Computational Evaluation of Statin Analogs Targeting HMG-CoA Reductase for Coronary Artery Disease Treatment

Cardiovascular diseases remain a leading cause of global mortality. While statins are pivotal in managing risk, most research focuses on their derivatives. This study provides a novel computational evaluation of statin analogs, addressing a significant literature gap. Our comprehensive in silico approach, integrating ADMET profiling, molecular docking, and extensive 200-ns molecular dynamics (MD) simulations, investigated the pharmacokinetic behavior, binding affinities, and structural stability of five statin analogs against HMG-CoA reductase. ADMET analysis showed that analogs of simvastatin, lovastatin, and pravastatin have favorable pharmacological profiles and low toxicity. While docking showed that simvastatin and lovastatin analogs had the strongest affinities, MD offered critical mechanistic insights. The unbound enzyme exhibited significant conformational flexibility. In contrast, binding induced a superior stabilizing effect, confining the protein to a single, compact, low-energy state, as confirmed by free energy landscape analysis. This ligand-induced rigidity is a powerful indicator of enhanced inhibitory efficacy and stability. Our findings highlight that statin analogs are a promising class whose unique binding dynamics offer a new, rational pathway for designing more effective HMG-CoA reductase inhibitors.