Development of a Novel CYP3A4 Classifier Model via Site of Metabolism (SOM)-based Molecular Docking, Multivariate Analysis and Molecular Dynamics of Known Substrates and Inhibitors

• Published in: Journal of Computational Biophysics and Chemistry
• Main Author: Ridhwan M.J.M.; Hashim N.A.A.; Kasim N.; Abdullah N.N.; Inayatsyah N.A.; Abid O.; Ismail N.H.; Imran S.
• Format: Article
• Language: English
• Published: World Scientific 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179061037&doi=10.1142%2fS2737416523500618&partnerID=40&md5=93d0041781a4a6a578ac4888d8971c9b

CYP3A4 is a major hepatic enzyme essential for metabolizing diverse chemical entities. The development of CYP3A4 substrate and inhibitor classifier models is a valuable research strategy to prevent toxicokinetics. Currently, no molecular docking model is available to classify the substrates and inhibitors for a wide range of chemical entities. This study generated a CYP3A4 substrate-inhibitor classifier model using multivariate analysis of selected variables from site of metabolism (SOM)-based docking results and molecular descriptors. CYP3A4 SOM-based molecular docking experiment was performed on the substrates and inhibitors from the DrugBank database. The relevant descriptors were selected using the area under the receiving operating curve (AUROC) and boxplot analysis. The selected variables were used to generate a CYP3A4 substrate-inhibitor classifier model using multivariate analysis. Two complexes were selected for molecular dynamic simulations. A total of 326 substrates and 154 inhibitors were successfully docked. The SOM-based docking model predicted 78.5% SOM correctly of the substrates. The CDOCKER energy, improper dihedral energy, potential energy, initial RMS (root mean square) gradient, and RMS gradient demonstrated acceptable AUROC, sensitivity, specificity, and Youden’s index results. The selected variables were subjected to multivariate analysis, and the PLS-DA analysis classification model showed promising performance in predicting chemical entities’ behavior as substrates or inhibitors. This model classified the external samples correctly with over 70.0% prediction accuracy. Molecular dynamic simulations showed that the selected complexes obtained from the docking model produced stable complexes. The classifier model is applicable as a research tool for the early stages of drug discovery. © 2024 World Scientific Publishing Company.