Background: Enzymes belonging to the kinase family have been extensively implicated in cancer. Aurora A (AURKA) is crucial in regulating the cell cycle. AURKA's significant role in the formation of abnormal mitotic spindles and the failure of cytokinesis positions it as a promising target for anticancer treatments. Notably, AURKA is observed to be overexpressed in various cancer types, making its inhibition a compelling strategy for the development of anticancer agents. Objective: In this study, we set out to investigate a novel de novo design strategy aimed at developing and optimizing potent inhibitors for Aurora Kinase A. Given Aurora Kinase A's critical role in cell cycle regulation and its overexpression in various cancers, it presents a promising target for therapeutic intervention. Our goal was to create a new library of compounds, building on existing inhibitors known for their selectivity and potency against Aurora Kinase A. By making strategic modifications to these lead molecules, we aimed to improve their binding affinity and inhibitory effectiveness. This research was focused on identifying and refining compounds with enhanced drug-like properties and robust inhibitory potential, contributing to the advancement of effective anticancer therapies. Methods: A compound library based on known inhibitors having Aurora Kinase A selectivity and IC50 value in the nanomolar range was designed by modification in the lead molecules identified by analyzing the binding mode of the molecules in the catalytic site of the enzyme. A molecular docking study was performed in GOLD 2020. Drug-likeness ADME parameters (molecular weight, H-bond acceptors, H-bond donors, LogP, and the number of rotatable bonds) of designed molecules were calculated using SwissADME, pkCSM, and ProToxII web servers. Results: The docking study, utilizing GOLD 2020 software on Aurora Kinase A (PDB: 3W2C), successfully identified inhibitors that hindered the enzyme's activity by occupying its catalytic site. This inhibition mechanism, consistent across all cases, involves crucial interactions with residues such as Ala213, Asp274, and Phe144. A detailed analysis of the compounds guided the design of new analogs, aiming to enhance the lead compound's affinity for the receptor. Subsequent derivatization of M1 and M15 resulted in molecules (M1_46, M1_49, M1_50, M15_21, M15_14, and M15_43) showing a notable 10-15% increase in the Chemscore fitness scoring function compared to their parent molecules. This improvement correlated with a rise in the number of hydrogen bond interactions in the complexes, guiding further development. Conclusion: This computational assessment lays a foundation for further in-vitro and in-vivo studies in drug development, suggesting these derivatives as promising candidates for cancer treatment.
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