Researchers developed a three-stage computational pipeline using Mendelian randomization to systematically identify existing drugs that could be repurposed for new therapeutic uses. Applied to cardiovascular disease, the method screened 2,923 circulating proteins from UK Biobank data, identifying 72 proteins linked to LDL cholesterol and 75 to triglycerides. Of these lipid-regulating targets, 18 also showed associations with coronary artery disease risk, ultimately revealing 5 proteins targeted by already-approved drugs as potential repurposing candidates. This approach represents a significant advance in drug discovery efficiency. Traditional pharmaceutical development takes 10-15 years and costs billions, while repurposing existing drugs can reduce timelines to 3-12 years with substantially lower costs and established safety profiles. The pipeline's incorporation of rigorous quality controls—including tests for pleiotropy and colocalization—addresses key limitations of previous repurposing methods. However, as a preprint awaiting peer review, these computational predictions require experimental validation and clinical testing. The methodology appears incremental yet practically valuable, offering a scalable framework that could accelerate identification of new therapeutic applications across multiple disease areas beyond cardiovascular health.