Drug response in association with pharmacogenomics and pharmacomicrobiomics: towards a better personalized medicine

Hassan, Radia; Allali, Imane; Agamah, Francis E.; Elsheikh, Samar S. M.; Thomford, Nicholas Ekow; Dandara, Collet; Chimusa, Emile R.

doi:10.1093/bib/bbaa292

Cited by 22 publications

(19 citation statements)

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“…Pharmacomicrobiomics investigates the interplay of microbiome diversity and drug disposition and response and may provide an important basis in personalized medicine ( Doestzada et al, 2018 ; Hassan et al, 2021 ). The role of bacterial GUS on drug bioavailability and biological effects (i.e., the reactivation and absorption vs the excretion) encouraged our deep analysis of interindividual variability of the intestinal GUS, originating from different microbiome compositions.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, it encodes a broad diversity of enzymes capable of processing foreign compounds (e.g., phytochemicals, environmental pollutants, pharmaceuticals, and other xenobiotics) and their endogenous metabolites, adding significant chemical diversity and modifying lifetimes, bioavailability, and biological activity ( Rossi et al, 2013 ; Koppel et al, 2017 ). In this context, pharmacomicrobiomics is an emerging field focusing on the interplay of microbiome and drug metabolism and response ( Doestzada et al, 2018 ; Hassan et al, 2021 ).…”

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: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…CYP3A1, CYP3A2, and UGT1A1 are the main metabolic enzymes of CSA in liver ( Dupuis et al, 2012 ; Yu et al, 2016 ; Hassan et al, 2021 ). Our results showed that neither of the two microbiome-targeted interventions altered CYP3A2 protein expression, but significantly altered the protein expression of the other two metabolic enzymes in liver.…”

Section: Discussionmentioning

confidence: 99%

Effects of intestinal microbiota on pharmacokinetics of cyclosporine a in rats

et al. 2022

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BackgroundIntestinal 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.MethodTwenty-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.ResultThe 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.ConclusionIntestinal microbiota affects the pharmacokinetics of CSA by regulating the expression of CYP3A1, UGT1A1 and P-GP.

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“…Additional factors affect patient response to medications such as age, sex, disease stage, and environment. However, genetic makeup, [ 1 , 2 , 3 ] is a critical factor, as it directly determines the likelihood to respond to a targeted therapy or how drugs are metabolized [ 2 , 4 , 5 ]. Pharmacogenomics, therefore, forms an integral part of precision medicine since it combines both pharmacology and genomics to determine how differences in the human genome, especially the ones located at pharmacogenes, affect drug metabolism and efficacy [ 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Human Cytochrome P450 1, 2, 3 Families as Pharmacogenes with Emphases on Their Antimalarial and Antituberculosis Drugs and Prevalent African Alleles

Chamboko

Veldman

Tata

et al. 2023

IJMS

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Precision medicine gives individuals tailored medical treatment, with the genotype determining the therapeutic strategy, the appropriate dosage, and the likelihood of benefit or toxicity. Cytochrome P450 (CYP) enzyme families 1, 2, and 3 play a pivotal role in eliminating most drugs. Factors that affect CYP function and expression have a major impact on treatment outcomes. Therefore, polymorphisms of these enzymes result in alleles with diverse enzymatic activity and drug metabolism phenotypes. Africa has the highest CYP genetic diversity and also the highest burden of malaria and tuberculosis, and this review presents current general information on CYP enzymes together with variation data concerning antimalarial and antituberculosis drugs, while focusing on the first three CYP families. Afrocentric alleles such as CYP2A6*17, CYP2A6*23, CYP2A6*25, CYP2A6*28, CYP2B6*6, CYP2B6*18, CYP2C8*2, CYP2C9*5, CYP2C9*8, CYP2C9*9, CYP2C19*9, CYP2C19*13, CYP2C19*15, CYP2D6*2, CYP2D6*17, CYP2D6*29, and CYP3A4*15 are implicated in diverse metabolic phenotypes of different antimalarials such as artesunate, mefloquine, quinine, primaquine, and chloroquine. Moreover, CYP3A4, CYP1A1, CYP2C8, CYP2C18, CYP2C19, CYP2J2, and CYP1B1 are implicated in the metabolism of some second-line antituberculosis drugs such as bedaquiline and linezolid. Drug–drug interactions, induction/inhibition, and enzyme polymorphisms that influence the metabolism of antituberculosis, antimalarial, and other drugs, are explored. Moreover, a mapping of Afrocentric missense mutations to CYP structures and a documentation of their known effects provided structural insights, as understanding the mechanism of action of these enzymes and how the different alleles influence enzyme function is invaluable to the advancement of precision medicine.

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Drug response in association with pharmacogenomics and pharmacomicrobiomics: towards a better personalized medicine

Cited by 22 publications

References 159 publications

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

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

Effects of intestinal microbiota on pharmacokinetics of cyclosporine a in rats

Human Cytochrome P450 1, 2, 3 Families as Pharmacogenes with Emphases on Their Antimalarial and Antituberculosis Drugs and Prevalent African Alleles

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