Enhanced Characterization of Drug Metabolism and the Influence of the Intestinal Microbiome: A Pharmacokinetic, Microbiome, and Untargeted Metabolomics Study

Microdose Cocktail Study Reveals the Activity and Key Influencing Factors of OATP1B, P-Gp, BCRP, and CYP3A in End-Stage Renal Disease Patients

OATP1B, P-gp, BCRP, and CYP3A are the most contributing drug-metabolizing enzymes or transporters (DMETs) for commonly prescribed medication. Their activities may change in end-stage renal disease (ESRD) patients with large inter-individual variabilities (IIVs), leading to altered substrate drug exposure and ultimately elevated safety risk. However, the changing extent and indictive influencing factors are not quantified so far. Here, a microdose cocktail regimen containing five sensitive substrate drugs (pitavastatin, dabigatran etexilate, rosuvastatin, midazolam, and atorvastatin) for these DMETs was administrated to Chinese healthy volunteers and ESRD patients. Drug pharmacokinetics profiles were determined, together with physiological, pharmacogenetic, and gut microbiome signature. Population pharmacokinetic and machine learning model were established to identify key influencing factors and quantify their contribution to drug exposure change. The exposure of pitavastatin, dabigatran, rosuvastatin, and atorvastatin increased to 1.8-, 3.1-, 1.1-, and 1.3-fold, respectively, whereas midazolam exposure decreased by 72% in ESRD patients. Notably, in addition to disease state, the relative abundance of genus Veillonella and Clostridium_XIVb were firstly identified as significant influencing factors for PTV and RSV apparent clearance, respectively, suggesting their indicative role for OATP and BCRP activity evaluation. Moreover, several genera were found to strongly associate with drug clearance and reduce unexplained IIVs. Accordingly, it was estimated that OATP1B and intestine P-gp activity decreased by 35-75% and 29-44%, respectively, whereas BCRP and CYP3A4 activity may upregulate to some extent. Our study provides a quantitative and mechanistic understanding of individual DMET activity and could support precision medicine of substrate drugs in ESRD patients.

Bioengineered human gut-on-a-chip for advancing non-clinical pharmaco-toxicology

INTRODUCTION

There is a growing need for alternative models to advance current non-clinical experimental models because they often fail to accurately predict drug responses in human clinical trials. Human organ-on-a-chip models have emerged as promising approaches for advancing the predictability of drug behaviors and responses.

AREAS COVERED

We summarize up-to-date human gut-on-a-chip models designed to demonstrate intricate interactions involving the host, microbiome, and pharmaceutical compounds since these models have been reported a decade ago. This overview covers recent advances in gut-on-a-chip models as a bridge technology between non-clinical and clinical assessments of drug toxicity and metabolism. We highlight the promising potential of gut-on-a-chip platforms, offering a reliable and valid framework for investigating reciprocal crosstalk between the host, gut microbiome, and drug compounds.

EXPERT OPINION

Gut-on-a-chip platforms can attract multiple end users as predictive, human-relevant, and non-clinical model. Notably, gut-on-a-chip platforms provide a unique opportunity to recreate a human intestinal microenvironment, including dynamic bowel movement, luminal flow, oxygen gradient, host-microbiome interactions, and disease-specific manipulations restricted in animal and in vitro cell culture models. Additionally, given the profound impact of the gut microbiome on pharmacological bioprocess, it is critical to leverage breakthroughs of gut-on-a-chip technology to address knowledge gaps and drive innovations in predictive drug toxicology and metabolism.

Antibiotics affect the pharmacokinetics of n-butylphthalide in vivo by altering the intestinal microbiota

OBJECTIVE

N-butylphthalide (NBP) is a monomeric compound extracted from natural plant celery seeds, whether intestinal microbiota alteration can modify its pharmacokinetics is still unclear. The purpose of this study is to investigate the effect of intestinal microbiota alteration on the pharmacokinetics of NBP and its related mechanisms.

