Darunavir (DRV) is a novel oral non peptidic HIV-1 protease inhibitor, indicated for the treatment of HIV infection in treatment naïve and experienced patients. DRV should be co-administered with low dose ritonavir as a booster to enhance its pharmacokinetic exposure.
DRV is both a substrate and inhibitor of cytochrome P450 3A4 (CYP3A4) and was found to be a substrate and inhibitor of P-glycoprotein in vitro. Therefore, drug-drug interactions are expected. High inter-individual variability was reported for DRV exposure that could be explained in part variation in ritonavir exposure. To define the shape of interaction between DRV and its booster, we developed the first simultaneous population model integrating the drug levels of both drugs in 106 HIV-infected patients, and we tested several models of inhibition of DRV by ritonavir and vice versa.
Own contribution: Pharmacokinetic modeling, simulation and statistical analyses. In addition to article writing and generating figures and tables.
Simultaneous Population Pharmacokinetic Analysis of Darunavir and Ritonavir in HIV-Infected Individuals
M. Arab-Alameddine1,5, T. Buclin1, M. Rotger2, R. Lubomirov2, K.Rentsch3, B. Ledergerber3, M. Cavassini4, A.Telenti2, Members of the Swiss HIV Cohort, L.A. Décosterd1 & C. Csajka1,5.
.
1 Division of Clinical Pharmacology and Toxicology, University Hospital Center and University of Lausanne, Lausanne
2 Institute of Microbiology, University Hospital Center and University of Lausanne; Lausanne 3 Division of Laboratory Medicine, University Hospital Zurich, Zurich, Switzerland
4 Division of Infectious Diseases, University Hospital Center and University of Lausanne; Lausanne 5 Department of Pharmaceutical Sciences, University of Geneva, University of Lausanne,
Geneva Switzerland.
In Preparation
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Abstract
Background. Darunavir (DRV) should be administered with low dose ritonavir (RTV) to enhance its bioavailability. Population pharmacokinetic approach was implemented to identify potential influencing factors on DRV and RTV exposure, to characterize the interaction between these 2 compounds and to compare several dosage regimens.
Methods. Study population included 106 HIV-infected individuals that contributed 289 DRV and 289 RTV plasma concentrations. Genetic variants of CYPP3A and few nuclear receptors were available for 60 individuals. First, population pharmacokinetic models for DRV and RTV were built using NONMEM, with inclusion of demographic and genetic factors as covariates.
Second, interaction between DRV and RTV was tested in a simultaneous model incorporating both drugs levels. Finally, simulations were used to compare trough concentrations (Ctrough) of several dosage regimens.
Results. One-compartment model with first-order absorption best characterized DRV and RTV pharmacokinetics. Lopinavir coadministration and RTV exposure (AUC) affected DRV CL, while body weight and DRV AUC influenced RTV CL. Interaction model did not capture a direct effect of RTV concentrations on DRV CL. Simulated DRV/RTV at 600/50 and 400/100 mg twice daily (b.i.d) resulted in 28% and 33% lower Ctrough respectively while 600/200 mg b.i.d resulted in 35% higher Ctrough compared to 600/100 b.i.d. DRV/RTV 900/100 mg once daily (q.d) had 12.5% higher Ctrough compared to 800/100 mg q.d.
Conclusion. Relatively important variability in DRV (34%) and RTV (47%) pharmacokinetics remained unexplained after inclusion of covariates. Dosage adjustment of DRV or RTV is conceivable in patients with wild-type and resistant HIV strains and warrants further investigations.
Introduction
Giant advances in HIV therapy in terms of viral suppression have been achieved since the introduction of the protease inhibitors (PI). However, the emergence of viral resistance to the old drugs has urged the development of new molecules active against the resistant strains.
Darunavir (DRV) is a novel oral non peptidic HIV-1 protease inhibitor, indicated for the treatment of HIV infection in antiretroviral treatment-naive and treatment-experienced adults and pediatric patients 6 years and older [1]. It has more potent and higher binding magnitude to both wild type and multidrug resistant (MDR) HIV-1 strains enzymes than other drugs in the same class. Moreover, DRV was found to have superior antiviral efficacy at 96 weeks
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and significantly lower incidence of gastrointestinal intolerance and lower increase in triglyceride and total cholesterol [2, 3].
