Molecular Modelling, Docking and Pharmacokinetic Studies of N-Arylidenequinoline-3-Carbohydrazides Analogs as Novel β-Glucuronidase Inhibitors

Abstract

Quantitative structure-activity relationships (QSAR) modelling on 30 N-Arylidenequinoline-3-carbohydrazides analogs was performed using Multi-Linear Regression (MLR) analysis adopting Genetic Function Algorithm (GFA) method. Semi empirical method using PM6 basis set was used for complete geometry optimization of the data set. The best model was chosen based on its statistical fit due to it good internal and external validations. From the Williams plot, it can be inferred that the reported model can make prediction of new compounds that are not within the data set. The molecular docking study showed that, the most active chemical in the data set was better than the standard β-glucuronidase inhibitor both in terms of binding scores and the amino acid residues that interacted with the drug and β-glucuronidase enzyme. The Pharmacokinetic studies indicated that none of the chemicals violated any of the condition set by the Lipinski′s Rule of five which confirm the bioavailability of these chemicals. The results these findings give room for designing novel β-glucuronidase inhibitors that are highly effective.
 
 Resumen. Se llevó a cabo la técnica de QSAR en 30 analogos de N-arilidenequinolina-3-carbohidrazidas mediante el analisis de regresesión lineal múltiple (MLS) adopatando el método del algoritmo de función genética (GFA). Para la optimización completa de la geometría del conjunto de datos se utilizó un método semiémpirico del conjunto de bases PM6. El mejor modelo fue elegido basado en función de su ajuste estadístico debido a su validación interna y externa. A partir de la gráfica de Williams, se puede inferir que el modelo reportado puede predecir nuevos compuestos que no se encuentran en el conjunto de datos. Este estudio de acomplamiento molecular mostró que, el químico más activo del conjunto de datos fue mejor que el inhibidor estándar β-glucuronidasa, tanto en términos de unión y en términos de interacción de los residuos con el fármaco y la enzima β-glucuronidasa. Los estudios farmacocinéticos que indicaron que ninguno de los fármacos incumple ninguna de las condiciones establecidas por la regla de cinco de Lipinski, en donde se confirma la biodisponibilidad de estos químicos. Los resultados de los hallazgos computacionales permiten diseñar nuevos inhibidores de la β-glucuronidasa que son altamente efectivos.

Molecular modeling and docking analysis of bis-indolymethanes derivatives as human β-glucuronidase enzyme inhibitors

“…Ligands were prepared prior to the commencement of the docking simulation, by saving the optimum conformation ascertained using density functional theory in protein data bank file (pdb file format). The crystal structure of Humanβ-glucuronidase was retrieved from pdb with pdbID 1bhg (Ibrahim et al, 2020c). The preparation of the human β-glucuronidase for the docking simulation was done using Discovery Studio Visualizer, by removing chain B, heteroatoms and co-ligands from the dimer saved also as protein data bank file (pdb file format) (Abdulfatai et al, 2017).Pyrex software was used in the execution of the docking simulation in which the ligands were docked to the binding site of the human β-glucuronidase (Trott and Olson, 2010).…”

2D-QSAR, molecular docking, drug-likeness, and ADMET/pharmacokinetic predictions of some non-small cell lung cancer therapeutic agents

“… 19 , 20 Quantitative structure–activity relationship (QSAR) is a CADD method that entails the generation of a mathematical model, which relates the biological activities of a set of molecules to their molecular descriptors (physicochemical properties). 21 These properties have a key impact on the drug's activity. Molecular docking is also a CAAD technique utilized to reveal the interaction between a drug (ligand) and a receptor and how this drug binds to the target.…”