Literature DB >> 32772362

Circulating miRNAs as Biomarkers for CYP2B6 Enzyme Activity.

Joseph Ipe1, Rudong Li2, Ingrid F Metzger1,2, Jessica Bo Li Lu1,2, Brandon T Gufford1, Zeruesenay Desta1,2, Yunlong Liu2, Todd C Skaar1,2.   

Abstract

The CYP2B6 gene is highly polymorphic and its activity shows wide interindividual variability. However, substantial variability in CYP2B6 activity remains unexplained by the known CYP2B6 genetic variations. Circulating, cell-free micro RNAs (miRNAs) may serve as biomarkers of hepatic enzyme activity. CYP2B6 activity in 72 healthy volunteers was determined using the disposition of efavirenz as a probe drug. Circulating miRNA expression was quantified from baseline plasma samples. A linear model consisting of the effects of miRNA expression, genotype-determined metabolizer status, and demographic information was developed to predict CYP2B6 activity. Expression of 2,510 miRNAs were quantified out of which 7 miRNAs, together with the CYP2B6-genotypic metabolizer status and demographics, was shown to be predictive markers for CYP2B6 activity. The reproducibility of the model was evaluated by cross-validation. The average Pearson's correlation (R) between the predicted and observed maximum plasma concentration (Cmax ) ratios of efavirenz and its metabolite-8-OH efavirenz using the linear model with all features (7 miRNA + metabolizer status + age + sex + race) was 0.6702. Similar results were also observed using area under the curve (AUC) ratios (Pearson correlation's R = 0.6035). Thus, at least 36% (R2 ) of the variability of in vivo CYP2B6 activity was explained using this model. This is a significant improvement over the models using only the genotype-based metabolizer status or the demographic information, which explained only 6% or less of the variability of in vivo CYP2B6 activity. Our results, therefore, demonstrate that circulating plasma miRNAs can be valuable biomarkers for in vivo CYP2B6 activity.
© 2020 The Authors Clinical Pharmacology & Therapeutics © 2020 American Society for Clinical Pharmacology and Therapeutics.

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Year:  2020        PMID: 32772362      PMCID: PMC9007394          DOI: 10.1002/cpt.2018

Source DB:  PubMed          Journal:  Clin Pharmacol Ther        ISSN: 0009-9236            Impact factor:   6.875


  29 in total

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3.  Population Pharmacokinetic Modeling To Estimate the Contributions of Genetic and Nongenetic Factors to Efavirenz Disposition.

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Review 7.  Efavirenz.

Authors:  J C Adkins; S Noble
Journal:  Drugs       Date:  1998-12       Impact factor: 9.546

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Journal:  PLoS One       Date:  2013-09-13       Impact factor: 3.240

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