DRV is generally well tolerated in treatment naïve and experienced HIV-infected patients;
with few treatment discontinuation due to adverse that are most frequently event mild to moderate in severity [3, 4] and mainly included diarrhea, nausea, headache, and nasopharyngitis in addition to grade 2 to 4 laboratory abnormalities including abnormal bilirubin and transaminase levels. Drug induced hepatitis and liver injury were rarely reported [3].
DRV must be co-administered with low doses of ritonavir (RTV) to improve its pharmacokinetic profile and increase drug exposure. Indeed, the absolute oral bioavailability of DRV increased by more than 2 fold (from 37% to 82%) and its overall exposure increased by 14 when co-administered with RTV [5]. The standard approved DRV/RTV dosage is 800 /100mg once daily for treatment naïve patients and of 600/100 mg twice daily for treatment experienced patients [2, 3].
DRV is rapidly absorbed from the gastrointestinal track and food is known to increase DRV solubility thus increasing both AUC by 42% and maximal concentration (Cmax) by 35%. DRV is 95% bound to plasma protein mainly to alpha-1 acid glycoprotein (AAG) and to a lesser extent to albumin. DRV is both a substrate and inhibitor of cytochrome P450 3A4 (CYP3A4) and was found to be a substrate and inhibitor of P-glycoprotein in vitro [3, 4] therefore, drug interactions would be expected. Furthermore, pharmaco-enhancement of DRV exposure by co-administration of RTV markedly improves the pharmacokinetic profile and decreases the inter-individual variability [4].
In this study we performed a population pharmacokinetic analysis of DRV and RTV in order to 1- characterize DRV and RTV pharmacokinetic profile in HIV-1 infected individuals, and to estimate inter- and intra-individual variability in HIV-1 infected population, 2- evaluate the impact of demographics and co-medications on DRV absorption and disposition, 3- characterize the shape of RTV inhibition on DRV elimination, 4- and finally explore the influence of CYP3A4/5 genetic variants on DRV clearance.
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Materials and methods
Study population
Plasma DRV and RTV levels were obtained from 106 HIV infected individual enrolled in the Swiss HIV cohort Study and measured during routine therapeutic drug monitoring according to local treatment guidelines. A median of 2 concentration sample per individual (range 1-12) was collected and drawn between 0.5 and 43.25 hours after last drug intake under steady-state conditions. This study was conducted within the framework of the Swiss HIV Cohort Study (www.shcs.ch). The ethics committees of all participating centers approved the project and all participants gave written informed consent for genetic testing.
Analytical method
Collected blood samples were stored on ice, centrifuged within 30 minutes of collection and stored at -20°C until batch analysis. Plasma obtained from HIV infected individuals was isolated by centrifugation, inactivated for virus at 60°C for 60 minutes. Plasma DRV levels were determined by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) after protein precipitation with acetonitrile according to our previously reported analytical method [6]. The calibration curves are linear with a lower limit of quantification of 25 ng/mL for DRV and 5 ng/mL for RTV. The laboratory participates in an international external quality assurance program for antiretroviral drugs analysis (KKGT, Stichting Kwaliteitsbewaking Klinische Geneesmiddelanalyse en Toxicologie, Association for Quality Assessment in TDM and clinical Toxicology, The Hague, The Netherlands).
Genotyping
Thirty three SNPs with proven functional effect in 15 genes possibly relevant for DRV metabolism in Caucasians were available for 60 individuals from a previous pharmacokinetic study. The complete list of genes and SNPs included in the array are shown in Supplementary Table 1S.
Parameter Estimation and Selection
NONMEM® [7] (version VII, NM-TRAN, version II) was used with the FOCE INTERACTION method to fit the data. As goodness of fit statistic, NONMEM® uses the objective function, which is approximately equal to minus twice the logarithm of the maximum likelihood. The likelihood ratio test, based on the reduction in objective function (ΔOF), was used to compare two models. A ΔOF (-2 log likelihood, approximate χ2distribution) of 3.84, 5.99, 7.81 and
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9.48 points for 1, 2, 3 or 4 additional parameters, respectively, was used to determine statistical significance (p <0.05) between two models. The reliability of the results was checked on diagnostic goodness of fit plots, along with the measure of the standard errors, the correlation matrix of parameter estimates and the size of residual errors.
