Understanding the contributions of microbiome-encoded drug metabolizing enzymes

项目摘要 与母体化合物相比，药物代谢物可以具有不同的疗效和毒性特征，并且 药物代谢中的人际差异可以决定不良反应。值得注意的是，口服药物展示了 生物利用度低，以延迟释放的配方提供，或通过胆汁排泄可遇到 胃肠道中的细菌密度极大。肠道微生物群携带一种集体基因 比人类基因组大150倍的内容，编码了丰富的酶存储库，具有 代谢药物的能力，且个体差异很大。因此，微生物组有可能 影响血清药物和代谢物暴露。然而，用于鉴定活性物质的体外可扩展系统 微生物种类、酶和候选药物代谢物不可用。我们对此的理解 微生物群介导的药物代谢在很大程度上仅限于坊间事例，目前尚不清楚肠道是否 微生物编码代谢多种药物的酶。确定数量贡献的策略 肠道微生物药物代谢到血清药物和代谢物暴露也是不可用的。 我们开发了高通量的方法来生成新陈代谢的时间序列曲线 数百种人类肠道微生物群落和物种的数百种药物，揭示了微生物群- 负责这些转化的编码酶，并识别产生的候选药物代谢物 结果就是。我们还提供了将生理学药代动力学与诺生素结合起来的证据。 建模可以定量地揭示宿主和微生物对血清药物代谢物暴露的贡献 体内，使用一种被宿主和微生物组转化为单一代谢物的药物作为概念证明。 在这项提案的目标1中，我们提出了一个计划，以建立第一个药物代谢酶在 人体肠道微生物群。这些结果将揭示人类肠道微生物是否编码 代谢多种药物以及编码药物代谢酶的微生物组基因是否起作用 预测个体微生物组药物代谢能力的生物标记物。在目标2中，我们描述了一个 开发和测试捕获宿主和微生物组的广义宿主-微生物组药代动力学模型的策略 微生物将一种药物转化为多种代谢物，并在模型中包括肠-肝循环 拓扑学。如果成功，这些目标将建立广泛适用的方法，以确定何时以及如何 微生物组可以促进药物和其他小分子的新陈代谢。

Project Summary Drug metabolites can have distinct efficacy and toxicity profiles relative to the parent compound, and interpersonal variation in drug metabolism can determine adverse reactions. Notably, oral drugs that exhibit low bioavailability, are delivered in delayed release formulations, or are excreted through bile can encounter enormous densities of bacteria in the gastrointestinal tract. The gut microbiome carries a collective gene content 150-fold larger than the human genome, encodes a rich repository of enzymes with the potential capacity to metabolize drugs, and varies widely between individuals. The microbiome thus has the potential to impact serum drug and metabolite exposure. However, scalable in vitro systems for identification of active microbial species, enzymes, and candidate drug metabolites are not available. Our understanding of microbiome-mediated drug metabolism is largely limited to anecdotal examples, and it is unknown whether gut microbes encode enzymes that metabolize many drugs. Strategies for determining the quantitative contribution of gut microbial drug metabolism to serum drug and metabolite exposure are also not available. We developed high-throughput approaches that generate metabolomic time-series profiles of the metabolism of hundreds of drugs by hundreds of human gut microbial communities and species, uncover the microbiome- encoded enzymes responsible for these transformations, and identify candidate drug metabolites produced as a result. We also provide evidence that combining gnotobiotics with physiologically-based pharmacokinetic modeling can quantitatively disentangle host and microbial contributions to serum drug metabolite exposure in vivo, using a drug that is converted into a single metabolite by host and microbiome as a proof of concept. In Aim 1 of this proposal, we present a plan to establish the first repertoire of drug-metabolizing enzymes in the human gut microbiome. These results will reveal whether human gut microbes encode enzymes that metabolize many drugs and whether microbiome genes encoding drug-metabolizing enzymes serve as predictive biomarkers of the drug-metabolizing capacity of an individual's microbiome. In Aim 2, we describe a strategy to develop and test generalized host-microbiome pharmacokinetic models that capture host and microbial conversion of a drug into multiple metabolites and include enterohepatic circulation in the model topology. If successful, these aims will establish broadly applicable approaches to identify when and how the microbiome could contribute to the metabolism of drugs and other small molecules.