Generic placeholder image

Current Computer-Aided Drug Design

Editor-in-Chief

ISSN (Print): 1573-4099
ISSN (Online): 1875-6697

Research Article

Image-based QSAR Model for the Prediction of P-gp Inhibitory Activity of Epigallocatechin and Gallocatechin Derivatives

Author(s): Paria Ghaemian and Ali Shayanfar*

Volume 15, Issue 3, 2019

Page: [212 - 224] Pages: 13

DOI: 10.2174/1573409914666181003152042

Price: $65

Open Access Journals Promotions 2
Abstract

Background: Permeability glycoprotein (P-gp) is one of the cell membrane proteins that can push some drugs out of the cell causing drug tolerance and its inhibition can prevent drug resistance.

Objective: In this study, we used image-based Quantitative Structure-Activity Relationship (QSAR) models to predict the P-gp inhibitory activity of epigallocatechin and gallocatechin derivatives.

Methods: The 2D-chemical structures and their P-gp inhibitory activity were taken from literature. The pixels of images and their Principal Components (PCs) were calculated using MATLAB software. Principle Component Regression (PCR), Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches were used to develop QSAR models. Statistical parameters included the leave one out cross-validated correlation coefficient (q2) for internal validation of the models and R2 of test set, Root Mean Square Error (RMSE) and Concordance Correlation Coefficient (CCC) were applied for external validation.

Results: Six PCs from image analysis method were selected by stepwise regression for developing linear and non-linear models. Non-linear models i.e. ANN (with the R2 of 0.80 for test set) were chosen as the best for the established QSAR models.

Conclusion: According to the result of the external validation, ANN model based on image analysis method can predict the P-gp inhibitory activity of epigallocatechin and gallocatechin derivatives better than the PCR and SVM models.

Keywords: Image analysis, QSAR, P-glycoprotein (P-gp), PCR, SVM, epigallocatechin.

