Identification of Novel HMG-CoA Reductase Inhibitors as Potential Antihyperlipidemic Agents Using Computer-Aided Drug Design, Molecular Docking, and In Silico ADMET Analysis

Authors

  • Nusieba A. Mohammed Ibrahim Department of Pharmacology and Toxicology, Faculty of Pharmacy, Omar Al-Mukhtar University, Albayda, Libya Author
  • Manal G. S. Diryaq Department of Pharmacology and Toxicology, Faculty of Pharmacy, Omar Al-Mukhtar University, Albayda, Libya Author
  • Nisreen S. S. Majeed Department of Pharmaceutical Care, Faculty of Pharmacy, Omar Al-Mukhtar University, Albayda, Libya Author
  • Hayder S. Ali Hussein Department of Pharmacology and Toxicology, Faculty of Pharmacy, University of Mosul, Mosul, Iraq Author

Keywords:

Hyperlipidemia; HMG-CoA reductase, Computer-aided drug design, Molecular docking, ; In silico ADMET analysis;, Statins

Abstract

Hyperlipidemia is a major metabolic disorder that contributes substantially to the development of cardiovascular diseases and continues to impose a considerable burden on global morbidity and mortality. As the rate-limiting enzyme in the mevalonate pathway, 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase plays a central role in cholesterol biosynthesis and remains the principal molecular target of statin therapy. Despite the proven efficacy of statins in lowering serum cholesterol, their clinical application may be restricted by adverse effects, drug interactions, and interindividual variability in treatment response. Consequently, the discovery of novel HMG-CoA reductase inhibitors with improved pharmacological characteristics remains an important objective in drug development. This study employed a computer-aided drug design (CADD) strategy to identify and evaluate new HMG-CoA reductase inhibitor candidates for the management of hyperlipidemia. Structurally optimized molecules were generated from established pharmacophoric features and evaluated through molecular docking to examine their affinity for the enzyme active site. Their drug-likeness and pharmacokinetic characteristics were further investigated using in silico absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction tools. The docking performance of the designed molecules was compared with that of reference statins. Several candidates demonstrated stronger predicted binding affinities than the reference drug and established favorable interactions with critical catalytic amino acid residues through hydrogen bonding and hydrophobic contacts. Furthermore, the selected lead compounds displayed desirable drug-likeness properties, favorable predicted gastrointestinal absorption, and acceptable safety profiles. These computational findings identify several promising lead compounds that warrant further investigation as potential antihyperlipidemic agents. Nevertheless, additional experimental studies, including biochemical assays and both in vitro and in vivo studies are required to confirm their biological activity and therapeutic potential.

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Published

2026-08-01

Issue

Section

Applied Sciences Theme

How to Cite

Nusieba A. Mohammed Ibrahim, Manal G. S. Diryaq, Nisreen S. S. Majeed, & Hayder S. Ali Hussein. (2026). Identification of Novel HMG-CoA Reductase Inhibitors as Potential Antihyperlipidemic Agents Using Computer-Aided Drug Design, Molecular Docking, and In Silico ADMET Analysis. Afro-Asian Journal of Scientific Research (AAJSR), 4(3), 105-111. https://aajsr.com/index.php/aajsr/article/view/969