Atherosclerotic cardiovascular disease (CVD) 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 CVD?are urgently needed. One innovative approach is to find drugs that target the molecular dysfunctions that drive inflammation in human inflammatory cells. Systems genetics is a new approach that models molecular dysfunctions of complex traits like CVD in the form of gene networks. 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 CVD. Using our systems biology analysis pipeline to analyze time-of- flight mass cytometry (CyTOF), and mRNA sequencing (RNA-seq) data, we identified specific immune signaling pathways and an inflammatory transcriptional signature in human peripheral blood mononuclear cells (PBMCs). Next we applied a rigorous combination of systems biology and computational drug repurposing analyses and we identified several compounds predicted to influence this immune signature. Preliminary phenotypic screening revealed that our top-hit compound strongly inhibit this immune response. Given these findings, we hypothesize that targeted compound will show anti-atherosclerotic efficacy. We propose to rigorously validate the pre-clinical efficacy of phase 2a-ready test compound, with the goal of translating the findings into human clinical trials.
In specific Aim 1, we will test targeted compound ex-vivo in human PBMCs of CVD patients using CyTOF and in vivo in mice.
In Specific Aim 2, we will measure the therapeutic efficacy of targeted-compound 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 targeted compound in a validated large animal model of atherosclerosis will provide robust evidence for the translation of our findings to clinical trials.
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 atherosclerotic cardiovascular disease. Secondly, the use of quantitative non-invasive imaging as translational platform for measuring drug efficacy in a pre-clinical setting, will facilitate a smooth and fast translation of new therapeutics into clinical trials.
