Intestinal permeability of N-acetylcysteine is driven by gut microbiota-dependent cysteine palmitoylation.

Trillions of intestinal microbiota are essential to the permeability of orally administered drugs. However, identifying microbial-drug interactions remains challenging due to the highly variable composition of intestinal flora among individuals. Using single-pass intestinal perfusion (SPIP) platform, we establish the microbiota-based permeability screening framework involving germ-free (GF) and specific-pathogen-free (SPF) rats to compare in-situ P-values and metabolomic profiles of 32 orally administered drugs with disputable classifications of permeability, prior to the verifications of bioorthogonal chemistry and LC-MS/MS. In contrast with SPF controls, N-Acetylcysteine (NAC) exhibits significantly increased permeability in GF rats, which is inversely related to reduced cysteine-3-ketosphinganine by Bacteroides. To further validate these microbiome features, we integrate clinical descriptors from a prospective cohort of 319 participants to optimize a 15-feature eXtreme Gradient Boosting (XGB) model, which reveal that cysteine palmitoylation by intestinal microbiota has significantly affected NAC permeability. By comparison of net reclassification improvement (NRI) index, this machine learning (ML) model of clinical prediction model encompassing intestinal microbial features outperforms other three commercial models in predicting NAC permeability. Here we have developed an intestinal microbiota-based strategy to evaluate uncharacterized NAC permeability, thus accounting for its discordant biopharmaceutics classification.