Development of a physiologically‐based pharmacokinetic model for Ritonavir characterizing exposure and drug interaction potential at both acute and steady‐state conditions

Ritonavir (RTV) is a potent CYP3A inhibitor that is widely used as a pharmacokinetic (PK) enhancer to increase exposure to select protease inhibitors. However, as a strong and complex perpetrator of CYP3A interactions, RTV can also enhance the exposure of other co-administered CYP3A substrates, potentially causing toxicity. Therefore, the prediction of drug-drug interactions (DDIs) and estimation of dosing requirements for concomitantly administered drugs is imperative. In this study, we aimed to develop a physiologically-based PK (PBPK) model for RTV using the PK-sim® software platform. A total of 13 clinical PK studies of RTV covering a wide dose range (100 to 600 mg including both single and multiple dosing), and eight clinical DDI studies with RTV on CYP3A and P-gp substrates, including alprazolam, midazolam, rivaroxaban, clarithromycin, fluconazole, sildenafil, and digoxin were used for the model development and evaluation. Chronopharmacokinetic differences (between morning vs. evening doses) and limitations in parameter estimation for biochemical processes of RTV from in vitro studies were incorporated in the PBPK model. The final developed PBPK model predicted 100% of RTV AUC<sub>last</sub> and C<sub>max</sub> within a twofold dimension error. The geometric mean fold error (GMFE) from all PK datasets was 1.275 and 1.194, respectively. In addition, 97% of the DDI profiles were predicted with the DDI ratios within a twofold dimension error. The GMFE values from all DDI datasets were 1.297 and 1.212, respectively. Accordingly, this model could be applied to the prediction of DDI profiles of RTV and CYP3A substrates and used to estimate dosing requirements for concomitantly administered drugs.

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Journal
CPT Pharmacometrics & Systems Pharmacology
Published
2024-12-23
DOI
https://doi.org/10.1002/psp4.13293
Primary Topic
Pharmacogenetics and Drug Metabolism
Type
article
Field-Weighted Citation Impact
0.00

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article

Development of a physiologically‐based pharmacokinetic model for Ritonavir characterizing exposure and drug interaction potential at both acute and steady‐state conditions

Jung‐woo Chae, Hwi‐yeol Yun, Lien Thi Ngo, Tham Thi Bui et al.
CPT Pharmacometrics & Systems Pharmacology
Pharmacogenetics and Drug Metabolism
article

Development of a physiologically‐based pharmacokinetic model for Ritonavir characterizing exposure and drug interaction potential at both acute and steady‐state conditions

Jung‐woo Chae, Hwi‐yeol Yun, Lien Thi Ngo, Tham Thi Bui, Jeremiah D. Momper, Woo Jin Jung
article en

Abstract

Ritonavir (RTV) is a potent CYP3A inhibitor that is widely used as a pharmacokinetic (PK) enhancer to increase exposure to select protease inhibitors. However, as a strong and complex perpetrator of CYP3A interactions, RTV can also enhance the exposure of other co-administered CYP3A substrates, potentially causing toxicity. Therefore, the prediction of drug-drug interactions (DDIs) and estimation of dosing requirements for concomitantly administered drugs is imperative. In this study, we aimed to develop a physiologically-based PK (PBPK) model for RTV using the PK-sim® software platform. A total of 13 clinical PK studies of RTV covering a wide dose range (100 to 600 mg including both single and multiple dosing), and eight clinical DDI studies with RTV on CYP3A and P-gp substrates, including alprazolam, midazolam, rivaroxaban, clarithromycin, fluconazole, sildenafil, and digoxin were used for the model development and evaluation. Chronopharmacokinetic differences (between morning vs. evening doses) and limitations in parameter estimation for biochemical processes of RTV from in vitro studies were incorporated in the PBPK model. The final developed PBPK model predicted 100% of RTV AUC<sub>last</sub> and C<sub>max</sub> within a twofold dimension error. The geometric mean fold error (GMFE) from all PK datasets was 1.275 and 1.194, respectively. In addition, 97% of the DDI profiles were predicted with the DDI ratios within a twofold dimension error. The GMFE values from all DDI datasets were 1.297 and 1.212, respectively. Accordingly, this model could be applied to the prediction of DDI profiles of RTV and CYP3A substrates and used to estimate dosing requirements for concomitantly administered drugs.

CPT Pharmacometrics & Systems PharmacologyVol. 14(3)
Chungnam National University (KR), University of California San Diego (US), Phenikaa (Vietnam) (VN), Phenikaa University (VN), University of Montana (US)
National Research Foundation, Chungnam National University, National Research Foundation of Korea, Ministry of Science and ICT, South Korea
Good health and well-being
Openalex Percentile: Top 69%
Pharmacogenetics and Drug Metabolism
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