Predictive Modeling of the Human Hepatoma (Huh-7D12) Cancer Line of a Series of bis- (5-arylidene-rhodanine-3-yl) Diamine

Main Article Content

Koffi Alexis Respect Kouassi
Anoubilé Benié
Mamadou Guy-Richard Koné
Wacothon Karime Coulibaly
Kouadio Valery Bohoussou
Adenidji Ganiyou

Abstract

This work deals with the prediction of the antiproliferative activity of eighteen (18) substances derived from bis-5-arylidene rhodanine against human hepatoma tumor line (Huh-7D12). By applying the functional density theory (DFT) method to the B3LYP / 6-31G (d, p) level, theoretical descriptors were determined and correlated with antiproliferative (Huh-7) activity by linear regression multiple (RML). This correlation has shown that the electron energy, the energy of the lowest vacant molecular orbital (ELUMO) and the molecular volume (VM) are the quantum and geometric descriptors that best influences the antiproliferative activity of the molecules studied. The coefficient of determination R2 indicates that 97.9% of the molecular descriptors defining this model are taken into account with a standard deviation of 0.015. The significance of the model reflected by the Fischer test is estimated at 123.648. The robustness of the model given by the cross-validation correlation coefficient (Q2CV) is 97.9%. This model has been validated by Tropsha criteria. The very good correlation between these three descriptors and the Huh-7 activity was confirmed by the nonlinear multiple regression (RNML) method with better statistical data. (R2 = 0,998 ; Q2CV = 0,998 ; RMSE = 0,006).

Keywords:
RML, RMNL, Huh-7D12, bis-5-arylidène rhodanine, molecular descriptors

Article Details

How to Cite
Kouassi, K. A. R., Benié, A., Koné, M. G.-R., Coulibaly, W. K., Bohoussou, K. V., & Ganiyou, A. (2019). Predictive Modeling of the Human Hepatoma (Huh-7D12) Cancer Line of a Series of bis- (5-arylidene-rhodanine-3-yl) Diamine. Asian Journal of Chemical Sciences, 6(2), 1-11. https://doi.org/10.9734/ajocs/2019/v6i218993
Section
Original Research Article

References

Fartoux L, Desbois-Mouthon C, Rosmorduc O. Carcinome Hépatocellulaire. EMC-Hépatologie. 2009;7–38.

Parkin DM, Bray F, Ferlay J, Pisani P. Global Cancer Statistics, 2002. CA: A Cancer Journal for Clinicians. 2005;55(2): 74–108.

Xu G, Mcleod HL. Strategies for enzyme/prodrug cancer therapy. Clinical Cancer Research. 2001;7(11):3314– 3324.

Coulibaly W, Paquin L, Bénié A, Bekro YA, Durieux E, Meijer L, Le Guével R, Corlu A, Bazureau JP. Synthesis of new N, N'-Bis (5-Arylidene-4-Oxo-4, 5-Dihydrothiazolin-2-Yl) piperazine derivatives under microwave irradiation and preliminary biological evaluation. Scientia Pharmaceutica. 2012;80(4):825–836.

Soro D, Ekou L, Koné MGR, Ekou T, Affi ST, Ouattara L, Ziao N. Prediction of the inhibitory concentration of hydroxamic acids by DFT-QSAR models on histone deacetylase 1. International Research Journal of Pure and Applied Chemistry. 2018;1–13.

Tropsha A. Best practices for QSAR model development, validation and exploitation. Molecular Informatics. 2010;29(6‐7):476–488.

Chhabria MT, Mahajan BM, Brahmkshatriya PS. QSAR study of a series of Acyl Coenzyme A (Coa). Medicinal Chemistry Research. 2011; 20(9):1573–1580.

Buha VM, Rana DN, Chhabria MT, Chikhalia KH, Mahajan BM, Brahmkshatriya PS, Shah NK. Synthesis, biological evaluation and QSAR study of a series of substituted quinazolines as antimicrobial agents. Medicinal Chemistry Research. 2013;22(9):4096–4109.

Hansch C, Fujita T. P -Σ-Π analysis. A method for the correlation of biological activity and chemical structure. J. Am. Chem. Soc. 1964;86(8):1616–1626.

Free SM, Wilson JW. A mathematical contribution to structure-activity studies. J. Med. Chem. 1964;7(4):395–399.

Kangah NJB, Koné MGR, Kablan ALC, Yéo SA, Ziao N. Antibacterial activity of schiff bases derived from ortho-diaminocyclohexane, meta-phenylenediamine and 1, 6-diaminohexane. International Journal of Pharmaceutical Science Invention. 2017;6(13):38–43.

Huynh TNP. Synthèse Et Etudes Des Relations Structure/Activité Quantitatives (QSAR/2D) D'analyse Benzo [C] Phénanthridiniques (Université d'Angers); 2007.