METHODS

After treatment with antibiotics and probiotics, plasma NBP concentrations in SD rats were determined by high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). The effect of intestinal microbiota changes on NBP pharmacokinetics was compared. Intestinal microbiota changes after NBP treatment were analyzed by 16S rRNA sequencing. Expressions of CYP3A1 mRNA and protein in the liver and small intestine tissues under different intestinal flora conditions were determined by qRT-PCR and Western Blot. KEGG analysis was used to analyze the effect of intestinal microbiota changes on metabolic pathways.

RESULTS

Compared to the control group, the values of Cmax, AUC0-8, AUC0-∞, t1/2 in the antibiotic group increased by 56.1% (P<0.001), 56.4% (P<0.001), 53.2% (P<0.001), and 24.4% (P<0.05), respectively. In contrast, the CL and Tmax values decreased by 57.1% (P<0.001) and 28.6% (P<0.05), respectively. Treatment with antibiotics could reduce the richness and diversity of the intestinal microbiota. CYP3A1 mRNA and protein expressions in the small intestine of the antibiotic group were 61.2% and 66.1% of those of the control group, respectively. CYP3A1 mRNA and protein expressions in the liver were 44.6% and 63.9% of those in the control group, respectively. There was no significant change in the probiotic group. KEGG analysis showed that multiple metabolic pathways were significantly down-regulated in the antibiotic group. Among them, the pathways of drug metabolism, bile acid biosynthesis and decomposition, and fatty acid synthesis and decomposition were related to NBP biological metabolism.

CONCLUSION

Antibiotic treatment could affect the intestinal microbiota, decrease CYP3A1 mRNA and protein expressions and increase NBP exposure in vivo by inhibiting pathways related to NBP metabolism.

Predictive metabolites for incident myocardial infarction: a two-step meta-analysis of individual patient data from six cohorts comprising 7897 individuals from the COnsortium of METabolomics Studies

AIMS

Myocardial infarction (MI) is a major cause of death and disability worldwide. Most metabolomics studies investigating metabolites predicting MI are limited by the participant number and/or the demographic diversity. We sought to identify biomarkers of incident MI in the COnsortium of METabolomics Studies.

METHODS AND RESULTS

We included 7897 individuals aged on average 66 years from six intercontinental cohorts with blood metabolomic profiling (n = 1428 metabolites, of which 168 were present in at least three cohorts with over 80% prevalence) and MI information (1373 cases). We performed a two-stage individual patient data meta-analysis. We first assessed the associations between circulating metabolites and incident MI for each cohort adjusting for traditional risk factors and then performed a fixed effect inverse variance meta-analysis to pull the results together. Finally, we conducted a pathway enrichment analysis to identify potential pathways linked to MI. On meta-analysis, 56 metabolites including 21 lipids and 17 amino acids were associated with incident MI after adjusting for multiple testing (false discovery rate < 0.05), and 10 were novel. The largest increased risk was observed for the carbohydrate mannitol/sorbitol {hazard ratio [HR] [95% confidence interval (CI)] = 1.40 [1.26-1.56], P < 0.001}, whereas the largest decrease in risk was found for glutamine [HR (95% CI) = 0.74 (0.67-0.82), P < 0.001]. Moreover, the identified metabolites were significantly enriched (corrected P < 0.05) in pathways previously linked with cardiovascular diseases, including aminoacyl-tRNA biosynthesis.

CONCLUSIONS

In the most comprehensive metabolomic study of incident MI to date, 10 novel metabolites were associated with MI. Metabolite profiles might help to identify high-risk individuals before disease onset. Further research is needed to fully understand the mechanisms of action and elaborate pathway findings.

The changes of gut microbiota and metabolites in different drug-induced liver injuries

The increasing incidence of drug-induced liver injury (DILI) has become a major concern. Gut microbiota, as another organ of the human body, has been studied in various tumors, cardiovascular metabolic diseases, inflammatory bowel disease and human immunity. The studies mentioned above have confirmed its important impact on the occurrence and development of DILI. The gut-liver axis explains the close relationship between the gut and the liver, and it may be a pathway by which gut microbes contribute to DILI. In addition, the interaction between drugs and gut microbes affects both separately, which in turn may have positive or negative effects on the body, including DILI. There are both common and specific changes in liver injury caused by different drugs. The alteration of metabolites in DILI is also a new direction of therapeutic exploration. The application of microbiomics, metabolomics and other multi-omics to DILI has also explored new ideas for DILI. In this review, we conclude the alterations of gut microbes and metabolites under different DILI, and the significance of applying gut microbiome-metabolomics to DILI, so as to explore the metabolic characteristics of DILI and possible novel metabolic biomarkers.

