Quantitative Investigation of Irinotecan Metabolism, Transport, and Gut Microbiome Activation

Parvez, Masud; Basit, Abdul; Jariwala, Parth B.; Gáborik, Zsuzsanna; Kis, Emese; Heyward, Scott; Redinbo, Matthew R.; Prasad, Bhagwat

doi:10.1124/dmd.121.000476

Cited by 42 publications

(36 citation statements)

References 57 publications

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“…The peculiar richness of some of these species, as detected in our dataset for E. coli , likely modifies the clearance of the drug, facilitating reuptake and recirculation. A similar pattern of catalytic activity has also been assessed for SN-38, with L1 GUS reactivating this toxic metabolite of the anticancer drug irinotecan and causing consequent gastrointestinal toxicity ( Jariwala et al, 2020 ; Parvez et al, 2021 ).…”

Section: Discussionmentioning

confidence: 83%

“…L1 GUS process estrone and estradiol glucuronides, affecting the estrogen profile and promoting the onset of hormonal disorders ( Baker et al, 2017 ; Ervin et al, 2019 ). L1 GUS also participate in the toxicity of irinotecan since they are key effectors of metabolite SN-38 reactivation and have been targeted by specific inhibitors to protect the intestinal epithelial cells and to reduce chemotherapy-induced diarrhea ( Bhatt et al, 2020 ; Jariwala et al, 2020 ; Parvez et al, 2021 ; Wang et al, 2021 ). Moreover, L1 GUS are responsible for the deconjugation of glucuronidated non-steroidal anti-inflammatory drugs (NSAIDs), which is among the most commonly used medications worldwide ( Maseda and Ricciotti, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

“…GUS were first identified in 1934 in Escherichia coli and other Enterobacteriaceae (Masamune, 1934;Oshima, 1934), but later, they have been detected in several bacterial taxa belonging to all the main phyla within the gut microbiota: Bacteroidetes, Firmicutes, Proteobacteria, and Actinobacteria (McBain and Macfarlane, 1998;Russell and Klaenhammer, 2001;Nakamura et al, 2002;Gloux et al, 2011). Nowadays, it is known that intestinal bacteria encode different GUS types with structural differences affecting function, biocatalytic properties, and substrate specificity (Biernat et al, 2019;Parvez et al, 2021). The driving force for such evolution and diversification of bacterial GUS has been the availability of dietary and endogenous glucuronides to the commensal microbiota (Pellock and Redinbo, 2017).…”

Section: Introductionmentioning

confidence: 99%

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β-Glucuronidase Pattern Predicted From Gut Metagenomes Indicates Potentially Diversified Pharmacomicrobiomics

et al. 2022

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β-glucuronidases (GUS) of intestinal bacteria remove glucuronic acid from glucoronides, reversing phase II metabolism of the liver and affecting the level of active deconjugated metabolites deriving from drugs or xenobiotics. Two hundred seventy-nine non-redundant GUS sequences are known in the gut microbiota, classified in seven structural categories (NL, L1, L2, mL1, mL2, mL1,2, and NC) with different biocatalytic properties. In the present study, the intestinal metagenome of 60 healthy subjects from five geographically different cohorts was assembled, binned, and mined to determine qualitative and quantitative differences in GUS profile, potentially affecting response to drugs and xenobiotics. Each metagenome harbored 4–70 different GUS, altogether accounting for 218. The amount of intestinal bacteria with at least one GUS gene was highly variable, from 0.7 to 82.2%, 25.7% on average. No significant difference among cohorts could be identified, except for the Ethiopia (ETH) cohort where GUS-encoding bacteria were significantly less abundant. The structural categories were differently distributed among the metagenomes, but without any statistical significance related to the cohorts. GUS profiles were generally dominated by the category NL, followed by mL1, L2, and L1. The GUS categories most involved in the hydrolysis of small molecules, including drugs, are L1 and mL1. Bacteria contributing to these categories belonged to Bacteroides ovatus, Bacteroides dorei, Bacteroides fragilis, Escherichia coli, Eubacterium eligens, Faecalibacterium prausnitzii, Parabacteroides merdae, and Ruminococcus gnavus. Bacteria harboring L1 GUS were generally scarcely abundant (<1.3%), except in three metagenomes, where they reached up to 24.3% for the contribution of E. coli and F. prausnitzii. Bacteria harboring mL1 GUS were significantly more abundant (mean = 4.6%), with Bacteroides representing a major contributor. Albeit mL1 enzymes are less active than L1 ones, Bacteroides likely plays a pivotal role in the deglucuronidation, due to its remarkable abundance in the microbiomes. The observed broad interindividual heterogeneity of GUS profiles, particularly of the L1 and mL1 categories, likely represent a major driver of pharmacomicrobiomics variability, affecting drug response and toxicity. Different geographical origins, genetic, nutritional, and lifestyle features of the hosts seemed not to be relevant in the definition of glucuronidase activity, albeit they influenced the richness of the GUS profile.

