Ensemble-Based Modeling of Chemical Compounds with Antimalarial Activity

Author(s): Ana Yisel Caballero-Alfonso , Maykel Cruz-Monteagudo , Eduardo Tejera , Emilio Benfenati , Fernanda Borges , M. Natália D.S. Cordeiro , Vinicio Armijos-Jaramillo , Yunierkis Perez-Castillo* .

Journal Name: Current Topics in Medicinal Chemistry

Volume 19 , Issue 11 , 2019

Become EABM
Become Reviewer

Graphical Abstract:


Abstract:

Background: Malaria or Paludism is a tropical disease caused by parasites of the Plasmodium genre and transmitted to humans through the bite of infected mosquitos of the Anopheles genre. This pathology is considered one of the first causes of death in tropical countries and, despite several existing therapies, they have a high toxicity. Computational methods based on Quantitative Structure- Activity Relationship studies have been widely used in drug design work flows.

Objective: The main goal of the current research is to develop computational models for the identification of antimalarial hit compounds.

Materials and Methods: For this, a data set suitable for the modeling of the antimalarial activity of chemical compounds was compiled from the literature and subjected to a thorough curation process. In addition, the performance of a diverse set of ensemble-based classification methodologies was evaluated and one of these ensembles was selected as the most suitable for the identification of antimalarial hits based on its virtual screening performance. Data curation was conducted to minimize noise. Among the explored ensemble-based methods, the one combining Genetic Algorithms for the selection of the base classifiers and Majority Vote for their aggregation showed the best performance.

Results: Our results also show that ensemble modeling is an effective strategy for the QSAR modeling of highly heterogeneous datasets in the discovery of potential antimalarial compounds.

Conclusion: It was determined that the best performing ensembles were those that use Genetic Algorithms as a method of selection of base models and Majority Vote as the aggregation method.

Keywords: Malaria disease, Ensemble modeling, Genetic algorithms, Majority vote, QSAR, Antimalarial activity.