Dissecting the human gut microbiome to better decipher drug liability: A once-forgotten organ takes center stage

BACKGROUND

The gut microbiome is a diverse system within the gastrointestinal tract composed of trillions of microorganisms (gut microbiota), along with their genomes. Accumulated evidence has revealed the significance of the gut microbiome in human health and disease. Due to its ability to alter drug/xenobiotic pharmacokinetics and therapeutic outcomes, this once-forgotten "metabolic organ" is receiving increasing attention. In parallel with the growing microbiome-driven studies, traditional analytical techniques and technologies have also evolved, allowing researchers to gain a deeper understanding of the functional and mechanistic effects of gut microbiome.

AIM OF REVIEW

From a drug development perspective, microbial drug metabolism is becoming increasingly critical as new modalities (e.g., degradation peptides) with potential microbial metabolism implications emerge. The pharmaceutical industry thus has a pressing need to stay up-to-date with, and continue pursuing, research efforts investigating clinical impact of the gut microbiome on drug actions whilst integrating advances in analytical technology and gut microbiome models. Our review aims to practically address this need by comprehensively introducing the latest innovations in microbial drug metabolism research- including strengths and limitations, to aid in mechanistically dissecting the impact of the gut microbiome on drug metabolism and therapeutic impact, and to develop informed strategies to address microbiome-related drug liability and minimize clinical risk.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We present comprehensive mechanisms and co-contributing factors by which the gut microbiome influences drug therapeutic outcomes. We highlight in vitro, in vivo, and in silico models for elucidating the mechanistic role and clinical impact of the gut microbiome on drugs in combination with high-throughput, functionally oriented, and physiologically relevant techniques. Integrating pharmaceutical knowledge and insight, we provide practical suggestions to pharmaceutical scientists for when, why, how, and what is next in microbial studies for improved drug efficacy and safety, and ultimately, support precision medicine formulation for personalized and efficacious therapies.

Effects of intestinal microbiota on pharmacokinetics of cyclosporine a in rats

BACKGROUND

Intestinal microbiota has been confirmed to influencing the pharmacokinetic processes of a variety of oral drugs. However, the pharmacokinetic effects of the gut microbiota on cyclosporine A, a drug with a narrow therapeutic window, remain to be studied.

METHOD

Twenty-one rats were randomly divided into three groups: (a) control group (CON), (b) antibiotic treatment group (ABT) and (c) fecal microbe transplantation group (FMT). The ABT group was administrated with water containing multiple antibiotics to deplete microorganisms. FMT was with the same treatment, followed by oral administration of conventional rat fecal microorganisms for normalization.

RESULT

The bioavailability of CSA increased by 155.6% after intestinal microbes were consumed by antibiotics. After intestinal microbiota reconstruction by fecal transplantation, the increased bioavailability was significantly reduced and basically returned to the control group level. Changes in gut microbiota alter the protein expression of CYP3A1, UGT1A1 and P-gp in liver. The expressions of these three proteins in ABT group were significantly lower than those in CON and FMT groups. The relative abundance of Alloprevolleta and Oscillospiraceae UCG 005 was negatively correlated with CSA bioavailability while the relative abundance of Parasutterella and Eubacterium xylanophilum group was negatively correlated with CSA bioavailability.

CONCLUSION

Intestinal microbiota affects the pharmacokinetics of CSA by regulating the expression of CYP3A1, UGT1A1 and P-GP.

Gut microbiota and host Cyp450s co-contribute to pharmacokinetic variability in mice with non-alcoholic steatohepatitis: Effects vary from drug to drug

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Drugs’ pharmacokinetics were changed in NASH disease.

