Artificial Intelligence-rationalized balanced PPARα/γ dual agonism resets the dysregulated macrophage processes in inflammatory bowel disease

Abstract

A computational platform, the Boolean network explorer (BoNE), has recently been developed to infuse AI-enhanced precision into drug discovery; it enables querying and navigating invariant Boolean Implication Networks of disease maps for prioritizing high-value targets. Here we used BoNE to query an Inflammatory Bowel Disease (IBD)-map and prioritize a therapeutic strategy that involves dual agonism of two nuclear receptors, PPAR/{gamma}. Balanced agonism of PPAR/{gamma} was predicted to modulate macrophage processes, ameliorate colitis in network-prioritized animal models, reset the gene expression network from disease to health, and achieve a favorable therapeutic index that tracked other FDA-approved targets. Predictions were validated using a balanced and potent PPAR/{gamma}-dual agonist (PAR5359) in two pre-clinical murine models, i.e., Citrobacter rodentium-induced infectious colitis and DSS-induced colitis. Using a combination of selective inhibitors and agonists, we show that balanced dual agonism promotes bacterial clearance more efficiently than individual agonists, both in vivo and in vitro. PPARa is required and its agonism is sufficient to induce the pro-inflammatory cytokines and cellular ROS, which are essential for bacterial clearance and immunity, whereas PPARg-agonism blunts these responses, delays microbial clearance and induces the anti-inflammatory cytokine, IL10; balanced dual agonism achieved controlled inflammation while protecting the gut barrier and reversal of the transcriptomic network. Furthermore, dual agonism reversed the defective bacterial clearance observed in PBMCs derived from IBD patients. These findings not only deliver a macrophage modulator for use as barrier-protective therapy in IBD, but also highlight the potential of BoNE to rationalize combination therapy.