; Silva, M.S.; Scotti, M.T. Chemometric studies on natural products as potential inhibitors of the NADH oxidase from Trypanosoma cruzi using the VolSurf approach. Molecules, 2010, 15(10), 7363-7377.
[http://dx.doi.org/10.3390/molecules15107363 ] [PMID: 20966878]
[27]
Cruciani, G.; Pastor, M.; Guba, W. VolSurf: A new tool for the pharmacokinetic optimization of lead compounds. Eur. J. Pharm. Sci., 2000, 11(Suppl. 2), S29-S39.
[http://dx.doi.org/10.1016/S0928-0987(00)00162-7]
[28]
Scotti, L.; Fernandes, M.B.; Muramatsu, E.; Pasqualoto, K.F.M. de P. Emereciano, V.; Tavares, L.C.; da Silva, M. S.; Scotti, M.T. Self-organizing maps and VolSurf approach to predict aldose reductase inhibition by flavonoid compounds. Brazilian J. Pharmacogn., 2011, 21(1), 170-180.
[http://dx.doi.org/10.1590/S0102-695X2011005000028]
[29]
Brice, M.D.; Rodgers, J.R.; Kennard, O. Protein data bank: a computer-based archival file for macromolecular structures. Eur. J. Biochem., 1977, 80, 319-324.
[http://dx.doi.org/10.1111/j.1432-1033.1977.tb11885.x ] [PMID: 923582]
[30]
Bitencourt-Ferreira, G.; de Azevedo, W.F., Jr Molegro Virtual Docker for Docking. In: Docking Screens Drug Discovery; Humana: New York, NY, 2019; pp. 149-167.
[31]
Thomsen: R.; Christensen, M.H. MolDock: a new technique for high-accuracy molecular docking. J. Med. Chem., 2006, 49(11), 3315-3321.
[http://dx.doi.org/10.1021/jm051197e ] [PMID: 16722650]
[32]
Daina, A.; Michielin, O.; Zoete, V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep., 2017, 7, 42717.
[http://dx.doi.org/10.1038/srep42717 ] [PMID: 28256516]
[33]
Neves, B.J.; Braga, R.C.; Melo-Filho, C.C.; Moreira-Filho, J.T.; Muratov, E.N.; Andrade, C.H. QSAR-based virtual screening: advances and applications in drug discovery. Front. Pharmacol., 2018, 9, 1275.
[http://dx.doi.org/10.3389/fphar.2018.01275 ] [PMID: 30524275]
[34]
Adeniji, S.E.; Uba, S.; Uzairu, A.; Arthur, D.E. A derived QSAR model for predicting some compounds as potent antagonist against Mycobacterium tuberculosis: A Theoretical Approach. Adv. Prev. Med., 2019, 2019, 1-18.
[http://dx.doi.org/10.1155/2019/5173786]
[35]
Adeniji, S.E.; Uba, S.; Uzairu, A. QSAR modeling and molecular docking analysis of some active compounds against Mycobacterium tuberculosis receptor (Mtb CYP121). J. Pathogens, 2018, 20181018694
[http://dx.doi.org/10.1155/2018/1018694 ] [PMID: 29862081]
[36]
Ahamad, S.; Rahman, S.; Khan, F.I.; Dwivedi, N.; Ali, S.; Kim, J.; Imtaiyaz Hassan, M. QSAR based therapeutic management of M. tuberculosis. Arch. Pharm. Res., 2017, 40(6), 676-694.
[http://dx.doi.org/10.1007/s12272-017-0914-1 ] [PMID: 28456911]
[37]
Spagnuolo, L.A.; Eltschkner, S.; Yu, W.; Daryaee, F.; Davoodi, S.; Knudson, S.E.; Allen, E.K.H.; Merino, J.; Pschibul, A.; Moree, B.; Thivalapill, N.; Truglio, J.J.; Salafsky, J.; Slayden, R.A.; Kisker, C.; Tonge, P.J. Evaluating the contribution of transition-state destabilization to changes in the residence time of triazole-based InhA inhibitors. J. Am. Chem. Soc., 2017, 139(9), 3417-3429.
[http://dx.doi.org/10.1021/jacs.6b11148 ] [PMID: 28151657]
[38]
Silvestre, H.L.; Blundell, T.L.; Abell, C.; Ciulli, A. Integrated biophysical approach to fragment screening and validation for fragment-based lead discovery. Proc. Natl. Acad. Sci. USA, 2013, 110(32), 12984-12989.
[http://dx.doi.org/10.1073/pnas.1304045110 ] [PMID: 23872845]
[39]
Ciulli, A.; Scott, D.E.; Ando, M.; Reyes, F.; Saldanha, S.A.; Tuck, K.L.; Chirgadze, D.Y.; Blundell, S.T.L.; Abell, C. Inhibition of Mycobacterium tuberculosis pantothenate synthetase by analogues of the reaction intermediate. ChemBioChem, 2008, 9(16), 2606-2611.
[http://dx.doi.org/10.1002/cbic.200800437 ] [PMID: 18821554]
[40]
Scholar, E. Antituberculosis Agents. xPharm Compr. Pharmacol. Ref., 2007, No. Md, 1-3.