Circulating miRNAs as Biomarkers for CYP2B6 Enzyme Activity

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.

Novel Approaches to Characterize Individual Drug Metabolism and Advance Precision Medicine

“…A recent study by Zastrozhin et al found a significant negative correlation between CYP3A activity measured by urinary 6 β -hydroxycortisol/cortisol ratio and the plasma concentration of miR-27b in patients taking the CYP3A substrate alprazolam ( Zastrozhin et al, 2020 ). Another study developed a linear regression model to predict CYP2B6 activity, as measured by metabolism of the probe substrate efavirenz, in 72 healthy volunteers ( Ipe et al, 2021 ). Researchers found that the addition of seven circulating miRNAs (miR-204–5p, miR-212–3p, miR-3649, miR-3941, miR-4254, miR-4442, and miR-6867– 5p) to a linear regression model accounting for CYP2B6 genotype-determined metabolizer status, age, sex, and race increased the predictive power of their model from approximately 8% to 45% ( Ipe et al, 2021 ).…”

“…Another study developed a linear regression model to predict CYP2B6 activity, as measured by metabolism of the probe substrate efavirenz, in 72 healthy volunteers ( Ipe et al, 2021 ). Researchers found that the addition of seven circulating miRNAs (miR-204–5p, miR-212–3p, miR-3649, miR-3941, miR-4254, miR-4442, and miR-6867– 5p) to a linear regression model accounting for CYP2B6 genotype-determined metabolizer status, age, sex, and race increased the predictive power of their model from approximately 8% to 45% ( Ipe et al, 2021 ). Expression of phase II enzymes, including sulfotransferases (SULTs) and UDP-glucuronosyltransferases (UGTs), are also modulated by ncRNAs ( Yu et al, 2010 ; Li et al, 2020 ; Hu et al, 2022 ).…”

Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities

“…By using the multivariate adaptive regression splines ML method, Hall et al were able to identify the influence of weight and/or age on the PK of dapsone, not identifiable with standard population PK approaches as occurring only between certain ranges of patients' covariates, delimited by multiple regions of discontinuity (66). In another example, a reverse translational analysis using supervised-feature selection identified a circulating 7-miRNA signature as a predictor of population variability in CYP2B6 activity based on screening of expression of 2510 miRNAs in a multi-dimensional covariate analysis of efavirenz PK (67). The incorporation of big data and ML into the PMX pipeline to enhance the structural, population, and covariate modeling processes (Fig.…”