Impact of low-dose ritonavir on danoprevir pharmacokinetics: results of computer-based simulations and a clinical drug-drug interaction study.
ABSTRACT Danoprevir, a potent, selective inhibitor of the hepatitis C virus (HCV) NS3/4A protease, is metabolized by cytochrome P450 (CYP) 3A. Clinical studies in HCV patients have shown a potential need for a high danoprevir daily dose and/or dosing frequency. Ritonavir, an HIV-1 protease inhibitor (PI) and potent CYP3A inhibitor, is used as a pharmacokinetic enhancer at subtherapeutic doses in combination with other HIV PIs. Coadministering danoprevir with ritonavir as a pharmacokinetic enhancer could allow reduced danoprevir doses and/or dosing frequency. Here we evaluate the impact of ritonavir on danoprevir pharmacokinetics.
The effects of low-dose ritonavir on danoprevir pharmacokinetics were simulated using Simcyp, a population-based simulator. Following results from this drug-drug interaction (DDI) model, a crossover study was performed in healthy volunteers to investigate the effects of acute and repeat dosing of low-dose ritonavir on danoprevir single-dose pharmacokinetics. Volunteers received a single oral dose of danoprevir 100 mg in a fixed sequence as follows: alone, and on the first day and the last day of 10-day dosing with ritonavir 100 mg every 12 hours.
The initial DDI model predicted that following multiple dosing of ritonavir 100 mg every 12 hours for 10 days, the danoprevir area under the plasma concentration-time curve (AUC) from time zero to 24 hours and maximum plasma drug concentration (C(max)) would increase by about 3.9- and 3.2-fold, respectively. The clinical results at day 10 of ritonavir dosing showed that the plasma drug concentration at 12 hours postdose, AUC from time zero to infinity and C(max) of danoprevir increased by approximately 42-fold, 5.5-fold and 3.2-fold, respectively, compared with danoprevir alone. The DDI model was refined with the clinical data and sensitivity analyses were performed to better understand factors impacting the ritonavir-danoprevir interaction.
DDI model simulations predicted that danoprevir exposures could be successfully enhanced with ritonavir coadministration, and that a clinical study confirming this result was warranted. The clinical results demonstrate that low-dose ritonavir enhances the pharmacokinetic profile of low-dose danoprevir such that overall danoprevir exposures can be reduced while sustaining danoprevir trough concentrations.
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ABSTRACT: The aim of this study was to evaluate a strategy based on a physiologically based pharmacokinetic (PBPK) model for the prediction of PK profiles in human using in vitro data when elimination of compounds relies on active transport processes. The strategy was first applied to rat in vivo and in vitro data in order to refine the PBPK model. The model could then be applied to human in vitro uptake transport data using valsartan as a probe substrate. Plated rat and human hepatocytes, and cell lines overexpressing human OATP1B1 and OATP1B3 were used for in vitro uptake experiments. The uptake rate of valsartan was higher for rat hepatocytes (K (m,u) = 28.4 +/- 3.7 muM, V (max) = 1318 +/- 176 pmol/mg/min and P (dif) = 1.21 +/- 0.42 microl/mg/min) compared to human hepatocytes (K (m,u) = 44.4 +/- 14.6 microM, V (max) = 304 +/- 85 pmol/mg/min and P (dif) = 0.724 +/- 0.271 microl/mg/min). OATP1B1 and 1B3 parameters were correlated to human hepatocyte data using experimentally established relative activity factors (RAF). Resulting PBPK simulations using those in vitro data were compared for plasma (human and rat) and bile (rat) concentration-time profiles following i.v. bolus administration of valsartan. An uncertainty analysis indicated that the scaled in vitro uptake clearance had to be adjusted with an additional empirical scaling factor of 5 to match the plasma concentrations and biliary excretion profiles. Applying this model, plasma clearances (CL(P)) for rat and human were predicted within two-fold relative to predictions based on respective in vitro data. The corrected hepatic uptake transport kinetic parameters enabled the prediction of valsartan in vivo PK profiles and plasma clearances, using PBPK modeling. Moreover, the interspecies difference in elimination rate observed in vivo was correctly reflected in the transport parameters determined in vitro. More data are needed to support more general applications of the proposed approach including its use for metabolized compounds.Journal of Pharmacokinetics and Biopharmaceutics 11/2009; 36(6):585-611. · 2.06 Impact Factor