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A systematical research on cocktail drugs in NASH.

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Gut microbiota can bio-transform some drugs in vitro, and the metabolic rate was different in NASH.

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The gut microbiota and the host co-contributed the pharmacokinetic variability of drugs in NASH.

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The degree of influence on pharmacokinetic variability varies from drug to drug.

Introduction

Pharmacokinetic variability in disease state is common in clinical practice, but its underlying mechanism remains unclear. Recently, gut microbiota has been considered to be pharmacokinetically equivalent to the host liver. Although some studies have explored the roles of gut microbiota and host Cyp450s in drug pharmacokinetics, few have explored their effects on pharmacokinetic variability, especially in disease states.

Objectives

In this study, we aim to investigate the effects of gut microbiota and host Cyp450s on pharmacokinetic variability in mice with non-alcoholic steatohepatitis (NASH), and to elucidate the contribution of gut microbiota and host Cyp450s to pharmacokinetic variability in this setting.

Methods

The pharmacokinetic variability of mice with NASH was explored under intragastric and intravenous administrations of a cocktail mixture of omeprazole, phenacetin, midazolam, tolbutamide, chlorzoxazone, and metoprolol, after which the results were compared with those obtained from the control group. Thereafter, the pharmacokinetic variabilities of all drugs and their relations to the changes in gut microbiota and host Cyp450s were compared and analyzed.

Results

The exposures of all drugs, except metoprolol, significantly increased in the NASH group under intragastric administration. However, no significant increase in the exposure of all drugs, except tolbutamide, was observed in the NASH group under intravenous administration. The pharmacokinetic variabilities of phenacetin, midazolam, omeprazole, and chlorzoxazone were mainly associated with decreased elimination activity in the gut microbiota. By contrast, the pharmacokinetic variability of tolbutamide was mainly related to the change in the host Cyp2c65. Notably, gut microbiota and host Cyp450s exerted minimal effects on the pharmacokinetic variability of metoprolol.

Conclusion

Gut microbiota and host Cyp450s co-contribute to the pharmacokinetic variability in mice with NASH, and the degree of contribution varies from drug to drug. The present findings provide new insights into the explanation of pharmacokinetic variability in disease states.

Gut microbiota-drug interactions in cancer pharmacotherapies: implications for efficacy and adverse effects

INTRODUCTION

The gut microbiota is involved in host physiology and health. Reciprocal microbiota-drug interactions are increasingly recognized as underlying some individual differences in therapy response and adverse events. Cancer pharmacotherapies are characterized by a high degree of interpatient variability in efficacy and side effect profile and recently, the microbiota has emerged as a factor that may underlie these differences.

AREAS COVERED

The effects of cancer pharmacotherapy on microbiota composition and function are reviewed with consideration of the relationship between baseline microbiota composition, microbiota modification, antibiotics exposure and cancer therapy efficacy. We assess the evidence implicating the microbiota in cancer therapy-related adverse events including impaired gut function, cognition and pain perception. Finally, potential mechanisms underlying microbiota-cancer drug interactions are described, including direct microbial metabolism, and microbial modulation of liver metabolism and immune function. This review focused on preclinical and clinical studies conducted in the last 5 years.

EXPERT OPINION

Preclinical and clinical research supports a role for baseline microbiota in cancer therapy efficacy, with emerging evidence that the microbiota modification may assist in side effect management. Future efforts should focus on exploiting this knowledge towards the development of microbiota-targeted therapies. Finally, a focus on specific drug-microbiota-cancer interactions is warranted.