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Section: Discussionmentioning

confidence: 83%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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β-Glucuronidase Pattern Predicted From Gut Metagenomes Indicates Potentially Diversified Pharmacomicrobiomics

et al. 2022

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“…For example, the importance of CYP3A4 and other host metabolic enzymes in irinotecan metabolism is highlighted by the signi cant correlation between irinotecan and the clearance of CYP3A probe drug midazolam [28] . Nonetheless, recent studies have discovered that gut microbiota play a increasingly signi cant role in irinotecan metabolism and the toxic side effects of irinotecan diarrhea [29,30] . In order to roundly understand the role of the gut microbiota in the eld of metabolism and to compare and analyze the functions of the gut microbiota in the PGF model, the function of the gut ora in all groups were predicted by annotating the KEGG metabolic pathway of sample bacteria.…”

Section: Discussionmentioning

confidence: 99%

Gut Microbiota and Host CYP450s Characteristics in the Pseudo Germ-free Model: Co-shaping Individual Metabolic Landscapes

Wang

Wen

Yan

et al. 2022

Preprint

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Background Pseudo germ-free (PGF) model has been widely used to research the role of intestinal microbiota in drug metabolism and efficacy, while the modeling methods and the utilization of PGF model are still not standardized and unified. A comprehensive and systematic research of PGF model on the composition and function of intestinal microbiota, the changes of CYP450s enzyme expression in host and intestinal mucosal permeability in 4 different modeling cycles of the PGF groups were provided in this paper. Results 16S rRNA sequencing was employed to compare and analyze the alpha and beta diversity, species composition, indicator species and predicted function of gut microbiota in control and PGF groups. The results showed that bacterial species richness and diversity decreased significantly in the PGF group from the first week of PGF model establishment with the antibiotic cocktail. PGF group at the fourth week of modeling possessed the least indicator genera. Moreover, the increase of intestinal mucosal permeability occurred in the second week of PGF model establishment, indicating that 1 week was appropriate time for PGF modeling with antibiotic treatment. The results of western blot displayed that the expression level of CYP1A2, CYP2C19 and CYP2E1 in PGF group was significantly upregulated compared with the control group,, implying that the metabolic clearance of related drugs will change accordingly. The abundance of functional pathways predicted in gut microbiota changed dramatically between the control group and the PGF groups. Conclusions These results manifested the microbial profile and the expression characteristic of CYP450s enzymes and provides model reference for the study on individual drug metabolism differences co-affected by gut microbiota and host CYP450s enzymes.

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“…The relative expression factor (REF) approach (Equation 1) has been used for IVIVE of drug metabolism (Parvez et al, 2021) and transport (Harwood et al, 2016;Kumar et al, 2020) data from recombinant systems to human tissues. In general, relative quantification is sufficient for estimating REF values by quantifying a target protein in a recombinant system versus human tissues as long as the data are generated in a single laboratory using an optimized surrogate peptide and digestion protocol.…”

Section: Introductionmentioning

confidence: 99%

Quantification of Accurate Composition and Total Abundance of Homologous Proteins by Conserved-Plus-Surrogate Peptide Approach: Quantification of UDP Glucuronosyltransferases in Human Tissues

et al. 2022

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Characterization of accurate compositions and total abundance of homologous drugmetabolizing enzymes, such as UDP glucuronosyltransferases (UGTs), is important for predicting the fractional contribution of individual isoforms involved in the metabolism of a drug for applications in physiologically based pharmacokinetic (PBPK) modeling. Conventional targeted proteomics utilizes surrogate peptides, which often results in high technical and interlaboratory variability due to peptide-specific digestion leading to data inconsistencies. To address this problem, we developed a novel universal conserved-plus-surrogate peptide (CPSP) approach for determining the accurate compositions and total or cumulative abundance of homologous UGTs in commercially available pooled human liver microsomes (HLM), human intestinal microsomes (HIM), human kidney microsomes (HKM), and human liver S9 (HLS9) fractions. The relative percent composition of UGT1A and UGT2B isoforms in the human liver was 35:

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Quantitative Investigation of Irinotecan Metabolism, Transport, and Gut Microbiome Activation

Cited by 42 publications

References 57 publications

β-Glucuronidase Pattern Predicted From Gut Metagenomes Indicates Potentially Diversified Pharmacomicrobiomics

β-Glucuronidase Pattern Predicted From Gut Metagenomes Indicates Potentially Diversified Pharmacomicrobiomics

Gut Microbiota and Host CYP450s Characteristics in the Pseudo Germ-free Model: Co-shaping Individual Metabolic Landscapes

Quantification of Accurate Composition and Total Abundance of Homologous Proteins by Conserved-Plus-Surrogate Peptide Approach: Quantification of UDP Glucuronosyltransferases in Human Tissues

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