198
Views
13
CrossRef citations to date
0
Altmetric
Research Article

QSAR modelling of larvicidal phytocompounds against Aedes aegypti using index of ideality of correlation

&
Pages 717-739 | Received 09 Jun 2020, Accepted 04 Aug 2020, Published online: 15 Sep 2020

References

  • G.N. Pandiyan, N. Mathew, and S. Munusamy, Larvicidal activity of selected essential oil in synergized combinations against Aedes aegypti, Ecotoxicol. Environ. Saf. 174 (2019), pp. 549–556. doi:10.1016/j.ecoenv.2019.03.019.
  • R. Bibi, R.M. Tariq, and M. Rasheed, Toxic assessment, growth disrupting and neurotoxic effects of red seaweeds’ botanicals against the dengue vector mosquito Aedes aegypti L, Ecotoxicol. Environ. Saf. 195 (2020), pp. 110451. doi:10.1016/j.ecoenv.2020.110451.
  • S. Chalom, K. Jumpatong, S. Wangkarn, S. Chantara, C. Phalaraksh, S. Dheeranupattana, W. Suwankerd, S.G. Pyne, and P. Mungkornasawakul, Utilization of electrocoagulation for the isolation of alkaloids from the aerial parts of Stemona aphylla and their mosquitocidal activities against Aedes aegypti, Ecotoxicol. Environ. Saf. 182 (2019), pp. 109448. doi:10.1016/j.ecoenv.2019.109448.
  • P. Yogarajalakshmi, T.V. Poonguzhali, R. Ganesan, S. Karthi, S. Senthil-Nathan, P. Krutmuang, N. Radhakrishnan, F. Mohammad, T.J. Kim, and P. Vasantha-Srinivasan, Toxicological screening of marine red algae Champia parvula (C. Agardh) against the dengue mosquito vector Aedes aegypti (Linn.) and its non-toxicity against three beneficial aquatic predators, Aquat. Toxicol. 222 (2020), pp. 105474. doi:10.1016/j.aquatox.2020.105474.
  • K.V. Lakshmi, A.V. Sudhikumar, and E.M. Aneesh, Larvicidal activity of phytoextracts against dengue fever vector, Aedes aegypti-A review, Plant Sci. Today 5 (2018), pp. 167–174. doi:10.14719/pst.2018.5.4.407.
  • C.B.R. Santos, J.B. Vieira, C.C. Lobato, L.I. Hage-Melim, R.N. Souto, C.S. Lima, E.V. Costa, D.S. Brasil, W.J.C. Macêdo, and J.C.T. Carvalho, A SAR and QSAR study of new artemisinin compounds with antimalarial activity, Molecules 19 (2014), pp. 367–399. doi:10.3390/molecules19010367.
  • S. Ahmadi, M.R. Khazaei, and A. Abdolmaleki, Quantitative structure–property relationship study on the intercalation of anticancer drugs with ct-DNA, Med. Chem. Res. 23 (2014), pp. 1148–1161. doi:10.1007/s00044-013-0716-z.
  • E. Habibpour and S. Ahmadi, QSAR modeling of the arylthioindole class of colchicine polymerization inhibitors as anticancer agents, Curr. Comput. Aid. Drug 13 (2017), pp. 143–159. doi:10.2174/1573409913666170124100810.
  • J.B. Ghasemi, P. Zohrabi, and H. Khajehsharifi, Quantitative structure–activity relationship study of nonpeptide antagonists of CXCR2 using stepwise multiple linear regression analysis, Monatsh. Chem. 141 (2010), pp. 111–118. doi:10.1007/s00706-009-0225-4.
  • J. da Silva Costa, K. da Silva Lopes Costa, J.V. Cruz, R. da Silva Ramos, L.B. Silva, D. Do Socorro Barros Brasil, C.H. de Paula da Silva, C.B.R. Dos Santos, and W.J. da Cruz Macedo, Virtual screening and statistical analysis in the design of new caffeine analogues molecules with potential epithelial anticancer activity, Curr. Pharm. Des. 24 (2018), pp. 576–594. doi:10.2174/1381612823666170711112510.