Model-based pharmacokinetic modeling Structural model
A stepwise procedure was used to find the model that fitted DRV and RTV data best. First, both drugs were analyzed separately using one and two- compartments with first-order absorption from the gastrointestinal tract. The final pharmacokinetic model was a one-compartment model with first-order absorption and elimination for both drugs. The estimated parameters are the clearance (CL), the volume of distribution (Vd), and the absorption rate constant (ka). Since DRV and RTV were only administered orally, CL and V represent apparent values (CL/F, Vd/F and respectively, where F is the oral bioavailability).
Statistical model
Exponential errors following a log-normal distribution were assumed for the description of the interpatient variability of the pharmacokinetic parameters and were shown by the equation θj = θeηj, where θj is the individual pharmacokinetic parameter of the jth individual, θ is the geometric average population value and ηj, is the random effect value, which is an independent, normally distributed effect with a mean of 0 and a variance of Ω. A proportional error model was assigned to the intrapatient (residual) variability.
Covariate model
Analyses of the covariate effects on CL were performed in 2 main steps (i) assessment of the influence of demographic variables and concomitant medications by incorporating them directly in the model (ii) discovery analysis of the impact of candidate single nucleotide polymorphisms using generalized additive model (GAM) and graphical plots (iii) Analysis of the inhibition of DRV CL induced by RTV. The genetic covariates that showed an effect on DRV and/or RTV clearances in the GAM analysis were then selected to be tested for significance using NONMEM®.
GAM was performed on the empiric individual Bayesian estimates of a parameter using a stepwise addition/deletion method, where each covariate is introduced in the model through
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functional linear or nonlinear representation. At each step, the model is improved by the addition or deletion of the single term that results in the largest decrease in the Akaike information criterion [8]. The search stops when the Akaike information criterion reaches a minimum value. GAM was performed using the software R (R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (http://www.R-project.org/). The exploration of the potential impact of genetic variants on DRV and RTV exposure was performed as follows. First, we generated box-plots of the individual Bayesian estimates of DRV and RTV clearances versus the genetic covariates.
Second, we tested the covariates using GAM analysis. Finally, the covariates that showed an effect on DRV and RTV elimination both graphically and in terms of AIC were then incorporated in the population model and tested in NONMEM.
Potentially influential covariates were incorporated sequentially into the pharmacokinetic model. The typical value of CL was modeled to depend linearly on a covariate X (such as age, centered on the mean; categorical covariates being coded as indicator variables into 0 or 1) as shown in the equations:CL = θa ×(1+θb × X), where θa is the average estimate and θb is the relative deviation from average attributed to the covariate X.
The available demographic covariates were sex, race, age, body weight and height;
aspartate amino transferase (AST) and alanine amino transferase (ALT) in addition to a few antiretroviral co-medications.
The Individual Bayesian estimates of RTV clearance (CLind) were used to derive and individual values of area under the concentration curve (AUC0–tau) defined as Dose/CLind. The influence of RTV on DRV elimination and reciprocally was first evaluated assuming non-competitive inhibition. Models integrating several functions of AUC0–tau DRV were tested as follows:
1. Power function using the equation
2
where CLi is the DRV CL of the ith individual, θ1 is the estimate of the population CL, AUCRTV is the value of RTV AUC of ith individual, 4.56 (mg.h/liter) is the median RTV AUC of all the study participants and finally θ2 is the factor associated with the effect of RTV on DRV CL.
2. Linear function using the equation
)
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where θ2 is the factor associated with the effect of RTV AUC on DRV clearance.
3. Exponential function using the equation )
where θ2 is the factor associated with the effect of RTV AUC on DRV clearance.
RTV area under curve (AUC0–12 or 24) (defined as Dose/CLind) were derived from the final model incorporating covariates that best described RTV plasma levels.
