Predictive Modeling of HMG-CoA Reductase Inhibitory Activity and Design of New HMG-CoA Reductase Inhibitors

As blood Cholesterol increases, it accumulates in the intima of blood vessels, elevating the risk of atherosclerosis and coronary artery disease. Drugs that inhibit enzymes essential for Cholesterol synthesis are effective in improving blood Cholesterol levels. Statins are used to treat hypercholesterolemia as they inhibit 3-hydroxyl-3-methylglutaryl coenzyme A (HMG-CoA) reductase (HMGR), the rate-limiting Enzyme in Cholesterol synthesis. Statins are known to exert their effects by translocating to the liver, where they are taken up by the organic anion transporting polypeptide 1B1 (OATP1B1). Therefore, we hypothesized that a compound with high HMGR inhibitory activity and high affinity for OATP1B1 would be an excellent new therapeutic agent for hypercholesterolemia with increased liver selectivity and fewer side effects. In this study, we developed two models for predicting HMGR inhibitory activity and OATP1B1 affinity to propose the chemical structure of a new therapeutic agent for hypercholesterolemia with both high inhibitory activity and high liver selectivity. HMGR inhibitory activity and OATP1B1 affinity prediction models were constructed with high prediction accuracy for the test data: r² = 0.772 and 0.768, respectively. New chemical structures were then input into these models to search for candidate compounds. We found compounds with higher HMGR inhibitory activity and OATP1B1 affinity than rosuvastatin, the most recently developed statin drug, and compounds that did not have a common structure of statins with high HMGR inhibitory activity.