Effects of Antibiotic Use on Saliva Antibody Content and Oral Microbiota in Sprague Dawley Rats

Antibiotics are often used to treat systemic diseases not associated with the oral cavity. This application of antibiotics may affect the healthy oral microbiota community, as it destroys the balance between specific bacterial populations throughout the ecosystem and may lead to dysbacteriosis. We hypothesized that the effects on antibiotics on oral microbiota regulation and function would affect antibody content in saliva, depending on the antibiotic type. To address this, a total of 24 Sprague Dawley rats (divided into 4 cages, 6 per pen) were administered amoxicillin (AMX), spiramycin (SP), metronidazole (MTZ), or water (control) daily for 14 days (gavage). After treatment was completed, high-throughput sequencing of 16S rRNA genes was used to determine changes in the composition, metabolic function, and diversity of oral microbiota in the rats. Enzyme-linked immunosorbent assay was used to detect antibodies in saliva, including SIgA, IgG, and IgM. Results showed that AMX, MTZ, and SP significantly affected oral microbiota composition, diversity, and metabolic function in rats. AMX induced substantial changes in the rat salivary antibody concentrations. At the genus level, the relative abundance of Rothia and Haemophilus was higher in the AMX group than in the other groups. In conclusion, antibiotics-induced changes in oral microbiota populations may be associated with changes in salivary antibody concentrations. However, the specific interaction mechanisms remain unknown, and it is still unclear whether significant changes in the oral microbiota cause changes in salivary antibody concentrations or vice versa.

Advances in Microbiome-Derived Solutions and Methodologies Are Founding a New Era in Skin Health and Care

The microbiome, as a community of microorganisms and their structural elements, genomes, metabolites/signal molecules, has been shown to play an important role in human health, with significant beneficial applications for gut health. Skin microbiome has emerged as a new field with high potential to develop disruptive solutions to manage skin health and disease. Despite an incomplete toolbox for skin microbiome analyses, much progress has been made towards functional dissection of microbiomes and host-microbiome interactions. A standardized and robust investigation of the skin microbiome is necessary to provide accurate microbial information and set the base for a successful translation of innovations in the dermo-cosmetic field. This review provides an overview of how the landscape of skin microbiome research has evolved from method development (multi-omics/data-based analytical approaches) to the discovery and development of novel microbiome-derived ingredients. Moreover, it provides a summary of the latest findings on interactions between the microbiomes (gut and skin) and skin health/disease. Solutions derived from these two paths are used to develop novel microbiome-based ingredients or solutions acting on skin homeostasis are proposed. The most promising skin and gut-derived microbiome interventional strategies are presented, along with regulatory, safety, industrial, and technical challenges related to a successful translation of these microbiome-based concepts/technologies in the dermo-cosmetic industry.

Studies on CYP3A activity during the menstrual cycle as measured by urinary 6β-hydroxycortisol/cortisol

The 6β-OH-cortisol/cortisol ratio (6β-OHC/C) in urine is an endogenous marker of drug-metabolizing enzyme cytochrome P450 3A (CYP3A). The primary aim of this single center, prospective, non-interventional cohort study, was to investigate the variability of 6β-OHC/C during the menstrual cycle. In addition, possible associations between the CYP3A activity and sex hormones, gut microbiota metabolite trimethylamine-N-Oxide (TMAO) and microRNA-27b, respectively, were investigated. Serum and urinary samples from healthy, regularly menstruating women followed for two menstrual cycles were analyzed. Twenty-six complete menstrual cycles including follicular, ovulatory, and luteal phase were defined based on hormone analyses in serum. 6β-OHC/C were analyzed in urine and sex hormones, TMAO and miRNA-27b were analyzed in serum at the same time points. 6β-OHC/C did not vary between the follicular, ovulatory, or luteal phases. There was a difference in the relative miRNA-27b expression between the follicular and ovulatory phase (p = .03). A significant association was found between 6β-OHC/C and progesterone during the follicular (p = .005) and ovulatory (p = .01) phases (n = 26 for each phase). In addition, a significant association was found between the ratio and TMAO during the ovulatory (p = .02) and luteal (p = .002) phases. 6β-OHC/C and gut microbiota TMAO were significantly associated (p = .003) when evaluating all values, for all phases (n = 78). Interestingly, the finding of an association between 6β-OHC/C in urine and levels of TMAO in serum suggest that gut microbiota may affect CYP3A activity.