In the next step, the inhibition of DRV clearance by RTV was tested assuming competitive inhibition and modeled using a direct concentration-dependent relationship of the form:
)
The inhibition was modeled using:
a) a linear function using the equation:
) (
1 SLOPE CRTV
I = − × (eq. 4)
where SLOPE is the parameter associated with the negative relationship between RTV concentration and CL0
b) an exponential function using:
)
where RTV_INH is the factor associated with the decrease in CL0 c) a maximum effect function using:
RTV
where CRTV is RTV plasma concentration at each time point, Imax is the maximum inhibitory effect of RTV and IC50 is the CRTV producing 50% of the Imax.
Simulations
Simulations of the pharmacokinetic parameters obtained from the final model in 1000 individuals under several dosing regimens (600/100 b.i.d, 400/100 b.i.d, 600/50 b.i.d, 800/100 q.d, 900/100 q.d) were performed to predict DRV exposure and to compare DRV concentrations at trough (Cmin). These simulations were also used to predict the percentage of patients that will exhibit a Cmin less than DRV 50% effective concentration (EC50)
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corrected for protein binding for both wild type (55 ng/ml) and protease inhibitors resistant HIV-1 strains (550 ng/ml) [9, 10].
Model validation
The stability and the performance of the final population pharmacokinetic model were validated by the bootstrap method. Repeated random sampling from the original dataset generated 200 bootstrap samples having the same the size as the original one. The final population pharmacokinetic model was fitted repeatedly to the 200 bootstrapped samples and pharmacokinetic parameters were calculated for each dataset. The median and the 95%
confidence interval of each parameter obtained with the bootstrapped data were then compared to the corresponding parameters obtained with the original dataset. The statistical analysis was performed using Perl-speaks-NONMEM version 3.2.4 (http://psn.sourceforge.net/).
The final model was also validated using a visual predictive check method (VPC). Using the parameter values of the final population pharmacokinetic model, we simulated data for 1000 individuals and we generated 2.5th, 50th and 97.5th percentiles. Then the observed concentrations were plotted against the 95% prediction interval of the simulated dataset at each time point and visually compared. The figures were generated using GraphPad Prism (Version 4.00 for Windows, GraphPad Software, San Diego California USA, (www.graphpad.com).
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Results
A total of 289 observations of both DRV and RTV were obtained from the 106 HIV+
individuals and included in the population pharmacokinetic analysis. The demographic characteristic of the study population are described in Table 1.
1. Population Pharmacokinetic Analysis of DRV
DRV concentration measurements ranged between 17 and 14’635 ng/mL. A 1-compartment model with first order absorption from the gastrointestinal tract fitted the data appropriately and the addition of a second compartment did not significantly improve the fit (ΔOF=-2). No further reduction in the objective function was observed upon assignment of lag time (ΔOF=
0.0). Assignment of a between-subject variability to Vd and Ka in addition to CL did not improve the model fit (ΔOF= 0.0). A proportional error model fitted the data at best, with no improvement using a combine additive and proportional residual error (ΔOF=-0.0). The final population parameters with their inter-individual variability of the model without covariates were CL 10.8 liters/h (CV, 37.4%), V 122.8 liters and a Ka 1.04 h-1.
Among the demographic covariates tested, body weight showed some influence on DRV pharmacokinetics (ΔOF= -4.3, p=0.03) where a 43% increase in DRV clearance was observed on body weight doubling. Among the tested co-medications, the inclusion of lopinavir (LPV) slightly improved the fit, suggesting a 48% increase in CL in presence of this drug (ΔOF= -4.18, p=0.04) and explained only 1% of the inter-individual variability. As expected, the inclusion of RTV concentrations on DRV CL improved the description of the data. Among the models testing a competitive inhibition, the use of a power function of RTV AUC0-tau on DRV CL (eq. 1) provided the best fit (ΔOF= -9.4, p=0.001). DRV CL was shown to be reduced by 5% for an increase in RTV AUC of 1mg.L-1.hr and explained 2% of the inter-individual variability.
The combination of the statistically significant covariates on DRV CL revealed that only RTV AUC and LPV remained significant (ΔOF= -14.6, p=0.0006) compared to a model without covariates and reduced inter-individual variability only by 3%.
