INSILICO MODELLING ON SOME C14-UREA TETRANDRINE COMPOUNDS AS POTENT ANTI-CANCER AGAINST HUMAN ERYTHROLEUKEMIA (HEL) CELL LINE
DOI:
https://doi.org/10.18540/jcecvl5iss1pp0063-0078Palavras-chave:
QSAR, Mean Effect, Validation, Descriptors, Model, Y-randomizationResumo
Insulin modeling was performed on 28 C14-urea tetrandrine compounds as inhibitors of leukemic (HEL) cell lines using Quantitative Structure-Activity Relationship (QSAR) method. The structure of the inhibitors was correctly drawn, then geometrically optimized at Density Functional Theory (DFT) level (DFT / B3LYP / 6-31G *) with Spartan 14 V1.1.4. Also, molecular descriptors of the inhibitors were calculated with PaDEL calculator, and the results were partitioned into training and test set after data pretreatment. The training set was used to generate a model by employing genetic function approximation in choosing best descriptors to form the model. The validation parameters of the model include; R ^ 2 (train) at 0.8067, LOF 0.037 r ^ 2 (QCV) to 0.6378 R ^ 2 (test) 0.7629 of the CRP ^ 2 and the 0. 6990 Which have passed the acceptance criteria for a QSAR model worldwide. In addition, the model depicted four (4) descriptors, AATS4v, AATS5i, AATSC5i, and GATS5m with positive meanings signifying that increase in these descriptors will positively influence and increase the activity of the inhibitors. This study depicts a route in designing and synthesizing new C14-urea tetrandrine compounds with better inhibitory potentials.
Downloads
Referências
AMBURE, P.; RAHUL, B.A.; AGNIESZKA, G.; TOMASZ, P.; KUNAL, R. “NanoBRIDGES” Software: Open Access Tools to Perform QSAR and Nano-QSAR Modeling. Chemical Intelligence Laboratory Systems. v.147, p.1–13, 2015.
ADENIJI, S.E.; SANI, U.; UZAIRU, A. QSAR Modeling and Molecular Docking Analysis of Some Active Compounds against Mycobacterium Tuberculosis Receptor (Mtb CYP121). Journal of Pathogens. Hindawi. v2018, p.24-64, 2018
ALISI, I.O.; UZAIRU, A.; ABECHI, S.E.; IDRIS, S.O. Quantitative Structure activity relationship analysis of coumarins as free radical scavengers by genetic function algorithm. Iranian Chemical Society. v.6, p.208–222
BECK, A.D. Becke’s three parameter hybrid method using the LYP correlation functional. Journal of Chemical Physics. v.98, p.5648–5652, 1993
BRANDON, V.; ORR, K.A. Comprehensive R archive network (CRAN): http:// CRAN.Rproject.org; 2015:112-113
DAVEY, F.R.; ABRAHAM, J.R.N.; BRUNETTO, V.L.; MACCALLUM, J.M.; NELSON, D.A.; BALL, E.D. Morphologic characteristics of erythroleukemia (acute myeloid leukemia; FAB-M6): a CALGB study. American Journal of Hematology, v.49, p. 29–38, 1995.
ERIKSSON, L.; JAWORSKA, J.; WORTH, A.P.; CRONIN, M.T.D.; MCDOWELL, R.M.; GRAMATICA, P. Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-baes QSARs. Environmental Health Perspectives, v.111, p.1361-1375, 2003.
FAN, Y.; LU, H.; AN, L.; WANG, C.; ZHOU, Z.; FENG, F.; ZHAO, Q. Effect of active fraction of Eriocaulon sieboldianum on human leukemia K562 cells via proliferation inhibition, cell cycle arrest and apoptosis induction. Environmental Toxicology and Pharmacology, v.43, p.13-20, 2016.
FRIEDMAN JH, Multivariate Adaptive Regression Splines. The Annals of Statistics, p.1–67, 1991.
GRAMATICA, P.; GIANI, E.; PAPA, E. Statistical external validation and consensus modeling: A QSPR case study for KOC prediction. Journal of Molecular Graphics Modelling, v.25, p.755-66. DOI: 10.1016/j.jmgm.2006.06.005, 2007.
KENNARD, R.W.; STONE, L.A. Computer Aided Design of Experiments. Technometrics, v.11 (1), p.137–48, 1969, DOI: 10.1080/00401706.1969.
KHALED, K.F.; ABDEL-SHAFI, N.S. Quantitative structure and activity relationship modeling study of corrosion inhibitors: Genetic function approximation and molecular dynamics simulation methods. International Journal of Electrochemical Science, v.6, p.4077-4094, 2011
KOWAL-VERN, A.; MAZZELLA, F.M; COTELINGAM, J.D; SHRIT, M.A.; RECTOR, J.T.; SCHUMACHER, H.R. Diagnosis and characterization of acute erythroleukemia subsets by determining the percentages of myeloblasts and proerythroblasts in 69 cases. American Journal of Hematology, v.65, p. 5–13, 2000.
LIU, T.; LIU, X.; LI, W.H. Tetrandrine, a Chinese Plant-Derived Alkaloid, Is a Potential Candidate for Cancer Chemotherapy, On Co-Target, v.7, p.480100–480115, 2016.
LAN, J.; HUANG, L.; LOU, H.; CHEN, C.; LIU, T.; HU, S.; YAO, Y.; SONG, J.; LUO, J.; LIU, Y.; XIA, B.; XIA, L; ZENG, X.; BEN-DAVID, Y.; PAN, W. Design and Synthesis of Novel Tetrandrine Derivatives as Potential Anti-Tumor Agents against Human Hepatocellular Carcinoma. European Journal Medicinal Chemistry, v.12, p. 3-4, 2017 DOI: 10.1016/j.ejmech.11.007s
LEE, C.; YANG, W.; PARR, R.G. Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Physical Review B, p.37-785, 1988
MINOVSKI, N.; ŽUPERL, Š.; DRGAN, V.; NOVIČ, M. Assessment of applicability domain for multivariate counter-propagation artificial neural network predictive models by minimum Euclidean distance space analysis: a case study. Analytica Chemical Acta, v.759, p.28–42, 2013.
MYERS, R.H. Classical and modern regression application. 2nd edition. Duxbury press. CA. 1990
NANDI, S.; MONESI, A.; DRGAN, V.; MERZEL, F.; NOVIČ, M. Quantitative structure-activation barrier relationship modeling for Diels-Alder ligations utilizing quantum chemical structural descriptors. Chemistry Central Journal, v.7, p.1-13, 2013. DOI.org/10.1186/1752153X-7-171
PERKINS, R.; FANG, H.; TONG, W.; WELSH, W.J. Quantitative Structure-Activity Relationship Methods: Perspectives on Drug Discovery and Toxicology, v. 22(1), p.1666–1679, 2003.
ROY, K.; MITRA, I.; KAR, S.; OJHA, P.K.; DAS, R.N.; KABIR H. Comparative studies on some metrics for external validation of QSPR models. Journal of Chemical Informatics and Modelling. v.52, p.:396–408. 2012. DOI:10.1021/ci200520g
TODESCHINI, R.; CONSONNI, V. Molecular descriptors for chemo-informatics. Weinheim: Wiley- VCH, (Methods and principles in medicinal chemistry). 2009. ISBN: 9783527318520
TROPSHA, A. Best Practices for QSAR Model Development, Validation, and Exploitation. Molecular Informatics, v.29 (6–7), p.476–88, DOI: 10.1002/minf.201000061, 2010