<b>Intestinal permeability of N-acetylcysteine is driven by gut microbiota-dependent cysteine palmitoylation</b>

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 established the microbiota-based permeability screening framework involving germ-free (GF) and specific-pathogen-free (SPF) rats to compare<i> </i><i>in-situ</i> <i>P</i>_eff-values and metabolomic profiles of 32 orally administered drugs with disputable classifications of permeability. In contrast with SPF controls, N-Acetylcysteine (NAC) exhibited significantly increased permeability in GF rats, which was inversely related to reduced cysteine-3-ketosphinganine by <i>Bacteroides</i>. To further validate these microbiome features, we integrated clinical descriptors from a prospective cohort of 319 participants to optimize a 15-feature eXtreme Gradient Boosting (XGB) model. This machine learning (ML) model of clinical prediction revealed that cysteine palmitoylation by intestinal microbiota has significantly affected NAC permeability, thus leading to discordant biopharmaceutics classification.

数以万亿计的肠道菌群（intestinal microbiota）对于口服药物的肠黏膜渗透性至关重要。然而，由于个体间肠道菌群组成存在高度异质性，鉴定微生物-药物相互作用仍颇具挑战。本研究借助单次肠道灌流（single-pass intestinal perfusion, SPIP）平台，构建了基于菌群的渗透性筛选框架：选用无菌（germ-free, GF）与无特定病原体（specific-pathogen-free, SPF）大鼠，对32种存在渗透性分类争议的口服药物的原位（in-situ）表观渗透系数（P_eff）与代谢组谱进行对比分析。与SPF对照组相比，N-乙酰半胱氨酸（N-Acetylcysteine, NAC）在GF大鼠体内的渗透性显著升高，且该现象与拟杆菌属（Bacteroides）介导的半胱氨酸-3-酮鞘氨醇（cysteine-3-ketosphinganine）水平降低呈负相关。为进一步验证这些菌群相关特征，我们整合了包含319名受试者的前瞻性队列（prospective cohort）的临床信息，优化得到一个含15个特征的极限梯度提升（eXtreme Gradient Boosting, XGB）模型。该机器学习（machine learning, ML）临床预测模型显示，肠道菌群介导的半胱氨酸棕榈酰化（cysteine palmitoylation）会显著影响NAC的渗透性，进而导致其生物药剂学分类（biopharmaceutics classification）出现偏差。

搜集汇总

背景与挑战

背景概述

该数据集聚焦于肠道微生物群如何通过半胱氨酸棕榈酰化影响N-乙酰半胱氨酸的肠道渗透性，研究使用大鼠模型和机器学习方法分析药物渗透性，揭示了微生物群与药物相互作用的关键机制，并挑战了传统的生物药剂学分类。数据集包括实验数据和临床队列信息，旨在为药物开发和个性化医疗提供见解。