The final population pharmacokinetic parameters were a CL/F of 10.9 L/h, (CV=34.0%) a Vd/F of 121.2 L and a Ka of 1.04 h-1 (Table 2). The DRV elimination half life was 7.7hours.
Goodness-of-fit plots of population and individual predictions obtained in the final DRV model versus the observations are presented in Figure 1.
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Table 2: Pharmacokinetic parameters obtained in the Final model. ,Median and 90%
confidence interval of the pharmacokinetic parameters obtained from the bootstrapped 200 DRV samples.
a CL/F, mean apparent clearance; V/F, mean apparent volume of distribution; ka, mean absorption rate constant; F, bioavailability.
b Estimate of variability is expressed as CV (%).
c Standard errors of the estimates (SE),defined as SE/estimate and expressed as percentages.
d Standard errors of the coefficient of variation, taken as SE /Estimate and expressed as percentage.
e Relative influence of RTV AUC on DRV clearance (see text)
f Relative influence of LPV on DRV clearance (see text)
†Difference (%) = (bootstrap median value – typical value from final model)/bootstrap median x 100
PRED DRV [ng/ml]
Figure 2. Plots of population (left panel) and individual (right panel) predictions versus the observations of the final model of RTV.
Parametera Final Population PK parameters
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Table 1: Demographic characteristics of the study population.
Characteristic Value % Of study population
Sex (No.)
HIV RNA Level (log10 copies/ml) Median (Range)
332 (19-1496) 0 (0-59700)
- -
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2. Population Pharmacokinetic Analysis of RTV
RTV concentration measurements ranged between 0 and 5’195ng/mL. A 1-compartment model with first order absorption from the gastrointestinal tract fitted the data appropriately and the addition of a second compartment did not significantly improve the fit (ΔOF= +5.0).
No further reduction in objective function was observed upon assignment of lag time (ΔOF= -1.0). In addition to CL, assignment of BSV to Vd improved significantly the fit (ΔOF= -30.1) but no BSV was observed on Ka (ΔOF= 0.0). A proportional error model fitted the data well and no improvement was observed with the use of a proportional and additive error model (ΔOF=-0.0). The final population parameters with their BSV of the model without covariates were a CL of 20.7 liters/h (CV, 54.6%), a V of 55.31 liters (CV, 148.3%) and a Ka of 0.13 h-1. Among the demographic covariates testes, body weight showed an influence on RTV pharmacokinetics (ΔOF= -16.9, p<0.001), yielding a 13.7% increase in RTV CL with 10 kg increase in body weight and explained 6% of the inter-individual variability. Gender, height and age also had an impact on RTV exposure (ΔOF= -11.6, -17.03 and -6.9, respectively) but were all correlated to body weight. This factor explained 4% of the variability in RTV CL.
As observed for DRV, the inclusion of DRV AUC on RTV clearance improved the description
of RTV data. The use of a power function 2
1 52
θ θ
×
= DRV
i
CL AUC was the best model
(ΔOF= -17.0, p<0.0001) where an increase in DRV AUC of 1 mg.L-1.hr resulted in a 0.6%
lower in RTV CL and explained 6% of variability in RTV CL and was responsible of 3% of the variability in CL.
The multivariate analysis showed that body weight and DRV AUC remained both significant (ΔOF= -11.6) and explained 9% of variability in CL. No other factors showed any other effect on RTV CL or Vd. The final population pharmacokinetic parameters are presented in Table 3. The RTV elimination half life was 6.2 hours. Goodness-of-fit plots of population and individual predictions obtained in the final RTV model versus the observations are presented in Figure 2.
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PRED RTV [ng/ml]
Concentration [ng/ml]
1 10 100 1000 10000 100000 1
1 10 100 1000 10000 100000 1
Figure 2. Plots of population (left panel) and individual (right panel) predictions versus the observations of the final model of RTV
.
Table 3: median and 90% confidence interval of the pharmacokinetic parameters obtained from the bootstrapped 200 RTV samples
)
BW= Mean body weight, BW= Body weight, (see text)
a CL/F, mean apparent clearance; V/F, mean apparent volume of distribution; ka, mean absorption
a CL/F, mean apparent clearance; V/F, mean apparent volume of distribution; ka, mean absorption