Gut Microbiota-driven Drug Metabolism in Inflammatory Bowel Disease

Crouwel, Femke; Buiter, Hans J C; Boer, Nanne K de

doi:10.1093/ecco-jcc/jjaa143

Cited by 47 publications

(30 citation statements)

References 71 publications

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“…The effect of some of the above-mentioned drugs on gut microbiota are summarized in Table 2 . Interestingly some drugs that are used for treatment of IBD requires metabolic activation via the gut microbiota e.g., sulfasalazine, balsalazide (mesalamine prodrug), olsalazine, and methotrexates ( Crouwel et al, 2020 ).…”

Section: Effect Of Ibd Medications On Gut Microbiotamentioning

confidence: 99%

Metabolic Influences of Gut Microbiota Dysbiosis on Inflammatory Bowel Disease

et al. 2021

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Inflammatory bowel diseases (IBD) are chronic medical disorders characterized by recurrent gastrointestinal inflammation. While the etiology of IBD is still unknown, the pathogenesis of the disease results from perturbations in both gut microbiota and the host immune system. Gut microbiota dysbiosis in IBD is characterized by depleted diversity, reduced abundance of short chain fatty acids (SCFAs) producers and enriched proinflammatory microbes such as adherent/invasive E. coli and H2S producers. This dysbiosis may contribute to the inflammation through affecting either the immune system or a metabolic pathway. The immune responses to gut microbiota in IBD are extensively discussed. In this review, we highlight the main metabolic pathways that regulate the host-microbiota interaction. We also discuss the reported findings indicating that the microbial dysbiosis during IBD has a potential metabolic impact on colonocytes and this may underlie the disease progression. Moreover, we present the host metabolic defectiveness that adds to the impact of symbiont dysbiosis on the disease progression. This will raise the possibility that gut microbiota dysbiosis associated with IBD results in functional perturbations of host-microbiota interactions, and consequently modulates the disease development. Finally, we shed light on the possible therapeutic approaches of IBD through targeting gut microbiome.

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Section: Effect Of Ibd Medications On Gut Microbiotamentioning

confidence: 99%

Metabolic Influences of Gut Microbiota Dysbiosis on Inflammatory Bowel Disease

et al. 2021

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“…As an autoimmune disease closely related to intestinal microbes, the occurrence of inflammatory bowel disease (IBD) was often accompanied by gut microbiome dysbiosis. A study suggested that the human intestinal microbiome had an important influence on the drug metabolism and efficacy of IBD ( 25 ). Currently, there are limited studies on the association between the candidate intestinal bacteria found in this study and complex traits.…”

Section: Discussionmentioning

confidence: 99%

Causal Effects of Gut Microbiome on Systemic Lupus Erythematosus: A Two-Sample Mendelian Randomization Study

et al. 2021

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The observational association between gut microbiome and systemic lupus erythematosus (SLE) has been well documented. However, whether the association is causal remains unclear. The present study used publicly available genome-wide association study (GWAS) summary data to perform two-sample Mendelian randomization (MR), aiming to examine the causal links between gut microbiome and SLE. Two sets of MR analyses were conducted. A group of single nucleotide polymorphisms (SNPs) that less than the genome-wide statistical significance threshold (5 × 10-8) served as instrumental variables. To obtain a comprehensive conclusion, the other group where SNPs were smaller than the locus-wide significance level (1 × 10-5) were selected as instrumental variables. Based on the locus-wide significance level, the results indicated that there were causal effects of gut microbiome components on SLE risk. The inverse variance weighted (IVW) method suggested that Bacilli and Lactobacillales were positively correlated with the risk of SLE and Bacillales, Coprobacter and Lachnospira were negatively correlated with SLE risk. The results of weighted median method supported that Bacilli, Lactobacillales, and Eggerthella were risk factors for SLE and Bacillales and Coprobacter served as protective factors for SLE. The estimates of MR Egger suggested that genetically predicted Ruminiclostridium6 was negatively associated with SLE. Based on the genome-wide statistical significance threshold, the results showed that Actinobacteria might reduce the SLE risk. However, Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) detected significant horizontal pleiotropy between the instrumental variables of Ruminiclostridium6 and outcome. This study support that there are beneficial or detrimental causal effects of gut microbiome components on SLE risk.

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“…133 Over 180 orally administered drugs are now known to be metabolized by gut microbiota. 10,11,33,134,135 Microbiota drug metabolism can lead to significant inter-individual pharmacokinetic variability. 136 For example, a strain of Eggerthella lenta capable of inactivating the cardiac glycoside drug digoxin is estimated to inhabit the guts of 40% of the global population.…”

Section: Prediction Of Microbiome-drug Interactionsmentioning

confidence: 99%

Harnessing machine learning for development of microbiome therapeutics

et al. 2021

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The last twenty years of seminal microbiome research has uncovered microbiota's intrinsic relationship with human health. Studies elucidating the relationship between an unbalanced microbiome and disease are currently published daily. As such, microbiome big data have become a reality that provide a mine of information for the development of new therapeutics. Machine learning (ML), a branch of artificial intelligence, offers powerful techniques for big data analysis and predictionmaking, that are out of reach of human intellect alone. This review will explore how ML can be applied for the development of microbiome-targeted therapeutics. A background on ML will be given, followed by a guide on where to find reliable microbiome big data. Existing applications and opportunities will be discussed, including the use of ML to discover, design, and characterize microbiome therapeutics. The use of ML to optimize advanced processes, such as 3D printing and in silico prediction of drug-microbiome interactions, will also be highlighted. Finally, barriers to adoption of ML in academic and industrial settings will be examined, concluded by a future outlook for the field.

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Gut Microbiota-driven Drug Metabolism in Inflammatory Bowel Disease

Cited by 47 publications

References 71 publications

Metabolic Influences of Gut Microbiota Dysbiosis on Inflammatory Bowel Disease

Metabolic Influences of Gut Microbiota Dysbiosis on Inflammatory Bowel Disease

Causal Effects of Gut Microbiome on Systemic Lupus Erythematosus: A Two-Sample Mendelian Randomization Study

Harnessing machine learning for development of microbiome therapeutics

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