[1]
World Health Organization Guidelines for the treatment of malaria, 2015. (Available at: https://www.who.int/malaria/publications/ atoz/9789241549127/en/).
[2]
World Health Organization, World malaria report 2016. Geneva: 2016;13. 2016. (Available at: https://www.who.int/malaria/ publications/world-malaria-report-2016/report/en/.
[3]
Katsuno, K.; Burrows, J.N.; Duncan, K.; Hooft van Huijsduijnen, R.; Kaneko, T.; Kita, K.; Mowbray, C.E.; Schmatz, D.; Warner, P.; Slingsby, B.T. Hit and lead criteria in drug discovery for infectious diseases of the developing world. Nat. Rev. Drug Discov., 2015, 14(11), 751-758. [http://dx.doi.org/10.1038/nrd4683]. [PMID: 26435527].
[4]
Avandano, C. A brief updated report on the battle against Malaria. Anales de la Real Academia Nacional de Farmacia, 2015, 81, 145-157.
[5]
Kindt, T.; Morse, S.; Gotschlich, E.; Lyons, K. Structure-based strategies for drug design and discovery. Nature, 1991, 352, 581.
[6]
Macalino, S.J.Y.; Gosu, V.; Hong, S.; Choi, S. Role of computer-aided drug design in modern drug discovery. Arch. Pharm. Res., 2015, 38(9), 1686-1701. [http://dx.doi.org/10.1007/s12272-015-0640-5]. [PMID: 26208641].
[7]
Benfenati, E.; Gini, G.; Hoffmann, S.; Luttik, R. Comparing in vivo, in vitro and in silico methods and integrated strategies for chemical assessment: problems and prospects. Altern. Lab. Anim., 2010, 38(2), 153-166. [http://dx.doi.org/10.1177/026119291003800201]. [PMID: 20507186].
[8]
Golbamaki, A.; Benfenati, E. In Silico Methods for Carcinogenicity Assessment. Methods Mol. Biol., 2016, 1425, 107-119. [http://dx.doi.org/10.1007/978-1-4939-3609-0_6].
[9]
Mombelli, E.; Raitano, G.; Benfenati, E. In Silico Prediction of Chemically Induced Mutagenicity: How to Use QSAR Models and Interpret Their Results. Methods Mol. Biol., 2016, 87-105.
[10]
Ojha, P.K.; Roy, K. Exploring QSAR, pharmacophore mapping and docking studies and virtual library generation for cycloguanil derivatives as PfDHFR-TS inhibitors. Med. Chem., 2011, 7(3), 173-199. [http://dx.doi.org/10.2174/157340611795564295]. [PMID: 21486210].
[11]
Prasanth Kumar, S.; Jasrai, Y.T.; Pandya, H.A.; Rawal, R.M. Pharmacophore-similarity-based QSAR (PS-QSAR) for group-specific biological activity predictions. J. Biomol. Struct. Dyn., 2015, 33(1), 56-69. [http://dx.doi.org/10.1080/07391102.2013.849618]. [PMID: 24266725].
[12]
Saghaie, L.; Sakhi, H.; Sabzyan, H.; Shahlaei, M.; Shamshirian, D. Stepwise MLR and PCR QSAR study of the pharmaceutical activities of antimalarial 3-hydroxypyridinone agents using B3LYP/6-311++ G** descriptors. Med. Chem. Res., 2013, 22(4), 1679-1688. [http://dx.doi.org/10.1007/s00044-012-0152-5].
[13]
Sahu, N.K.; Sharma, M.; Mourya, V.; Kohli, D.V. Qsar study of some substituted 4-quinolinyl and 9-acridinyl hydrazones as antimalarial agents. Acta Pol. Pharm., 2012, 69(6), 1153-1165. [PMID: 23285677].
[14]
Verma, S.S.; Prabhakar, Y. Topological and physicochemical characteristics of 1, 2, 3, 4-tetrahydroacridin-9 (10H)-ones and their antimalarial profiles: A composite insight to the structure-activity relationsect. Curr. Computeraided Drug Des., 2013, 9(3), 317-335. [http://dx.doi.org/10.2174/15734099113099990017].
[15]
Qidwai, T. QSAR modeling, docking and ADMET studies for exploration of potential anti-malarial compounds against Plasmodium falciparum. In Silico Pharmacol., 2016, 5(1), 6. [http://dx.doi.org/10.1007/s40203-017-0026-0]. [PMID: 28726171].