Gaussian 09, Revision D.01, Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Mennucci B, Petersson GA, Nakatsuji H, Caricato M, Li X, Hratchian HP, Izmaylov AF, Bloino J, Zheng G, Sonnenberg JL, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Vreven T, Montgomery JA Jr., Peralta JE, Ogliaro F, Bearpark M, Heyd JJ, Brothers E, Kudin KN, Staroverov VN, Keith T, Kobayashi R, Normand J, Raghavachari K, Rendell A, Burant JC, Iyengar SS, Tomasi J, Cossi M, Rega N, Millam JM, Klene M, Knox JE, Cross JB, Bakken V, Adamo C, Jaramillo J, Gomperts R, Stratmann RE, Yazyev O, Austin AJ, Cammi R, Pomelli C, Ochterski JW, Martin RL, Morokuma K, Zakrzewski VG, Voth GA, Salvador P, Dannenberg JJ, Dapprich S, Daniels AD, Farkas O, Foresman JB, Ortiz JV, Cioslowski J, Fox DJ. Gaussian, Inc., Wallingford CT; 2013. Google Search.

Ayers P, Parr RG. J. Am. Chem. Soc. 2000;122–2010.

F. De Proft, Geerlings P. Conceptual and computational DFT in the study of aromaticity. Chemical Reviews. 2001;101(5):1451–1464.

De Proft F, Martin JML, Geerlings P. On the performance of density functional methods for describing atomic populations, dipole moments and infrared intensities. Chemical Physics Letters. 1996;250(3-4): 393–401.

Pliego Jr JR. Thermodynamic cycles and the calculation of Pka. Chemical Physics Letters. 2003;367(1-2):145–149.

Franke R. Theoretical drug design methods. Elsevier Science Ltd; 1984.

N’guessan KN, Koné MGR, Bamba K, Patrice OW, Ziao N. Quantitative structure anti-cancer activity relationship (QSAR) of a series of ruthenium complex azopyridine by the density functional theory (DFT) method. Computational Molecular Bioscience. 2017;7(02):19.

XLSTAT Version 2016.5.03- Google Search, Copyright Addinsoft 1995-2014 XLSTAT and Addinsoft are Registered Trademarks of Addinsoft; 2016.

Available:Https://Www.Xlstat.Com

Koopmans T. Über Die Zuordnung Von Wellenfunktionen Und Eigenwerten Zu Den Einzelnen Elektronen Eines Atoms. Physica. 1934;1(1-6):104–113.

Logiciel Libre Molinspiration Cheminformatics. Available:Http://Www.Molinspiration.Com (Accession En 02 May 2019)

Lee B, Richards FM. The interpretation of protein structures. Journal of Molecular Biology. 1971;55(3):379-IN4.

Shrake A, Rupley JA. Environment and exposure to solvent of protein atoms. Lysozyme and insulin. Journal of Molecular Biology. 1973;79(2):351–371.

Nalimov VV. The application of mathematical statistics to chemical analysis. Elsevier; 2014.

Katritzky AR, Lobanov VS, Karelson M. CODESSA, University of Florida, Gainesville, FL; 1994.

Roy K, Kar S, Das RN. Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment. Academic Press; 2015.

Larif M, Adad A, Hmammouchi R, Taghki AI, Soulaymani A, Elmidaoui A, Bouachrine M, Lakhlifi T. Biological activities of triazine derivatives. Combining DFT and QSAR results. Arabian Journal of Chemistry. 2017;10:S946-S955.

Karabulut S, Sizochenko N, Orhan A, Leszczynski J. A DFT-based QSAR study on inhibition of human dihydrofolate reductase. Journal of Molecular Graphics and Modelling. 2016;70:23–29.

Seber GAF, Lee AJ. Linear regression analysis. John Wiley & Sons; 2012.

Asgaonkar K, Mote G, Chitre T. QSAR and molecular docking studies of oxadiazole-ligated pyrrole derivatives as Enoyl-ACP (Coa) reductase inhibitors. Scientia Pharmaceutica. 2013;82(1):71–86.

Rücker C, Rücker G, Meringer M. Y-randomization and its variants in QSPR/QSAR. Journal of Chemical Information and Modeling. 2007;47(6): 2345–2357.

Eriksson L, Jaworska J, Worth AP, Cronin MTD, Mcdowell RM, Gramatica P. Methods for reliability and uncertainty assessment and for applicability evaluations of classification-and regression-based Qsars. Environmental Health Perspectives. 2003;111(10):1361–1375.

Tropsha A, Gramatica P, Gombar VK. The importance of being earnest. QSAR & Combinatorial Science. 2003;22(1):69–77.

Ouattara O, Thomas Sopi A, Koné MGR, Bamba K, Ziao N. Can empirical descriptors reliably predict molecular lipophilicity? A QSPR study investigation. Int. Journal of Engineering Research and Application. 2017;7(15):50–56.

Golbraikh A, Tropsha A. Beware of Q2! Journal of Molecular Graphics and Modelling. 2002;20(4):269–276.