title: Image-Based Analysis to Predict the Activity of Tariquidar Analogs as P-Glycoprotein Inhibitors: The Importance of External Validation.
abstract: Permeability glycoprotein (P-gp) is involved in the pathology of various diseases including cancer and epilepsy, mainly through the translocation of some medicines across the cell membrane. Here, we employed image-based quantitative structure-activity relationship (QSAR) models to predict the P-gp inhibitory activity of some Tariquidar derivatives. The structures of 65 Tariquidar derivatives and their P-gp inhibition activities were collected from the literature. For each compound, the pixels of bidimensional images and their principal components (PCs) were calculated using MATLAB software. Various statistical methods including principal component regression, artificial neural networks, and support vector machines were employed to investigate the correlation between the PCs and the activity of the compounds. The predictability of the models was investigated using external validation and applicability domain analysis. An artificial neural network-based model demonstrated the best prediction results for the test set. Moreover, external validation analysis of the developed models supports the idea that R(2) cannot assure the validity of QSAR models and another criterion, i.e., the concordance correlation coefficient (CCC) parameter, should be involved to evaluate the validity of the QSAR models. The results of this study indicate that image analysis could be as suitable as descriptors calculated by commercial software to predict the activity of drug-like molecules.
authors.affiliation: Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.|Faculty of Pharmacy, Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran.
authors.forename: Shadi
authors.initials: S
authors.lastname: Shayanfar
authors.affiliation: Drug Applied Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran.|Pharmaceutical Analysis Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
authors.forename: Ali
authors.initials: A
authors.lastname: Shayanfar
authors.affiliation: Department of Pharmaceutical Biotechnology, Faculty of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran.
authors.forename: Morteza
authors.initials: M
authors.lastname: Ghandadi
mesh_terms: D020168:ATP Binding Cassette Transporter, Subfamily B, Member 1 / Q000037:antagonists & inhibitors* / Q000737:chemistry; D016014:Linear Models; D008958:Models, Molecular; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D025341:Principal Component Analysis; D021281:Quantitative Structure-Activity Relationship; D011804:Quinolines / Q000737:chemistry*; D060388:Support Vector Machine
chemical_list: D020168:ATP Binding Cassette Transporter, Subfamily B, Member 1; D011804:Quinolines; C402343:tariquidar
keywords: External validation; Image analysis; P-glycoprotein; QSAR