Parallel screening of drug-like natural compounds using Caco-2 cell permeability QSAR model with applicability domain, lipophilic ligand efficiency index and shape property: A case study of HIV-1 reverse transcriptase inhibitors

Abstract

The traditional drug design strategy centrally focuses on optimizing binding affinity with the receptor target and evaluates pharmacokinetic properties at a later stage which causes high rate of attrition in clinical trials. Alternatively, parallel screening allows evaluation of these properties and affinity simultaneously. In a case study to identify leads from natural compounds with experimental HIV-1 reverse transcriptase (RT) inhibition, we integrated various computational approaches including Caco-2 cell permeability QSAR model with applicability domain (AD) to recognize drug-like natural compounds, molecular docking to study HIV-1 RT interactions and shape similarity analysis with known crystal inhibitors having characteristic butterfly-like model. Further, the lipophilic properties of the compounds refined from the process with best scores were examined using lipophilic ligand efficiency (LLE) index. Seven natural compound hits viz. baicalien, (+)-calanolide A, mniopetal F, fagaronine chloride, 3,5,8-trihydroxy-4-quinolone methyl ether derivative, nitidine chloride and palmatine, were prioritized based on LLE score which demonstrated Caco-2 well absorption labeling, encompassment in AD structural coverage, better receptor affinity, shape adaptation and permissible AlogP value. We showed that this integrative approach is successful in lead exploration of natural compounds targeted against HIV-1 RT enzyme.