[16]
Ojha, P.K.; Roy, K. The current status of antimalarial drug research with special reference to application of QSAR models. Comb. Chem. High Throughput Screen., 2015, 18(2), 91-128. [http://dx.doi.org/10.2174/1386207318666141229125527]. [PMID: 25543681].
[17]
Gupta, M.K. CP-MLR/PLS-directed QSAR studies on the antimalarial activity and cytotoxicity of substituted 4-aminoquinolines. Med. Chem. Res., 2013, 22(7), 3497-3509. [http://dx.doi.org/10.1007/s00044-012-0344-z].
[18]
Iman, M.; Davood, A.; Khamesipour, A. Computational study of quinolone derivatives to improve their therapeutic index as anti-malaria agents: QSAR and QSTR. Iranian journal of pharmaceutical research. Iran. J. Pharm. Res., 2015, 14(3), 775-784. [PMID: 26330866].
[19]
Qidwai, T.; Yadav, D.K.; Khan, F.; Dhawan, S.; Bhakuni, R.S. QSAR, docking and ADMET studies of artemisinin derivatives for antimalarial activity targeting plasmepsin II, a hemoglobin-degrading enzyme from P. falciparum. Curr. Pharm. Des., 2012, 18(37), 6133-6154. [http://dx.doi.org/10.2174/138161212803582397]. [PMID: 22670592].
[20]
Abbasitabar, F.; Zare-Shahabadi, V. Development predictive QSAR models for artemisinin analogues by various feature selection methods: a comparative study. SAR QSAR Environ. Res., 2012, 23(1-2), 1-15. [http://dx.doi.org/10.1080/1062936X.2011.623316]. [PMID: 22040327].
[21]
Sharma, M.C.; Sharma, S.; Sharma, P.; Kumar, A. Pharmacophore and QSAR modeling of some structurally diverse azaaurones derivatives as anti-malarial activity. Med. Chem. Res., 2014, 23(1), 181-198. [http://dx.doi.org/10.1007/s00044-013-0609-1].
[22]
Adhikari, N.; Halder, A.K.; Mondal, C.; Jha, T. Ligand based validated comparative chemometric modeling and pharmacophore mapping of aurone derivatives as antimalarial agents. Curr Comput Aided Drug Des, 2013, 9(3), 417-432. [http://dx.doi.org/10.2174/15734099113099990014]. [PMID: 24010937].
[23]
Batagin-Neto, A.; Lavarda, F.C. The correlation between electronic structure and antimalarial activity of alkoxylated and hydroxylated chalcones. Med. Chem. Res., 2014, 23(2), 580-586. [http://dx.doi.org/10.1007/s00044-013-0667-4].
[24]
Sahu, N.K.; Bari, S.B.; Kohli, D. Molecular modeling studies of some substituted chalcone derivatives as cysteine protease inhibitors. Med. Chem. Res., 2012, 21(11), 3835-3847. [http://dx.doi.org/10.1007/s00044-011-9900-1].
[25]
Ojha, P.K.; Roy, K. First report on exploring structural requirements of 1,2,3,4- tetrahydroacridin-9(10H)-one analogs as antimalarials using multiple QSAR approaches: descriptor-based QSAR, CoMFA-CoMSIA 3DQSAR, HQSAR and G-QSAR approaches. Comb. Chem. High Throughput Screen., 2013, 16(1), 7-21. [http://dx.doi.org/10.2174/1386207311316010003]. [PMID: 23127758].
[26]
Polikar, R. Ensemble based systems in decision making. IEEE Circuits Syst. Mag., 2006, 6(3), 21-45. [http://dx.doi.org/10.1109/MCAS.2006.1688199].
[27]
Zhang, L.; Fourches, D.; Sedykh, A.; Zhu, H.; Golbraikh, A.; Ekins, S.; Clark, J.; Connelly, M.C.; Sigal, M.; Hodges, D.; Guiguemde, A.; Guy, R.K.; Tropsha, A. Discovery of novel antimalarial compounds enabled by QSAR-based virtual screening. J. Chem. Inf. Model., 2013, 53(2), 475-492. [http://dx.doi.org/10.1021/ci300421n]. [PMID: 23252936].
[28]
