Combining explainable machine learning, demographic and multi-omic data to identify precision medicine strategies for inflammatory bowel disease

To pharmacologically assess patient variation in response to IBD treatment, we used the reduction in the release of the inflammatory cytokine TNFa from the fresh IBD tissues in the presence or absence of test drugs, as a measure of drug efficacy. The TNF pathway is a common target in current therapies for IBD; we initially explored the effects of a mitogen-activated protein kinase (MAPK) inhibitor on the production of TNFa; however, we later show the approach can be applied to other targets, test drugs or mechanisms of interest. Our best model was able to predict TNFa levels from a combination of integrated demographic, medicinal and genomic features with an error as low as 4.98% on unseen patients. We incorporated transcriptomic data to validate and expand insights from genomic features. Our results showed variations in drug effectiveness between patients that differed in gender, age or condition and linked new genetic polymorphisms in our cohort of IBD patients to variation in response to the anti-inflammatory treatment BIRB796 (Doramapimod). Our approach models drug response in a relevant human tissue model of IBD while also identifying its most predictive features as part of a transparent ML-based precision medicine strategy.