  • S. Ahmadi and E. Habibpour, Application of GA-MLR for QSAR modeling of the arylthioindole class of tubulin polymerization inhibitors as anticancer agents, Anti-Cancer Agent Med. Chem. 17 (2017), pp. 552–565. doi:10.2174/1871520616666160811162105.
  • S. Ahmadi and S. Ganji, Genetic algorithm and self-organizing maps for QSPR study of some N-aryl derivatives as butyrylcholinesterase inhibitors, Curr. Drug Discov. Technol. 13 (2016), pp. 232–253. doi:10.2174/1570163813666160725114241.
  • S. Lotfi, S. Ahmadi, and P. Zohrabi, QSAR modeling of toxicities of ionic liquids toward Staphylococcus aureus using SMILES and graph invariants, Struct. Chem. (2020). doi:10.1007/s11224-020-01568-y.
  • S. Ahmadi, H. Ghanbari, S. Lotfi, and N. Azimi, Predictive QSAR modeling for the antioxidant activity of natural compounds derivatives based on Monte Carlo method, Mol. Divers. (2020). doi:10.1007/s11030-019-10026-9.
  • S. Ahmadi, Mathematical modeling of cytotoxicity of metal oxide nanoparticles using the index of ideality correlation criteria, Chemosphere 242 (2020), pp. 125192. doi:10.1016/j.chemosphere.2019.125192.
  • S. Ahmadi and A. Akbari, Prediction of the adsorption coefficients of some aromatic compounds on multi-wall carbon nanotubes by the Monte Carlo method, SAR QSAR Environ. Res. 29 (2018), pp. 895–909. doi:10.1080/1062936X.2018.1526821.
  • J.L. Velázquez-Libera, J. Caballero, A.P. Toropova, and A.A. Toropov, Estimation of 2D autocorrelation descriptors and 2D Monte Carlo descriptors as a tool to build up predictive models for acetylcholinesterase (AChE) inhibitory activity, Chemom. Intell. Lab. 184 (2019), pp. 14–21. doi:10.1016/j.chemolab.2018.11.008.
  • P. Kumar, A. Kumar, and J. Sindhu, Design and development of novel focal adhesion kinase (FAK) inhibitors using Monte Carlo method with index of ideality of correlation to validate, QSAR. SAR QSAR Environ. Res. 30 (2019), pp. 63–80. doi:10.1080/1062936X.2018.1564067.
  • Manisha, S. Chauhan, P. Kumar, and A. Kumar, Development of prediction model for fructose-1, 6-bisphosphatase inhibitors using the Monte Carlo method, SAR QSAR Environ. Res. 30 (2019), pp. 145–159. doi:10.1080/1062936X.2019.1568299.
  • L. Saavedra, G. Romanelli, and P. Duchowicz, QSAR analysis of plant-derived compounds with larvicidal activity against Zika Aedes aegypti (Diptera: Culicidae) vector using freely available descriptors, Pest Manag. Sci. 74 (2018), pp. 1608–1615. doi:10.1002/ps.4850.
  • I.M. Santos, J.P.G. Agra, T.G.C. Carvalho, G.L. Azevedo Maia, and E.B. Alencar Filho, Classical and 3D QSAR studies of larvicidal monoterpenes against Aedes aegypti: New molecular insights for the rational design of more active compounds, Struct. Chem. 29 (2018), pp. 1287–1297. doi:10.1007/s11224-018-1110-8.
  • P. De, R. Aher, and K. Roy, Chemometric modeling of larvicidal activity of plant derived compounds against zika virus vector Aedes aegypti: Application of ETA indices, RSC Adv. 8 (2018), pp. 4662–4670. doi:10.1039/C7RA13159C.
  • P. Kumar, A. Kumar, and J. Sindhu, In silico design of diacylglycerol acyltransferase-1 (DGAT1) inhibitors based on SMILES descriptors using Monte-Carlo method, SAR QSAR Environ. Res. 30 (2019), pp. 525–541. doi:10.1080/1062936X.2019.1629998.
  • P. Kumar and A. Kumar, 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, Chemom. Intell. Lab. 200 (2020), pp. 103982. doi:10.1016/j.chemolab.2020.103982.