Pérez-Castillo, Y.; Cruz-Monteagudo, M.; Lazar, C.; Taminau, J.; Froeyen, M.; Cabrera-Pérez, M.Á.; Nowé, A. Toward the computer-aided discovery of FabH inhibitors. Do predictive QSAR models ensure high quality virtual screening performance? Mol. Divers., 2014, 18(3), 637-654. [http://dx.doi.org/10.1007/s11030-014-9513-y]. [PMID: 24671521].
[29]
Bonet, I.; Franco-Montero, P.; Rivero, V.; Teijeira, M.; Borges, F.; Uriarte, E.; Morales Helguera, A. Classifier ensemble based on feature selection and diversity measures for predicting the affinity of A(2B) adenosine receptor antagonists. J. Chem. Inf. Model., 2013, 53(12), 3140-3155. [http://dx.doi.org/10.1021/ci300516w]. [PMID: 24289249].
[30]
Cheng, F.; Yu, Y.; Shen, J.; Yang, L.; Li, W.; Liu, G.; Lee, P.W.; Tang, Y. Classification of cytochrome P450 inhibitors and noninhibitors using combined classifiers. J. Chem. Inf. Model., 2011, 51(5), 996-1011. [http://dx.doi.org/10.1021/ci200028n]. [PMID: 21491913].
[31]
Cortes-Ciriano, I.; Murrell, D.S.; van Westen, G.J.; Bender, A.; Malliavin, T.E. Prediction of the potency of mammalian cyclooxygenase inhibitors with ensemble proteochemometric modeling. J. Cheminform., 2015, 7(1), 1. [http://dx.doi.org/10.1186/s13321-014-0049-z]. [PMID: 25705261].
[32]
Marrero-Ponce, Y.; Siverio-Mota, D.; Gálvez-Llompart, M.; Recio, M.C.; Giner, R.M.; García-Domènech, R.; Torrens, F.; Arán, V.J.; Cordero-Maldonado, M.L.; Esguera, C.V.; de Witte, P.A.; Crawford, A.D. Discovery of novel anti-inflammatory drug-like compounds by aligning in silico and in vivo screening: the nitroindazolinone chemotype. Eur. J. Med. Chem., 2011, 46(12), 5736-5753. [http://dx.doi.org/10.1016/j.ejmech.2011.07.053]. [PMID: 22000935].
[33]
Perez-Castillo, Y.; Helguera, A.M.; Cordeiro, M.N.D.S.; Tejera, E. Paz-Y-Miño, C.; Sánchez-Rodríguez, A.; Borges, F.; Cruz-Monteagudo, M. Fusing docking scoring functions improves the virtual screening performance for discovering Parkinsons disease dual target ligands. Curr. Neuropharmacol., 2017, 15(8), 1107-1116. [http://dx.doi.org/10.2174/1570159X15666170109143757]. [PMID: 28067172].
[34]
Helguera, A.; Perez-Castillo, Y. Ligand-based virtual screening using tailored ensembles: A prioritization tool for dual a2a adenosine receptor antagonists/monoamine oxidase B inhibitors. Curr. Pharm. Des., 2016, 22(21), 3082-3096.
[35]
Plouffe, D.; Brinker, A.; McNamara, C.; Henson, K.; Kato, N.; Kuhen, K.; Nagle, A.; Adrián, F.; Matzen, J.T.; Anderson, P.; Nam, T.G.; Gray, N.S.; Chatterjee, A.; Janes, J.; Yan, S.F.; Trager, R.; Caldwell, J.S.; Schultz, P.G.; Zhou, Y.; Winzeler, E.A. In silico activity profiling reveals the mechanism of action of antimalarials discovered in a high-throughput screen. Proc. Natl. Acad. Sci. USA, 2008, 105(26), 9059-9064. [http://dx.doi.org/10.1073/pnas.0802982105]. [PMID: 18579783].
[36]
ChemAxon Software solutions and services for chemistry and biology, (Available at: https://chemaxon.com/).
[37]
Trust, B.V. On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research Fourches, Denis; Muratov, Eugene; Tropsha. J. Chem. Inf. Model., 2010, 50(7), 1189-1204. [http://dx.doi.org/10.1021/ci100176x]. [PMID: 20572635].
[38]
Cruz-Monteagudo, M.; Medina-Franco, J.L.; Perera-Sardiña, Y.; Borges, F.; Tejera, E.; Paz-Y-Miño, C.; Pérez-Castillo, Y.; Sánchez-Rodríguez, A.; Contreras-Posada, Z.; Cordeiro, M.N. Probing the hypothesis of SAR continuity restoration by the removal of activity cliffs generators in QSAR. Curr. Pharm. Des., 2016, 22(33), 5043-5056. [http://dx.doi.org/10.2174/1381612822666160509124337]. [PMID: 27157417].