Graphical Abstract
[1]
Ford, R.C.; Kamis, A.B.; Kerr, I.D.; Callaghan, R. The ABC transporters: Structural insights into drug transport. Transporters as Drug Carriers, Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim. 2010, 1-48.
[2]
Abbasi, M.M.; Valizadeh, H.; Hamishekar, H.; Mohammadnejad, L.; Zakeri-Milani, P. The effects of cetirizine on P-glycoprotein expression and function in vitro and in situ. Adv. Pharm. Bull., 2016, 6(1), 111-118.
[3]
Liu, H.; Ma, Z.; Wu, B. Structure-activity relationships and in silico models of P-glycoprotein (ABCB1) inhibitors. Xenobiotica, 2013, 43(11), 1018-1026.
[4]
Cherkasov, A.; Muratov, E.N.; Fourches, D.; Varnek, A.; Baskin, I.I.; Cronin, M.; Dearden, J.; Gramatica, P.; Martin, Y.C.; Todeschini, R. QSAR modeling: Where have you been? Where are you going to? J. Med. Chem., 2014, 57(12), 4977-5010.
[5]
Dearden, J.C. Whither QSAR? Pharm. Sci., 2017, 23(2), 82-83.
[6]
Jafari, B.; Hamzeh-Mivehroud, M.; Alizadeh, A.A.; Sharifi, M.; Dastmalchi, S. An alignment-independent 3D-QSAR study of FGFR2 tyrosine kinase inhibitors. Adv. Pharm. Bull., 2017, 7(3), 409-418.
[7]
Sarkhosh, M.; Khorshidi, N.; Niazi, A.; Leardi, R. Application of genetic algorithms for pixel selection in multivariate image analysis for a QSAR study of trypanocidal activity for quinone compounds and design new quinone compounds. Chemom. Intell. Lab. Syst., 2014, 139, 168-174.
[8]
Veyseh, S.; Hamzehali, H.; Niazi, A.; Ghasemi, J.B. Application of multivariate image analysis in QSPR study of pKa of various acids by principal components-least squares support vector machine. J. Chil. Chem. Soc., 2015, 60(3), 2985-2987.
[9]
Ottavian, M.; Barolo, M.; García-Muñoz, S. Multivariate image and texture analysis to investigate the erosion mechanism of film-coated tablets: An industrial case study. J. Pharm. Innov., 2014, 9(1), 5-15.
[10]
Guimarães, M.C.; da Mota, E.G.; Silva, D.G.; Freitas, M.P. Aug-MIA-QSPR modelling of the toxicities of anilines and phenols to Vibrio fischeri and Pseudokirchneriella subcapitata. Chemom. Intell. Lab. Syst., 2014, 134, 53-57.
[11]
Goodarzi, M.; Freitas, M.P. Predicting boiling points of aliphatic alcohols through multivariate image analysis applied to quantitative structure- property relationships. J. Phys. Chem. A, 2008, 112(44), 11263-11265.
[12]
Bitencourt, M.; Freitas, M.P.; Rittner, R. The MIA‐QSAR method for the prediction of bioactivities of possible acetylcholinesterase inhibitors. Archiv. der. Pharmazie., 2012, 345(9), 723-728.
[13]
Garkani-Nejad, Z.; Poshteh-Shirani, M. Prediction of antihypertensive activity of pyridazinone derivatives through multivariate image analysis applied to QSAR. Med. Chem. Res., 2013, 22(7), 3389-3397.
[14]
Goodarzi, M. P Freitas, M. MIA-QSAR coupled to different regression methods for the modeling of antimalarial activities of 2-aziridinyl and 2, 3-bis-(aziridinyl)-1, 4-naphtoquinonyl sulfate and acylate derivatives. Med. Chem., 2011, 7(6), 645-654.
[15]
Nunes, C.A.; Freitas, M.P. Introducing new dimensions in MIA-QSAR: A case for chemokine receptor inhibitors. Eur. J. Pharm. Sci., 2013, 62, 297-300.
[16]
Shahlaei, M.; Pourhossein, A. A 2D image-based method for modeling some c-Src tyrosine kinase inhibitors. Med. Chem. Res., 2013, 22(6), 3012-3025.
[17]
Geladi, P.; Kowalski, B.R. Partial least-squares regression: A tutorial. Anal. Chim. Acta, 1986, 185, 1-17.
[18]
Katritzky, A.R.; Kuanar, M.; Slavov, S.; Hall, C.D.; Karelson, M.; Kahn, I.; Dobchev, D.A. Quantitative correlation of physical and chemical properties with chemical structure: Utility for prediction. Chem. Rev., 2010, 110(10), 5714-5789.
[19]
Shayanfar, A.; Ghasemi, S.; Soltani, S.; Asadpour-Zeynali, K.J.; Doerksen, R.; Jouyban, A. Quantitative structure-activity relationships of imidazole-containing farnesyltransferase inhibitors using different chemometric methods. Med. Chem., 2013, 9(3), 434-448.
[20]
Roy, K.; Mandal, A.S. Development of linear and nonlinear predictive QSAR models and their external validation using molecular similarity principle for anti-HIV indolyl aryl sulfones. J. Enzyme Inhib. Med. Chem., 2008, 23(6), 980-995.
[21]
Wong, I.L.; Wang, B-C.; Yuan, J.; Duan, L-X.; Liu, Z.; Liu, T.; Li, X-M.; Hu, X.; Zhang, X-Y.; Jiang, T. Potent and nontoxic chemosensitizer of p-glycoprotein-mediated multidrug resistance in cancer: Synthesis and evaluation of methylated epigallocatechin, gallocatechin, and dihydromyricetin derivatives. J. Med. Chem., 2015, 58(11), 4529-4549.
[22]
Soltani, S.; Abolhasani, H.; Zarghi, A.; Jouyban, A. QSAR analysis of diaryl COX-2 inhibitors: Comparison of feature selection and train-test data selection methods. Eur. J. Med. Chem., 2010, 45(7), 2753-2760.
[23]
Daszykowski, M.; Serneels, S.; Kaczmarek, K.; Van Espen, P.; Croux, C.; Walczak, B. TOMCAT: A MATLAB toolbox for multivariate calibration techniques. Chemom. Intell. Lab. Syst., 2007, 85(2), 269-277.
[24]
Alexander, D.; Tropsha, A.; Winkler, D.A. Beware of R 2: Simple, unambiguous assessment of the prediction accuracy of QSAR and QSPR models. J. Chem. Inf. Model., 2015, 55(7), 1316-1322.
[25]
Shayanfar, S.; Shayanfar, A.; Ghandadi, M. Image‐based analysis to predict the activity of tariquidar analogs as p‐glycoprotein inhibitors: The importance of external validation. Archiv. der. Pharmazie., 2016, 349(2), 124-131.
[26]
Chirico, N.; Gramatica, P. Real external predictivity of QSAR models: How to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient. J. Chem. Inf. Model., 2011, 51(9), 2320-2335.
[27]
Dearden, J.; Cronin, M.; Kaiser, K. How not to develop a quantitative structure-activity or structure-property relationship (QSAR/QSPR). SAR QSAR Environ. Res., 2009, 20(3-4), 241-266.
[28]
Parveen, Z.; Brunhofer, G.; Jabeen, I.; Erker, T.; Chiba, P.; Ecker, G.F. Synthesis, biological evaluation and 3D-QSAR studies of new chalcone derivatives as inhibitors of human P-glycoprotein. Bioorg. Med. Chem., 2014, 22(7), 2311-2319.
[29]
Sousa, I.J.; Ferreira, M.J.U.; Molnár, J.; Fernandes, M.X. QSAR studies of macrocyclic diterpenes with P-glycoprotein inhibitory activity. Eur. J. Pharm. Sci., 2013, 48(3), 542-553.
[30]
Ghandadi, M.; Shayanfar, A.; Hamzeh-Mivehroud, M.; Jouyban, A. Quantitative structure activity relationship and docking studies of imidazole-based derivatives as P-glycoprotein inhibitors. Med. Chem. Res., 2014, 23(11), 4700-4712.

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy