Coronary artery disease (CAD) is the leading cause of mortality and disability worldwide. Even in patients treated with optimized standard-of-care regimens, residual morbidity and mortality remain high. New strategies that directly target atherosclerosis?the main cause of CAD?are urgently needed. One innovative approach is to find drugs that target the molecular dysfunctions that drive atherosclerosis in the arterial wall. Systems genetics is a new approach that models molecular dysfunctions of complex traits like CAD in the form of regulatory gene networks (RGNs). Combined with network-driven computational approaches to repurpose existing drugs targeting networks in complex diseases, systems genetics can speed up the discovery of powerful strategies to treat CAD. In our previous work, using a systems genetics approach, we have identified RGN42?a CAD-causal network consisting of RNA-processing genes acting in the atherosclerotic arterial wall of CAD patients of the Stockholm Atherosclerosis Gene Expression (STAGE) study. As a proof-of-concept, we silenced the four key drivers genes of RGN42 and found that cholesterol-ester accumulation in foam-cells in vitro was markedly affected. Next we applied a rigorous combination of systems biology and computational drug repurposing analyses and we identified several compounds predicted to influence the four key drivers in RGN42 that affect foam cell formation. Preliminary phenotypic screening revealed that two of our top-hit compounds strongly inhibit foam-cell formation in vitro. Given these findings, we hypothesize that RGN42-targeted compound(s) will show anti- atherosclerotic efficacy. We propose to rigorously validate the pre-clinical efficacy of either FDA approved or phase 2a-ready test compound(s), with the goal of translating the findings into human clinical trials.
In specific Aim 1, we will identify the most effective RGN42-targeted compounds for their ability to prevent foam-cell formation in vitro and atherosclerosis in vivo.
In Specific Aim 2, we will measure compound(s) therapeutic efficacy in vivo using translational pre- clinical imaging in a well-validated rabbit model of atherosclerosis that recapitulates the complexity of human atherosclerotic plaques better than mouse models. The use of sophisticated, non-invasive imaging modalities to measure the efficacy of compounds targeting RGN42 in a validated large animal model of atherosclerosis will provide robust evidence for the translation of our findings to clinical trials. This study will set the stage for a translational platform to speed the repurposing of existing drugs with new indications identified by network strategies to treat CAD.
These studies are relevant to public health for two reasons. First, the may provide proof-of- concept evidence for the use of network-driven computational approaches to identify new therapies to target complex disease like coronary artery disease (CAD). Secondly, they may demonstrate that quantitative non-invasive imaging is an efficient platform for measuring drug efficacy in a pre-clinical setting, for a smooth, fast translation of new therapeutics into clinical trials.
