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, 44, 708-715

Bringing Microbiome-Drug Interaction Research Into the Clinic


Bringing Microbiome-Drug Interaction Research Into the Clinic

Leah Guthrie et al. EBioMedicine.


Our understanding of the scope and clinical relevance of gut microbiota metabolism of drugs is limited to relatively few biotransformations targeting a subset of therapeutics. Translating microbiome research into the clinic requires, in part, a mechanistic and predictive understanding of microbiome-drug interactions. This review provides an overview of microbiota chemistry that shapes drug efficacy and toxicity. We discuss experimental and computational approaches that attempt to bridge the gap between basic and clinical microbiome research. We highlight the current landscape of preclinical research focused on identifying microbiome-based biomarkers of patient drug response and we describe clinical trials investigating approaches to modulate the microbiome with the goal of improving drug efficacy and safety. We discuss approaches to aggregate clinical and experimental microbiome features into predictive models and review open questions and future directions toward utilizing the gut microbiome to improve drug safety and efficacy.

Keywords: Drug metabolism; High throughput genomics; Metabolomics; Microbiome.


Fig. 1
Fig. 1
Gut microbiota-host liver metabolic interactions drive variability in drug response. a Hepatic and gut microbiome enzymes co-metabolize chemically diverse exogenously derived substrates including foods, therapeutic drugs and environmental toxins. Key host hepatic enzymes include the cytochrome P450s (CYPs) superfamily and flavin-containing monooxygenases (FMOs) [16] which are involved in phase I metabolism. Phase II enzymes including glutathione S-transferases (GST), sulfotransferases (SULTs) and uridine diphosphate-glucuronosyltransferases (UGTs). Hydrophilic therapeutic drug and drug conjugates excreted from the liver into the gastrointestinal tract via the biliary route are chemically modified primarily by gut microbiota hydrolytic and reductive reactions into hydrophobic products that can be reabsorbed via enterohepatic circulation [72], modified or extensively degraded by the gut microbiota. Gut microbiota metabolism also indirectly regulates phase I and II hepatic enzymes by producing metabolites, including uremic toxins and secondary bile acids, that alter hepatic enzyme expression and activity. b Gut microbiota enzyme catalyzed reactions have been linked to variation in patient response phenotypes. For example, microbial mediated azoreduction transforms the anti-inflammatory drug, sulfasalazine, into bioactive products. 10% of healthy individuals are poor converters of sulfasalazine [73]. Microbial metabolism also negatively impacts host drug responses. Approximately 10% of patients given the cardiac glycoside, digoxin, excrete high levels of an inactive metabolite which is generated by microbial enzymes [74]. 25% of patients taking Irinotecan with 5-fluorouracil and leucovorin for the treatment of colorectal cancer experience grade 3–4 diarrhea which is mediated by microbial β-glucuronidase reactivation of a major inactive metabolite of the drug [75].
Fig. 2
Fig. 2
Pipeline for metabolic phenotyping and modulation of microbiome driven adverse drug responses. a The construction of a high performance classier (HPC) to distinguish high drug metabolizers (HM) from low drug metabolizers (LM). For patients treated with therapeutic drugs that are susceptible to glucuronidation, such as irinotecan and NSAIDs, being a HM may reflect an elevated risk for drug-dependent toxicity. The main steps for metabolically phenotyping of HM and LM patients include data aggregation and preparation as input features for classifier training and testing, followed by the selection of key features that predict outcome and evaluation of classifier performance. The feature space for the classifier can be derived from preclinical and clinical studies and might include multi'omic data derived from both microbiome and host studies. This data can be integrated into hybrid COBRA-PBPK models to gain further predictive and mechanistic insight into drug pharmacokinetic profiles and aid in the identification of key host and microbiome parameters. b The HPC can be used to stratify new patients taking susceptible therapeutics into either HM or LM ‘metabotypes’ based on non-invasive fecal sampling alone or in addition to host biological samples. HM patients may undergo pre-treatment therapy, ranging from the use of probiotics and prebiotics to FMT, to modulate the microbiome towards a LM profile and improved treatment efficacy and safety.

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