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ABSTRACT: To investigate the pharmacokinetics of darunavir/ritonavir and raltegravir, in HIV-infected subjects, in both plasma and at the intracellular (IC) site of action. HIV-infected patients on antiretroviral therapy received raltegravir 400 mg twice daily for 21 days (period 1); darunavir/ritonavir 800/100 mg once daily was added for 14 days (period 2), and patients were randomized to continue raltegravir twice daily (group 1) or to switch to 800 mg once daily (group 2), then they all stopped raltegravir intake and continued darunavir/ritonavir once daily for 14 days (period 3). Drug concentrations in plasma and cells (peripheral blood mononuclear cell) were measured, and differences in geometric mean ratios (GMR) and 90% confidence intervals (CI) between period 2 versus period 3 and period 2 versus period 1 were assessed. Twenty-four patients completed the study. Group 1 GMR (90% CI) of darunavir area under the curve (AUC) with and without raltegravir was 1.24 (1.13 to 1.45) for plasma and 1.24 (1.07 to 1.73) for cells and for group 2 was 1.14 (1.07 to 1.24) and 1.03 (0.94 to 1.16). GMR (90% CI) of raltegravir AUC without and with darunavir/ritonavir (plasma and cells) for group 1 was 0.90 (0.73 to 1.44) and 1.02 (0.81 to 1.67) and for group 2 was 1.21 (1.03 to 1.77) and 1.27 (1.07 to 1.94). Geometric mean IC to plasma AUC ratios were 5.3 and 4.9 for darunavir in groups 1 and 2 when darunavir/ritonavir was given alone and 4.9 and 5.6 for raltegravir when given alone. These ratios were not altered by the coadministered drug. No remarkable interactions between darunavir/ritonavir and raltegravir in plasma or cells were seen. Raltegravir IC concentrations are higher than previously reported; the difference being due to modified cell isolation procedures that reduced drug loss caused by washing.JAIDS Journal of Acquired Immune Deficiency Syndromes 09/2011; 58(5):450-7. · 4.65 Impact Factor
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ABSTRACT: To characterize the cytochrome P450 enzyme(s) responsible for the N-dealkylation of maraviroc in vitro, and predict the extent of clinical drug-drug interactions (DDIs). Human liver and recombinant CYP microsomes were used to identify the CYP enzyme responsible for maraviroc N-dealkylation. Studies comprised enzyme kinetics and evaluation of the effects of specific CYP inhibitors. In vitro data were then used as inputs for simulation of DDIs with ketoconazole, ritonavir, saquinavir and atazanvir, using the Simcyptrade mark population-based absorption, distribution, metabolism and elimination (ADME) simulator. Study designs for simulations mirrored those actually used in the clinic. Maraviroc was metabolized to its N-dealkylated product via a single CYP enzyme characterized by a K(m) of 21 microM and V(max) of 0.45 pmol pmol(-1) min(-1) in human liver microsomes and was inhibited by ketoconazole (CYP3A4 inhibitor). In a panel of recombinant CYP enzymes, CYP3A4 was identified as the major CYP responsible for maraviroc metabolism. Using recombinant CYP3A4, N-dealkylation was characterized by a K(m) of 13 microM and a V(max) of 3 pmol pmol(-1) CYP min(-1). Simulations therefore focused on the effect of CYP3A4 inhibitors on maraviroc pharmacokinetics. The simulated median AUC ratios were in good agreement with observed clinical changes (within twofold in all cases), although, in general, there was a trend for overprediction in the magnitude of the DDI. Maraviroc is a substrate for CYP3A4, and exposure will therefore be modulated by CYP3A4 inhibitors. Simcyptrade mark has successfully simulated the extent of clinical interactions with CYP3A4 inhibitors, further validating this software as a good predictor of CYP-based DDIs.British Journal of Clinical Pharmacology 10/2008; 66(4):498-507. · 3.58 Impact Factor