Physicochemical properties determining drug detection in skin

Chemicals, including some systemically administered xenobiotics and their biotransformations, can be detected noninvasively using skin swabs and untargeted metabolomics analysis. We sought to understand the principal drivers that determine whether a drug taken orally or systemically is likely to be observed on the epidermis by using a random forest classifier to predict which drugs would be detected on the skin. A variety of molecular descriptors describing calculated properties of drugs, such as measures of volume, electronegativity, bond energy, and electrotopology, were used to train the classifier. The mean area under the receiver operating characteristic curve was 0.71 for predicting drug detection on the epidermis, and the SHapley Additive exPlanations (SHAP) model interpretation technique was used to determine the most relevant molecular descriptors. Based on the analysis of 2561 US Food and Drug Administration (FDA)‐approved drugs, we predict that therapeutic drug classes, such as nervous system drugs, are more likely to be detected on the skin. Detecting drugs and other chemicals noninvasively on the skin using untargeted metabolomics could be a useful clinical advancement in therapeutic drug monitoring, adherence, and health status.

Chemical Proportionality within Molecular Networks

Molecular networking of non-targeted tandem mass spectrometry data connects structurally related molecules based on similar fragmentation spectra. Here, we report the Chemical Proportionality (ChemProp) contextualization of molecular networks. ChemProp scores the changes of abundance between two connected nodes over sequential data series (e.g., temporal or spatial relationships), which can be displayed as a direction within the network to prioritize potential biological and chemical transformations or proportional changes of (biosynthetically) related compounds. We tested the ChemProp workflow on a ground truth data set of a defined mixture and highlighted the utility of the tool to prioritize specific molecules within biological samples, including bacterial transformations of bile acids, human drug metabolism, and bacterial natural products biosynthesis. The ChemProp workflow is freely available through the Global Natural Products Social Molecular Networking (GNPS) environment.

Contribution of the Gut Microbiome to Drug Disposition, Pharmacokinetic and Pharmacodynamic Variability

The trillions of microbes that make up the gut microbiome are an important contributor to health and disease. With respect to xenobiotics, particularly orally administered compounds, the gut microbiome interacts directly with drugs to break them down into metabolic products. In addition, microbial products such as bile acids interact with nuclear receptors on host drug-metabolizing enzyme machinery, thus indirectly influencing drug disposition and pharmacokinetics. Gut microbes also influence drugs that undergo enterohepatic recycling by reversing host enzyme metabolic processes and increasing exposure to toxic metabolites as exemplified by the chemotherapy agent irinotecan and non-steroidal anti-inflammatory drugs. Recent data with immune checkpoint inhibitors demonstrate the impact of the gut microbiome on drug pharmacodynamics. We summarize the clinical importance of gut microbe interaction with digoxin, irinotecan, immune checkpoint inhibitors, levodopa, and non-steroidal anti-inflammatory drugs. Understanding the complex interactions of the gut microbiome with xenobiotics is challenging; and highly sensitive methods such as untargeted metabolomics with molecular networking along with other in silico methods and animal and human in vivo studies will uncover mechanisms and pathways. Incorporating the contribution of the gut microbiome to drug disposition, pharmacokinetics, and pharmacodynamics is vital in this era of precision medicine.

Recent advances in the ontogeny of drug disposition

Developmental changes that occur throughout childhood have long been known to impact drug disposition. However, pharmacokinetic studies in the paediatric population have historically been limited due to ethical concerns arising from incorporating children into clinical trials. As such, much of the early work in the field of developmental pharmacology was reliant on difficult-to-interpret in vitro and in vivo animal studies. Over the last 2 decades, our understanding of the mechanistic processes underlying age-related changes in drug disposition has advanced considerably. Progress has largely been driven by technological advances in mass spectrometry-based methods for quantifying proteins implicated in drug disposition, and in silico tools that leverage these data to predict age-related changes in pharmacokinetics. This review summarizes our current understanding of the impact of childhood development on drug disposition, particularly focusing on research of the past 20 years, but also highlighting select examples of earlier foundational research. Equally important to the studies reviewed herein are the areas that we cannot currently describe due to the lack of research evidence; these gaps provide a map of drug disposition pathways for which developmental trends still need to be characterized.