Computational identification of epifriedelanol and derived analogs from Mikania cordata as potential HMG-CoA reductase inhibitors

Abstract

Hypercholesterolemia, a major risk factor for cardiovascular diseases, arises from elevated blood cholesterol levels and remains a global health concern. The limitations of current therapies underscore the need for alternative drugs from natural sources. Mikania cordata (Asteraceae) is an ethnomedicinally important species that harbors numerous bioactive phytoconstituents. In this study, 91 phytocompounds of this medicinal species were virtually screened targeting the HMG-CoA (3-hydroxy-3-methylglutaryl-coenzyme A) reductase protein. Molecular docking, ADMET (absorption, distribution, metabolism, excretion, and toxicity), and MM/GBSA (molecular mechanics/generalized born surface area) analyses identified epifriedelanol as the best lead candidate among the phytocompounds with strong binding affinity (−8.6 kcal/mol), drug-likeness, and free binding energy (−39.5 kcal/mol), outperforming the standard drug atorvastatin (−7.7 kcal/mol and −21.4 kcal/mol). Analogs of epifriedelanol (EA) were further explored, generating 451 compounds. High-throughput screening of these analogs identified 244 compounds with a docking score higher than atorvastatin (−7.7 kcal/mol). The ADMET evaluation highlighted two analogs, EA2 and EA3, with docking scores of −9.3 kcal/mol and supportive MM/GBSA free energies (−31.9 and −43.7 kcal/mol). Molecular dynamics simulation (500 ns) confirmed the structural stability of epifriedelanol, EA2, and EA3, while essential dynamics and Gibbs free energy landscape analyses indicated a binding behavior comparable to that of atorvastatin. Target class analysis predicted interactions with nuclear receptors. These findings suggest that epifriedelanol and its analogs are promising natural leads against hypercholesterolemia, warranting further in vitro and in vivo validation.