[39]
Golbraikh, A.; Shen, M.; Xiao, Z.; Xiao, Y-D.; Lee, K-H.; Tropsha, A. Rational selection of training and test sets for the development of validated QSAR models. J. Comput. Aided Mol. Des., 2003, 17(2-4), 241-253. [http://dx.doi.org/10.1023/A:1025386326946]. [PMID: 13677490].
[40]
MathWorks I. MATLAB : the language of technical computing : computation, visualization, programming : installation guide for UNIX version 5: Natwick : Math Works Inc., 1996.
[41]
Varnek, A.; Fourches, D.; Hoonakker, F.; Solov’ev, V.P. Substructural fragments: an universal language to encode reactions, molecular and supramolecular structures. J. Comput. Aided Mol. Des., 2005, 19(9-10), 693-703. [http://dx.doi.org/10.1007/s10822-005-9008-0]. [PMID: 16292611].
[42]
Varnek, A.; Fourches, D.; Horvath, D.; Klimchuk, O.; Gaudin, C.; Vayer, P. ISIDA-Platform for virtual screening based on fragment and pharmacophoric descriptors. Curr. Computeraided Drug Des., 2008, 4(3), 191. [http://dx.doi.org/10.2174/157340908785747465].
[43]
Peng, H.; Long, F.; Ding, C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27(8), 1226-1238. [http://dx.doi.org/10.1109/TPAMI.2005.159]. [PMID: 16119262].
[44]
Kuncheva, L.I. Combining pattern classifiers: methods and algorithms; John Wiley & Sons, 2004. [http://dx.doi.org/10.1002/0471660264]
[45]
Pérez-Castillo, Y.; Lazar, C.; Taminau, J.; Froeyen, M.; Cabrera-Pérez, M.Á.; Nowé, A.G.A. (M)E-QSAR: a novel, fully automatic genetic-algorithm-(meta)-ensembles approach for binary classification in ligand-based drug design. J. Chem. Inf. Model., 2012, 52(9), 2366-2386. [http://dx.doi.org/10.1021/ci300146h]. [PMID: 22856471].
[46]
Suykens, J.A.; Van Gestel, T.; De Brabanter, J. Least squares support vector machines; World Scientific, 2002. [http://dx.doi.org/10.1142/5089]
[47]
Akaike, H. Information theory and an extension of the maximum likelihood principle. In: 2nd International Symposium on Information Theory Pebrov, B.; Csaki, F. Ed.; Akadémiai Kiadò: Budapest, Hungary; , 1973, pp. 267-281.
[48]
Mysinger, M.M.; Carchia, M.; Irwin, J.J.; Shoichet, B.K. Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J. Med. Chem., 2012, 55(14), 6582-6594. [http://dx.doi.org/10.1021/jm300687e]. [PMID: 22716043].
[49]
Perez-Castillo, Y.; Sánchez-Rodríguez, A.; Tejera, E.; Cruz-Monteagudo, M.; Borges, F.; Cordeiro, M.N.D.S.; Le-Thi-Thu, H.; Pham-The, H. A desirability-based multi objective approach for the virtual screening discovery of broad-spectrum anti-gastric cancer agents. PLoS One, 2018, 13(2)e0192176 [http://dx.doi.org/10.1371/journal.pone.0192176]. [PMID: 29420638].
[50]
Truchon, J.F.; Bayly, C.I. Evaluating virtual screening methods: good and bad metrics for the “early recognition” problem. J. Chem. Inf. Model., 2007, 47(2), 488-508. [http://dx.doi.org/10.1021/ci600426e]. [PMID: 17288412].
[51]
Cruz-Monteagudo, M.; Medina-Franco, J.L.; Pérez-Castillo, Y.; Nicolotti, O.; Cordeiro, M.N.D.; Borges, F. Activity cliffs in drug discovery: Dr Jekyll or Mr Hyde? Drug Discov. Today, 2014, 19(8), 1069-1080. [http://dx.doi.org/10.1016/j.drudis.2014.02.003]. [PMID: 24560935].


Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 19
ISSUE: 11
Year: 2019
Page: [957 - 969]
Pages: 13
DOI: 10.2174/1568026619666190510100313
Price: $58

Article Metrics

PDF: 8
HTML: 5
EPUB: 2
PRC: 2