An Exposure-Response Modeling Approach to Examine the Relationship Between Potency of CYP3A Inducer and Plasma 4β-Hydroxycholesterol in Healthy Subjects

Novel Approaches to Characterize Individual Drug Metabolism and Advance Precision Medicine

Interindividual variability in drug metabolism can significantly affect drug concentrations in the body and subsequent drug response. Understanding an individual's drug metabolism capacity is important for predicting drug exposure and developing precision medicine strategies. The goal of precision medicine is to individualize drug treatment for patients to maximize efficacy and minimize drug toxicity. While advances in pharmacogenomics have improved our understanding of how genetic variations in drug-metabolizing enzymes (DMEs) affect drug response, nongenetic factors are also known to influence drug metabolism phenotypes. This minireview discusses approaches beyond pharmacogenetic testing to phenotype DMEs-particularly the cytochrome P450 enzymes-in clinical settings. Several phenotyping approaches have been proposed: traditional approaches include phenotyping with exogenous probe substrates and the use of endogenous biomarkers; newer approaches include evaluating circulating noncoding RNAs and liquid biopsy-derived markers relevant to DME expression and function. The goals of this minireview are to 1) provide a high-level overview of traditional and novel approaches to phenotype individual drug metabolism capacity, 2) describe how these approaches are being applied or can be applied to pharmacokinetic studies, and 3) discuss perspectives on future opportunities to advance precision medicine in diverse populations. SIGNIFICANCE STATEMENT: This minireview provides an overview of recent advances in approaches to characterize individual drug metabolism phenotypes in clinical settings. It highlights the integration of existing pharmacokinetic biomarkers with novel approaches; also discussed are current challenges and existing knowledge gaps. The article concludes with perspectives on the future deployment of a liquid biopsy-informed physiologically based pharmacokinetic strategy for patient characterization and precision dosing.

Mathematical Models in the Description of Pregnane X Receptor (PXR)-Regulated Cytochrome P450 Enzyme Induction

The pregnane X receptor (PXR) is a drug/xenobiotic-activated transcription factor of crucial importance for major cytochrome P450 xenobiotic-metabolizing enzymes (CYP) expression and regulation in the liver and the intestine. One of the major target genes regulated by PXR is the cytochrome P450 enzyme (CYP3A4), which is the most important human drug-metabolizing enzyme. In addition, PXR is supposed to be involved both in basal and/or inducible expression of many other CYPs, such as CYP2B6, CYP2C8, 2C9 and 2C19, CYP3A5, CYP3A7, and CYP2A6. Interestingly, the dynamics of PXR-mediated target genes regulation has not been systematically studied and we have only a few mechanistic mathematical and biologically based models describing gene expression dynamics after PXR activation in cellular models. Furthermore, few indirect mathematical PKPD models for prediction of CYP3A metabolic activity in vivo have been built based on compartmental models with respect to drug–drug interactions or hormonal crosstalk. Importantly, several negative feedback loops have been described in PXR regulation. Although current mathematical models propose these adaptive mechanisms, a comprehensive mathematical model based on sufficient experimental data is still missing. In the current review, we summarize and compare these models and address some issues that should be considered for the improvement of PXR-mediated gene regulation modelling as well as for our better understanding of the quantitative and spatial dynamics of CYPs expression.

Impact of genetic and nongenetic factors on interindividual variability in 4β-hydroxycholesterol concentration

PURPOSE

Individual variability in the endogenous CYP3A metabolite 4β-hydroxycholesterol (4βOHC) is substantial, but to which extent this is determined by genetic and nongenetic factors remains unclear. The aim of the study was to evaluate the explanatory power of candidate genetic variants and key nongenetic factors on individual variability in 4βOHC levels in a large naturalistic patient population.

METHODS

We measured 4βOHC concentration in serum samples from 655 patients and used multiple linear regression analysis to estimate the quantitative effects of CYP3A4*22, CYP3A5*3, and POR*28 variant alleles, comedication with CYP3A inducers, inhibitors and substrates, sex, and age on individual 4βOHC levels.

RESULTS

4βOHC concentration ranged >100-fold in the population, and the multiple linear regression model explained about one fourth of the variability (R ² = 0.23). Only comedication with inducers or inhibitors, sex, and POR genotype were significantly associated with individual variability in 4βOHC level. The estimated quantitative effects on 4βOHC levels were greatest for inducer comedication (+>313%, P < 0.001), inhibitor comedication (-34%, P = 0.021), and female sex (+30%, P < 0.001), while only a modestly elevated 4βOHC level was observed in carriers vs. noncarriers of POR*28 (+11%, P = 0.023).

CONCLUSIONS

These findings suggest that the CYP3A4*22, CYP3A5*3, and POR*28 variant alleles are of limited importance for overall individual variability in 4βOHC levels compared to nongenetic factors.