Image-based QSAR Model for the Prediction of P-gp Inhibitory Activity of Epigallocatechin and Gallocatechin Derivatives.
Image analysis
P-glycoprotein (P-gp)
PCR
QSAR
SVM
epigallocatechin.
Journal
Current computer-aided drug design
ISSN: 1875-6697
Titre abrégé: Curr Comput Aided Drug Des
Pays: United Arab Emirates
ID NLM: 101265750
Informations de publication
Date de publication:
2019
2019
Historique:
received:
28
12
2017
revised:
09
09
2018
accepted:
28
09
2018
pubmed:
4
10
2018
medline:
7
9
2019
entrez:
4
10
2018
Statut:
ppublish
Résumé
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. 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. 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. 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. 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.
Sections du résumé
BACKGROUND
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
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
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
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
CONCLUSIONS
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.
Identifiants
pubmed: 30280673
pii: CAD-EPUB-93408
doi: 10.2174/1573409914666181003152042
doi:
Substances chimiques
ATP Binding Cassette Transporter, Subfamily B, Member 1
0
Catechin
8R1V1STN48
gallocatechol
HEJ6575V1X
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
212-224Informations de copyright
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