A gastrointestinal locally activating Janus kinase inhibitor to treat ulcerative colitis.

ABSTRACT: In this study, we integrated machine learning (ML), structure-tissue selectivity-activity-relationship (STAR), and wet lab synthesis/testing to design a gastrointestinal (GI) locally activating JAK inhibitor for ulcerative colitis treatment. The JAK inhibitor achieves site-specific efficacy through high local GI tissue selectivity while minimizing the requirement for JAK isoform specificity to reduce systemic toxicity. We used the ML model (CoGT) to classify whether the designed compounds were inhibitors or noninhibitors. Then we used the regression ML model (MTATFP) to predict their IC₅₀ against related JAK isoforms of predicted JAK inhibitors. The ML model predicted MMT3-72, which was retained in the GI tract, to be a weak JAK1 inhibitor, while MMT3-72-M2, which accumulated in

SUBMITTER: Bu Y 

PROVIDER: S-EPMC10755797 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature