An Exposure‐Response Modeling Approach to Examine the Relationship Between Potency of CYP3A Inducer and Plasma 4<b>β</b>‐Hydroxycholesterol in Healthy Subjects

Jiang, Xiaorui; Dutreix, Catherine; Jarugula, Venkateswar; Rebello, Sam; Won, Christina S.; Sun, Haiying

doi:10.1002/cpdd.267

Cited by 5 publications

(5 citation statements)

References 29 publications

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“…With the high performance of modern computer systems, such a process often has many advantages over laboratory experiments; it can also complement them or help us in the proper dosing of experiments based on pilot mathematical predictions. An example of a model employing such techniques for the simulation of the dynamic range of 4βHC as a biomarker for CYP3A4 induction and inhibition is presented in the report by Leil et al [ 85 ] and in the paper by Jiang et al [ 84 ].…”

Section: Discussionmentioning

confidence: 99%

“…In addition to mechanistic and biologically-based models, indirect response models and PK/PD modelling have been used to describe the effect of ligands of some nuclear receptors. Recently, population PK-PD models have been used to describe the exposure-response relationship between plasma rifampicin and 4β-hydroxycholesterol, an endogenous product of CYP3A [ 84 , 85 ]. Similarly, an indirect PK/PD response model has been built for the peroxisome proliferator-activated receptor gamma (PPARγ) agonist pioglitazone in vivo [ 86 ].…”

Section: Mathematical Models Of Pxr Activation and Pxr-induced Genmentioning

confidence: 99%

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

Tebbens

Azar

Friedmann

et al. 2018

IJMS

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Mathematical Models Of Pxr Activation and Pxr-induced Genmentioning

confidence: 99%

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

Tebbens

Azar

Friedmann

et al. 2018

IJMS

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“…Acknowledging the limited number of data points and use of mean data without interindividual variability, this composite (E max ‐I max ) model appears to form the basis for a reasonable induction potency‐biomarker correlation. This relationship has been noted by others and has been characterized using various modeling approaches, including Bayesian mechanism–based pharmacokinetic/pharmacodynamic (PK/PD) modeling and a population PK/PD model . For example, Jiang et al built a population PK/PD model using rifampicin concentrations (PK) and biomarker (PD) after 14 days of rifampicin dosing data and utilized it for predicting the increases in plasma 4βHC under the conditions of weak, moderate, and strong induction by using 10‐, 20‐, and 100‐mg rifampicin doses.…”

Section: Inducer Classification and Corresponding Model‐predicted 4βhmentioning

confidence: 99%

“…This relationship has been noted by others and has been characterized using various modeling approaches, including Bayesian mechanism–based pharmacokinetic/pharmacodynamic (PK/PD) modeling and a population PK/PD model . For example, Jiang et al built a population PK/PD model using rifampicin concentrations (PK) and biomarker (PD) after 14 days of rifampicin dosing data and utilized it for predicting the increases in plasma 4βHC under the conditions of weak, moderate, and strong induction by using 10‐, 20‐, and 100‐mg rifampicin doses. On the other hand, Leil et al developed a Bayesian mechanism–based PK/PD model based on a clinical study of the effects of ketoconazole and rifampicin on midazolam exposure and 4βHC concentrations.…”

Section: Inducer Classification and Corresponding Model‐predicted 4βhmentioning

confidence: 99%

Compelling Relationship of CYP3A Induction to Levels of the Putative Biomarker 4β‐Hydroxycholesterol and Changes in Midazolam Exposure

Mangold¹,

Wu²,

Rebello³

2016

Clinical Pharm in Drug Dev

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Having reliable clinical CYP3A biomarkers is attractive to those engaged in drug development as well as to academic groups studying clinical variability resulting from differences or changes in basal individual metabolic status (eg, due to disease or genetics) or drug-drug interactions (DDI). Moreover, if biomarkers can be adequately qualified, it raises the possibility of their use to better anticipate the need, or lack thereof, for defined clinical studies to establish DDI potential. But qualification requires a comprehensive body of evidence that currently appears not to exist in the published literature.We have been following with interest the many reports on the use of plasma 4β-hydroxycholesterol (4βHC) as a potential clinical biomarker of cytochrome P450 3A (CYP3A) enzyme activity in humans. Although 4βHC has been explored for both inhibition and induction of CYP3A, we have been interested in studies of CYP3A induction. These have included results from our own studies 1 as well as several other industry and academic groups.2-4 CYP3A is a major enzyme involved in the metabolic clearance of many drug substances. It has become apparent due to factors described below that the 4βHC biomarker is most amenable to the investigation of increases in CYP3A activity, for example, as a result of enzyme induction or the reversal of repression (ie, by removal of mechanisms that may reduce CYP3A expression such as inflammation).A critical factor in 4βHC use is an understanding of how consistently 4βHC plasma levels change in response to weak, moderate, and strong CYP3A inducers.3 In this regard, results from 6 recent studies suggest an emerging trend. [1][2][3][4][5][6] In drug development, establishing DDI potential for a drug coadministered with CYP3A enzyme inducers commonly involves determining drug exposure before and after dosing with a known strong inducer such as rifampicin.Accordingly a typical clinical DDI study would use the inducer rifampicin (RIF, 600 mg, repeated daily dosing for 14 days) to establish a maximum induction response. The maximum fold change in 4βHC levels can be established under the same conditions, before and after induction.1,3 But understanding the effects of moderate or weak CYP3A induction is also needed. Such information exists in reports of changes in 4βHC plasma concentrations in response to RIF daily doses of 10, 20, 100, and 500 mg (14 days), with these lesser doses of RIF as surrogates for low to moderate inducers.2,4 Together, using available published data, 1-6 these 4βHC changes allow one to establish an empirical relationship of RIF dose to 4βHC (left Y-axis) as shown in Figure 1. This relationship seems well described using a simple E max model (eg, E = 1 + E max × Dose/[ED50 + Dose], with E max = 3.55 [fold increase over baseline] and ED50 = 127 mg, r 2 = 0.90; E max is the maximum effect, and ED50 is the dose producing 50% of this maximum effect).An additional benchmark of the consequence of CYP3A induction (or inhibition) in the clinic is the corresponding change produced in...

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“…This could be due to the high levels of interindividual and intraindividual variability in tacrolimus exposure in the first days after transplant, which researchers believe could be independent of CYP3A activity ( Vanhove et al, 2017 ). However, other groups have found evidence that a new drug candidate can be classified for CYP3A4 induction based on 4 β -hydroxycholesterol level increases from baseline ( Jiang et al, 2017 ). For example, 4 β -hydroxycholesterol was used as a marker of CYP3A induction during treatment with enasidenib based on a PK/PD model for CYP3A induction.…”

Section: Endogenous Biomarkersmentioning

confidence: 99%

Novel Approaches to Characterize Individual Drug Metabolism and Advance Precision Medicine

et al. 2023

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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.

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

Cited by 5 publications

References 29 publications

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

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

Compelling Relationship of CYP3A Induction to Levels of the Putative Biomarker 4β‐Hydroxycholesterol and Changes in Midazolam Exposure

Novel Approaches to Characterize Individual Drug Metabolism and Advance Precision Medicine

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