Search

Diabetes Mellitus (DM) is linked to various factors causing cardiovascular diseases, with uncontrolled postprandial hyperglycemia being a direct contributor. α‐Glucosidase inhibitors (AGIs) aid in reducing postprandial hyperglycemia, potentially mitigating cardiovascular risks. In order to synthesize novel chemical scaffolds with possible α‐glucosidase inhibition activity, a series of novel soritin sulfonamide derivatives were synthesized. The soritin hydrazide was treated with various aryl sulfonyl chlorides to obtain targeted compounds (1–16). Findings suggested that all compounds have better α‐glucosidase inhibition compared to standard drugs, acarbose (2187.00 ± 1.25 μM) and 1‐deoxynojirimycin (334.90 ± 1.10 μM), with IC50 values ranging from 3.81 ± 1.67 μM to 265.40 ± 1.58 μM. The most potent analog was Compound 13, a trichloro phenyl substituted compound, with IC50 value of 3.81 ± 1.67 μM. Structure–activity relationship (SAR) showed that introducing an additional chlorine group into the parent nucleus increases the potency. The docking studies validated that Compound 13 established hydrogen bonds with the active site residues Asp214, Glu276, and Asp349, while being further stabilized by hydrophobic interactions, providing an explanation for its high potency. [ABSTRACT FROM AUTHOR]

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. In silico structure-based analysis of new chemical entities toward CYP3A4 metabolism and inhibitory activity is helpful in pharmacokinetic research. This study analysed the results of SOM-based molecular docking and relevant descriptors using multivariate analysis to generate CYP3A4 substrate-inhibitor classifier model. [ABSTRACT FROM AUTHOR]