In a breakthrough for cardiovascular medicine, researchers have harnessed artificial intelligence to discover unexpected lipid-lowering effects in existing FDA-approved drugs. The study, published in Acta Pharmacologica Sinica , addresses critical gaps in hyperlipidemia treatment—where many patients struggle with intolerance or inadequate response to statins and other standard therapies.
Using a novel machine learning framework, the team analyzed 3,430 drugs (176 known lipid-lowering agents vs. 3,254 controls). Top-performing AI models flagged 29 candidates for repurposing. Crucially, these predictions underwent rigorous validation by using clinical data, mouse experiments, and molecular docking.
"We’ve established a paradigm for AI-driven drug repositioning," says senior author Dr. Peng Luo. "By integrating computational predictions with clinical and experimental validation, we bypass decades of traditional drug development—offering clinicians new tools faster and cheaper."
This study employs an innovative approach by integrating machine learning techniques to systematically explore the lipidlowering potential of non-lipid-lowering drugs, potentially offering novel treatment options for patients with hyperlipidemia. The research methodology encompasses retrospective clinical data analysis and in vivo animal experiments for validation, while also examining the binding and interaction mechanisms between drugs and lipid-lowering targets at the molecular level. This approach may provide alternative options for patients exhibiting poor tolerance or inadequate response to conventional lipid lowering therapies, thus offering the potential for individualized and precise treatment of hyperlipidemia. Consequently, this research has the potential to enhance patient outcomes, thereby demonstrating substantial academic value and promising clinical applicability.
Acta Pharmacologica Sinica
Experimental study
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Integration of machine learning and experimental validation reveals new lipid-lowering drug candidates
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.