Prediction of Etravirine Pharmacogenetics Using a Physiologically Based Pharmacokinetic Approach (Abstract #888)

Prediction of Etravirine Pharmacogenetics Using a Physiologically Based Pharmacokinetic Approach (Abstract #888), Siccardi, Marco, Olagunju A., Curley P., Hobson J., Khoo S., Back D., and Owen A. , 20th Conference on Retroviruses and Opportunistic Infections (CROI), 3-6 March, Atlanta, GA, USA, (2013) copy at

Date Presented:

3-6 March


Background: Etravirine (ETV) is metabolized by CYP3A4 and CYP2C19. A known inhibitor of CYP2C19, omeprazole increases ETV exposure by 41%. Since CYP2C19*2 (rs4244285) can affect CYP2C19 expression there is the potential to alter ETV exposure. We previously showed utility of physiologically based PK (PBPK) models for predicting genetic associations and drug-drug interactions from in vitro data in the absence of clinical data. The aim of this study was to develop a PBPK model for ETV PK and predict effects of CYP2C19*2 in virtual human subjects.

Methods: A new open-source PBPK model was developed with algorithms describing covariance between demographics and organ size, hepatic metabolism, induction of metabolic enzymes, expression, and mechanisms regulating absorption and distribution. In vitro data describing chemical properties as well as absorption, distribution, metabolism, and elimination of ETV were used to simulate ETV PK at 200 mg twice daily in 200 virtual subjects. Simulated PK parameters, such as Ctrough, Cmax, and AUC were compared with observed values from the literature. The impact of CYP2C19*2 on ETV clearance was then determined by altering CYP2C19 expression in the model. All simulations were conducted using the differential equation solver Berkeley Madonna.

Results: Simulated PK variables at steady state (mean ± SD) were Ctrough (293 ± 185 ng/mL), Cmax (363 ± 207 ng/mL), and AUC (4005 ± 2364 ng/mL.h), in agreement with previous clinical PK data: Ctrough (297 ± 391 ng/mL) and AUC (4522 ± 4710 ng/mL.h). Simulated mean ETV clearance (CL/F), volume of distribution, and ka were 59 ± 31 (L/h), 14.9 ± 3.6 L/kg, and 0.17 ± 0.011 hr–1, respectively. ETV (CL/F) was predicted to be 62 ± 35, 53 ± 31, and 41 ± 28 L/h for CYP2C19 *1/*1, *1/*2 and *2/*2, respectively.

Conclusions: The IVIVE model predicted in vivo PK of ETV in individuals with different CYP2C19 genotypes. The frequency of CYP2C19*2 has a higher frequency in Asian populations which may underpin heterogeneity in ETV exposure. Mechanistic evaluation of disposition can inform PBPK models and prediction of pharmacogenetic associations. IVIVE may be particularly helpful for the rational design of novel regimens for use in stratified populations. This includes prediction of optimal dose and dosing regimen, selection of partner drugs and validation of the likely overall pharmacological effect of discrete molecular processes, all of which can and should be tested in clinical studies.

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