  • A.A. Toropov and A.P. Toropova, QSPR/QSAR: State-of-art, Weirdness, the Future, Molecules 25 (2020), pp. 1292. doi:10.3390/molecules25061292.
  • L. Scotti, M. Tullius Scotti, V. Barros Silva, S. Regina Lima Santos, S.C.H. Cavalcanti, and F.J.B. Mendonca Junior, Chemometric studies on potential larvicidal compounds against Aedes aegypti, Med. Chem. 10 (2014), pp. 201–210. doi:10.2174/15734064113099990005.
  • S.R. Santos, V.B. Silva, M.A. Melo, J.D. Barbosa, R.L. Santos, D.P. de Sousa, and S.C. Cavalcanti, Toxic effects on and structure-toxicity relationships of phenylpropanoids, terpenes, and related compounds in Aedes aegypti larvae, Vector-Borne Zoon. Dis. 10 (2010), pp. 1049–1054. doi:10.1089/vbz.2009.0158.
  • S.R. Santos, M.A. Melo, A.V. Cardoso, R.L. Santos, D.P. Sousa, and S.C. Cavalcanti, Structure–activity relationships of larvicidal monoterpenes and derivatives against Aedes aegypti Linn, Chemosphere 84 (2011), pp. 150–153. doi:10.1016/j.chemosphere.2011.02.018.
  • J.D. Barbosa, V.B. Silva, P.B. Alves, G. Gumina, R.L. Santos, D.P. Sousa, and S.C. Cavalcanti, Structure–activity relationships of eugenol derivatives against Aedes aegypti (Diptera: Culicidae) larvae, Pest Manag. Sci. 68 (2012), pp. 1478–1483. doi:10.1002/ps.3331.
  • San Diego Biovia C, USA. Available at http://accelrys.com/products/informatics/cheminformatics/draw/.
  • S. Ahmadi, F. Mardinia, N. Azimi, M. Qomi, and E. Balali, Prediction of chalcone derivative cytotoxicity activity against MCF-7 human breast cancer cell by Monte Carlo method, J. Mol. Struct. 1181 (2019), pp. 305–311. doi:10.1016/j.molstruc.2018.12.089.
  • A.P. Toropova and A.A. Toropov, Quasi-SMILES: Quantitative structure–activity relationships to predict anticancer activity, Mol. Divers. 23 (2019), pp. 403–412. doi:10.1007/s11030-018-9881-9.
  • M. Nimbhal, K. Bagri, P. Kumar, and A. Kumar, The index of ideality of correlation: A statistical yardstick for better QSAR modeling of glucokinase activators, Struct. Chem. 31 (2020), pp. 831–839. doi:10.1007/s11224-019-01468-w.
  • A.P. Toropova, A.A. Toropov, E. Carnesecchi, E. Benfenati, and J.L. Dorne, The index of ideality of correlation: Models for flammability of binary liquid mixtures, Chem. Pap. 74 (2020), pp. 601–609. doi:10.1007/s11696-019-00903-w.
  • S. Jain, S.A. Amin, N. Adhikari, T. Jha, and S. Gayen, 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, J. Biomol. Struct. Dyn. 38 (2020), pp. 66–77. doi:10.1080/07391102.2019.1566093.
  • A.P. Toropova, A.A. Toropov, E. Carnesecchi, E. Benfenati, and J.L. Dorne, The using of the index of ideality of correlation (IIC) to improve predictive potential of models of water solubility for pesticides, Environ. Sci. Pollut. Res. 27 (2020), pp. 13339–13347. doi:10.1007/s11356-020-07820-6.
  • A.A. Toropov, A.P. Toropova, M. Marzo, and E. Benfenati, Use of the index of ideality of correlation to improve aquatic solubility model, J. Mol. Graph. Model. 96 (2020), pp. 107525. doi:10.1016/j.jmgm.2019.107525.
  • K. Khan, H. Sanderson, and K. Roy, Ecotoxicological QSARs of Personal Care Products and Biocides, in Ecotoxicological QSARs, K. Roy, Ed., Springer, Totowa, New Jersey, USA, 2020, pp. 357–386.
