Zhihai Cao (1, 2), Liang Zheng (2)
(1) Anhui Medical University, China (2) The Second Affiliated Hospital of Anhui Medical University, China
Introduction:
Ritonavir is a protease inhibitor (PI) that is frequently used as a “booster” to increase the pharmacokinetic exposure to co-administered PIs[1]. Ritonavir as a potent CYP3A4 inhibitor can raise plasma concentrations of several opioids and prolongs the analgesic impact; nevertheless, it may cause opioid-induced adverse effects such as respiratory depression[2, 3]. Hydrocodone is a semi-synthetic opioid, acts on μ-opioid receptors to exert analgesic effects, but hydrocodone is one of the common opioid drugs of abuse[4, 5]. Hydromorphone is the active metabolite produced by the metabolism of hydrocodone by CYP2D6 via the O-demethylation pathway[6]. Clinical studies indicated that multiple doses of ritonavir increases single-dose hydrocodone plasma exposures by 90%[7]. Physiologically based pharmacokinetic modeling (PBPK) is a powerful tool for predicting the nature and magnitude of metabolism-based drug-drug interactions (DDIs). This tool has been successfully applied in optimizing dosing protocol for opioids when comedication is inevitable.
Objectives:
- Develop a validated PBPK model for ritonavir and hydrocodone and its active metabolite hydromorphone.
- Study the feasibility of these model in quantifying the pharmacokinetic DDI between ritonavir and hydro
- Provide the PBPK modeling-based dosing recommendation for hydrocodone in the presence of ritonavir.
Methods:
We conducted PBPK modeling and simulation using the Open Systems Pharmacology (OSP) Suite version 11.2. We first developed hydrocodone and hydromorphone PBPK models that reflect the actual bioavailability and in vivo clearance contribution. The models were validated by observed datasets of hydrocodone and hydromorphone from clinical time-concentration data, concerning intravenous and single or multiple oral doses of uncoated or extended-release formulations. By using our previously developed ritonavir model, we constructed the DDI model and simulated the pharmacokinetic profiles of hydrocodone and hydromorphone in the presence of ritonavir under various dosing scenarios.
Results:
We have successively developed hydromorphone and hydrocodone models The hydrocodone and hydromorphone model accurately simulated the pharmacokinetic profiles of all administration scenarios. The geometric mean fold error (GMFE) of hydrocodone model for predicted/observed Cmax, AUC, and tmax for all simulated studies is 1.21, 1.13, and 1.26, respectively; the GMFE of Cmax, AUC, and tmax of hydromorphone for all simulated studies is 1.82, 1.08, and 0.99, respectively. The developed DDI model of hydrocodone and ritonavir accurately predicts the exposure changes of hydrocodone in the presence of ritonavir, the predicted Cmax ratio (CmaxR) and AUC ratio (AUCR) of hydrocodone in the presence of ritonavir are 1.24 and 1.81, respectively, compared to the observed values of 1.27 and 1.90. Despite ritonavir’s modest inhibition of CYP2D6, the primary enzyme that converts hydrocodone to hydromorphone, the concurrent use of ritonavir and hydrocodone did not reduce the plasma exposure to hydromorphone. According to the model simulation, the effect of ritonavir (100 mg b.i.d.) on the AUC of hydrocodone was similar under different dosing scenarios, including single dose or multiple doses of uncoated and ER formulations of hydrocodone. For the extended-release and uncoated formulation of hydrocodone, the Cmax and AUC of hydrocodone were increased by 55-81% and 75-88%, respectively, when administered with ritonavir 100 mg QD or BID, and the Cmax and AUC of hydromorphone were increased by 39-95% and 50-88%, respectively. When coadmistered with ritonavir, dose halving may be necessary for hydrocodone to achieve comparable hydrocodone plasma exposure.
Conclusions:
In this study, the developed model adequately describes the changes in exposure of hydrocodone co-administered with ritonavir and provides patients with a simulation-based dosing strategy for long-term co-administration dosing. For either extended-release or uncoated formulations of hydrocodone, the steady-state concentrations can be obtained by varying the dose with no change in the dosing interval or by doubling the dosing interval with no change in the dose. The findings indicated hitherto unstudied exposure-related dangers of hydrocodone-ritonavir interactions, emphasizing PBPK as a viable technique for directing appropriate dosage.
References:
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[7]Polepally AR, King JR, Ding B, et al. Drug-Drug Interactions Between the Anti-Hepatitis C Virus 3D Regimen of Ombitasvir, Paritaprevir/Ritonavir, and Dasabuvir and Eight Commonly Used Medications in Healthy Volunteers[J]. Clin Pharmacokinet, 2016, 55(8): 1003-14.
Reference: PAGE 32 (2024) Abstr 10865 [www.page-meeting.org/?abstract=10865]
Poster: Drug/Disease Modelling - Absorption & PBPK