Artificial intelligence-rationalized balanced PPAR alpha/gamma dual agonism resets dysregulated macrophage processes in inflammatory bowel disease

A computational platform, Boolean network explorer (BoNE), has recently been developed to infuse AI-enhanced precision into drug discovery; it enables 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 alpha/gamma. Balanced agonism of PPAR alpha/gamma was predicted to modulate macrophage processes, ameliorate colitis, 'reset' the gene expression network from disease to health. Predictions were validated using a balanced and potent PPAR alpha/gamma-dual-agonist (PAR5359) in Citrobacter rodentium- and DSS-induced murine colitis models. Using inhibitors and agonists, we show that balanced-dual agonism promotes bacterial clearance efficiently than individual agonists, both in vivo and in vitro. PPAR alpha is required and sufficient to induce the pro-inflammatory cytokines and cellular ROS, which are essential for bacterial clearance and immunity, whereas PPAR gamma-agonism blunts these responses, delays microbial clearance; 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. A Boolean network explorer (BoNE) computational algorithm was used to identify a dual agonist of the nuclear receptors PPAR alpha and PPAR gamma that may be therapeutic in treating inflammatory bowel disease.