Prediction of In Vivo Clearance and Associated Variability of CYP2C19 Substrates by Genotypes in Populations Utilizing a Pharmacogenetics-Based Mechanistic Model

Abstract

It is important to examine the cytochrome P450 2C19 (CYP2C19) genetic contribution to drug disposition and responses of CYP2C19 substrates during drug development. Design of such clinical trials requires projection of genotype-dependent in vivo clearance and associated variabilities of the investigational drug, which is not generally available during early stages of drug development, but is essential for CYP2C19 substrates with multiple clearance pathways. This study evaluated the utility of pharmacogenetics-based mechanistic modeling in predicting such parameters. Hepatic CYP2C19 activity and variability within genotypes were derived from in vitro S-mephenytoin metabolic activity in genotyped human liver microsomes (N = 128). These data were then used in mechanistic models to predict genotype-dependent disposition of CYP2C19 substrates (i.e., S-mephenytoin, citalopram, pantoprazole, and voriconazole) by incorporating in vivo clearance or pharmacokinetics of wild-type subjects and parameters of other clearance pathways. Relative to the wild-type, the CYP2C19 abundance (coefficient of variation percentage) in CYP2C19*17/*17, *1/*17, *1/*1, *17/null, *1/null, and null/null microsomes was estimated as 1.85 (117%), 1.79 (155%), 1.00 (138%), 0.83 (80%), 0.38 (130%), and 0 (0%), respectively. The subsequent modeling and simulations predicted, within 2-fold of the observed, the means and variabilities of urinary S/R-mephenytoin ratio (36 of 37 genetic groups), the oral clearance of citalopram (9 of 9 genetic groups) and pantoprazole (6 of 6 genetic groups), and voriconazole oral clearance (4 of 4 genetic groups). Thus, relative CYP2C19 genotype-dependent hepatic activity and variability were quantified in vitro and used in a mechanistic model to predict pharmacokinetic variability, thus allowing the design of pharmacogenetics and drug-drug interaction trials for CYP2C19 substrates.

CYP2C19-Guided Escitalopram and Sertraline Dosing in Pediatric Patients: A Pharmacokinetic Modeling Study

“…CYP2C19-related differences in clearance and exposure in our models and CYP2C19-related parameter estimates in adults (Steere et al 2015). Regarding the impact of age on clearance and exposure, as the age (and weight) increases, the exposure and C max for both escitalopram and sertraline decrease.…”

“…C ytochrome P450 2C19 (CYP2C19) metabolizes multiple selective serotonin reuptake inhibitors (SSRIs) (Wang et al 2001;Chang et al 2014;Steere et al 2015), including sertraline, citalopram, and its s-enantiomer, escitalopram. Both escitalopram and sertraline are FDA approved for pediatric use, effectively treat anxiety (March et al 1998;Rynn et al 2001;Walkup et al 2008) and depressive disorders ) in youth, and are commonly prescribed to pediatric patients (Qato et al 2018).…”

“…In addition, patients with faster CYP2C19 metabolism have lower plasma escitalopram concentrations and are at high risk of treatment failure compared with NMs (Hicks et al 2015). In adults, the influence of CYP2C19 metabolizer status on escitalopram PKs is well described (Steere et al 2015;Jukić et al 2018); however, the relationship between these variants and efficacy in the pediatric population are less well understood ( Ji et al 2014).…”

Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification

“…Similarly, the interindividual variability in the physiologic parameters is dependent on the population demographics. Thus, ensuring that the demographics of the simulated population match those of the observed population may improve the accuracy of both the mean PK parameters (Steere et al, 2015) and the predicted population variability. More careful reporting of the simulated and observed study populations would also be critically important when model performance is assessed.…”

Pharmacodynamic studies of voriconazole: informing the clinical management of invasive fungal infections

“…To varying degrees, voriconazole is an inhibitor and/or substrate of CYPs 2B6, 2C19, 2C9, and 3A4 [9,164,183,184] with CYP2C19 playing the predominant, but not exclusive role in voriconazole's metabolism [121,185,186]. Genetic variation in CYP2C19 that limits metabolic activity may influence the potential for DDIs [18,58,187]. Likewise, younger pediatric patients are also at an increased risk for subtherapeutic voriconazole exposure, since the expression of CYP2C19 is higher in children relative to adults [18,[61][62][63].…”