Screening of drug efficacy utilizing human atherosclerotic data

Despite effective lipid-lowering therapies, patients still face residual cardiovascular risk, with cardiovascular disease projected to affect ~41% of the Western population by 2030. This rise will significantly increase healthcare costs, with U.S. CVD expenses expected to triple from $273 billion to $818 billion between 2010 and 2030.

Many CVD drugs fail in Phase I-II trials due to lack of efficacy, leading to substantial financial losses for pharmaceutical companies. This highlights the need for better experimental models that predict clinical outcomes more accurately. Animal models often poorly resemble drug effects in humans. There is an unmet need for “human models” of atherosclerotic disease to test drug efficacy.

Since 2002, our group has built the world’s largest and best-phenotyped human atherosclerotic plaque biobank. The scale and depth of our data are unmatched, requiring years to replicate. Pharma companies frequently seek access to our cross-sectional data for drug validation in human samples. Previously, our main limitation was the cross-sectional nature of our data.

In this project, we have developed advanced algorithms for "virtual gene editing," allowing us to extrapolate RNA sequencing outcomes from our biobank. This enables us to predict drug effects on advanced human atherosclerotic plaques, providing a powerful tool for pharmaceutical research and development.