  • S. Ahmadi and E. Babaee, Application of self organizing maps and GA-MLR for the estimation of stability constant of 18-crown-6 ether derivatives with sodium cation, J. Incl. Phenom. Macro. 79 (2014), pp. 141–149. doi:10.1007/s10847-013-0337-7.
  • A.P. Toropova, A.A. Toropov, A.M. Veselinović, J.B. Veselinović, E. Benfenati, D. Leszczynska, and J. Leszczynski, Nano-QSAR: Model of mutagenicity of fullerene as a mathematical function of different conditions, Ecotox. Environ. Saf. 124 (2016), pp. 32–36. doi:10.1016/j.ecoenv.2015.09.038.
  • F. Shiri, A. Shahraki, and M. Nejati-Yazdinejad, 3D-QSAR and molecular docking study on maleimide-based glycogen synthase kinase 3 (GSK-3) inhibitors as stimulators of steroidogenesis, Polycycl. Aromat. Comp. 40 (2020), pp. 743–757. doi:10.1080/10406638.2018.1481112.
  • F. Shiri, M. Salahinejad, R. Dijoor, and M. Nejati-Yazdinejad, An explorative study on potent Gram-negative specific LpxC inhibitors: CoMFA, CoMSIA, HQSAR and molecular docking, J.Recept. Sig. Transd. 38 (2018), pp. 151–165. doi:10.1080/10799893.2018.1457052.
  • R.S. Ramos, W.J.C. Macêdo, J.S. Costa, C.H.T.P. Silva, J.M.C. Roza, J.N. Cruz, M.S. Oliveira, E.H.A. Andrade, R.B.L. Silva, and R.N.P. Souto, Potential inhibitors of the enzyme acetylcholinesterase and juvenile hormone with insecticidal activity: Study of the binding mode via docking and molecular dynamics simulations, J. Biomol. Struct. Dyn. (2019), pp. 1–23. doi:10.1080/07391102.2019.1688192.
  • R.S. Ramos, J.S. Costa, R.C. Silva, G.V. da Costa, A.B.L. Rodrigues, É.M. Rabelo, R.N.P. Souto, C.A. Taft, C.H.T.P. Silva, and J.M.C. Rosa, Identification of potential inhibitors from pyriproxyfen with insecticidal activity by virtual screening, Pharmaceuticals 12 (2019), pp. 20. doi:10.3390/ph12010020.
  • A. Kroupova, A. Ivaşcu, M.M. Reimão-Pinto, S.L. Ameres, and M. Jinek, Structural basis for acceptor RNA substrate selectivity of the 3′ terminal uridylyl transferase Tailor, Nucleic Acids Res. 47 (2019), pp. 1030–1042. doi:10.1093/nar/gky1164.
  • S. Ahmadi, R. Khani, and M. Moghaddas, Prediction of anti-cancer activity of 1,8-naphthyridin derivatives by using of genetic algorithm-stepwise multiple linear regression, Med. Sci. J. 28 (2018), pp. 181–194.
  • M. Harel, G. Kryger, T.L. Rosenberry, W.D. Mallender, T. Lewis, R.J. Fletcher, J.M. Guss, I. Silman, and J.L. Sussman, Three‐dimensional structures of Drosophila melanogaster acetylcholinesterase and of its complexes with two potent inhibitors, Protein Sci. 9 (2000), pp. 1063–1072. doi:10.1110/ps.9.6.1063.
  • A.C. Wallace, R.A. Laskowski, and J.M. Thornton, LIGPLOT: A program to generate schematic diagrams of protein-ligand interactions, Protein Eng. Des. Sel. 8 (1995), pp. 127–134. doi:10.1093/protein/8.2.127.
  • L.M. Saavedra, G.P. Romanelli, C.E. Rozo, and P.R. Duchowicz, The quantitative structure–insecticidal activity relationships from plant derived compounds against chikungunya and Zika Aedes aegypti (Diptera: Culicidae) vector, Sci. Total Environ. 610 (2018), pp. 937–943. doi:10.1016/j.scitotenv.2017.08.119.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.