In Silico Screening for Novel Leucine Aminopeptidase Inhibitors with 3,4-Dihydroisoquinoline Scaffold

Abstract

Cancers are the leading cause of deaths worldwide. In 2018, an estimated 18.1 million new cancer cases and 9.6 million cancer-related deaths occurred globally. Several previous studies have shown that the enzyme, leucine aminopeptidase is involved in pathological conditions such as cancer. On the basis of the knowledge that isoquinoline alkaloids have antiproliferative activity and inhibitory activity towards leucine aminopeptidase, the present study was conducted a study which involved database search, virtual screening, and design of new potential leucine aminopeptidase inhibitors with a scaffold based on 3,4-dihydroisoquinoline. These compounds were then filtered through Lipinski’s “rule of five,” and 25 081 of them were then subjected to molecular docking. Next, three-dimensional quantitative structure-activity relationship (3D-QSAR) study was performed for the selected group of compounds with the best binding score results. The developed model, calculated by leave-one-out method, showed acceptable predictive and descriptive capability as represented by standard statistical parameters r2 (0.997) and q2 (0.717). Further, 35 compounds were identified to have an excellent predictive reliability. Finally, nine selected compounds were evaluated for drug-likeness and different pharmacokinetics parameters such as absorption, distribution, metabolism, excretion, and toxicity. Our methodology suggested that compounds with 3,4-dihydroisoquinoline moiety were potentially active in inhibiting leucine aminopeptidase and could be used for further in-depth in vitro and in vivo studies.

Keywords

• leucine aminopeptidase inhibitor
• 3,4-dihydroisoquinoline
• molecular docking
• structure-based drug design
• 3D-QSAR
• drug-likeness