Model-based analysis of the pharmacokinetic interactions between ritonavir, nelfinavir, and saquinavir after simultaneous and staggered oral administration

Population pharmacokinetic modelling of the changes in atazanavir plasma clearance caused by ritonavir plasma concentrations in HIV-1 infected patients

AIMS

The aim of the present study was to develop a simultaneous population pharmacokinetic model for atazanavir (ATV) incorporating the effect of ritonavir (RTV) on clearance to predict ATV concentrations under different dosing regimens in HIV-1-infected patients.

METHODS

A Cross-sectional study was carried out in 83 HIV-1-infected adults taking ATV 400 mg or ATV 300 mg/RTV 100 mg once daily. Demographic and clinical characteristics were registered and blood samples collected to measure drug concentrations. A population pharmacokinetic model was constructed using nonlinear mixed-effects modelling and used to simulate six dosing scenarios.

RESULTS

The selected one-compartmental model described the pharmacokinetics of RTV and ATV simultaneously, showing exponential, direct inhibition of ATV clearance according to the RTV plasma concentration, which explained 17.5% of the variability. A mean RTV plasma concentration of 0.63 mg l-1 predicted an 18% decrease in ATV clearance. The percentages of patients with an end-of-dose-interval concentration of ATV below or above the minimum and maximum target concentrations of 0.15 mg l-1 and 0.85 mg l-1 favoured the selection of the simulated ATV/RTV once-daily regimens (ATV 400 mg, ATV 300 mg/RTV 100 mg, ATV 300 mg/RTV 50 mg, ATV 200/RTV 100 mg) over the unboosted twice-daily regimens (ATV 300 mg, ATV 200 mg).

CONCLUSIONS

A one-compartment simultaneous model can describe the pharmacokinetics of RTV and ATV, including the effect of RTV plasma concentrations on ATV clearance. This model is promising for predicting individuals' ATV concentrations in clinical scenarios, and supports further clinical trials of once-daily doses of ATV 300 mg/RTV 50 mg or ATV 200 mg/RTV 100 mg to confirm efficacy and safety.

Methods and strategies for assessing uncontrolled drug-drug interactions in population pharmacokinetic analyses: results from the International Society of Pharmacometrics (ISOP) Working Group

The purpose of this work was to present a consolidated set of guidelines for the analysis of uncontrolled concomitant medications (ConMed) as a covariate and potential perpetrator in population pharmacokinetic (PopPK) analyses. This white paper is the result of an industry-academia-regulatory collaboration. It is the recommendation of the working group that greater focus be given to the analysis of uncontrolled ConMeds as part of a PopPK analysis of Phase 2/3 data to ensure that the resulting outcome in the PopPK analysis can be viewed as reliable. Other recommendations include: (1) collection of start and stop date and clock time, as well as dose and frequency, in Case Report Forms regarding ConMed administration schedule; (2) prespecification of goals and the methods of analysis, (3) consideration of alternate models, other than the binary covariate model, that might more fully characterize the interaction between perpetrator and victim drug, (4) analysts should consider whether the sample size, not the percent of subjects taking a ConMed, is sufficient to detect a ConMed effect if one is present and to consider the correlation with other covariates when the analysis is conducted, (5) grouping of ConMeds should be based on mechanism (e.g., PGP-inhibitor) and not drug class (e.g., beta-blocker), and (6) when reporting the results in a publication, all details related to the ConMed analysis should be presented allowing the reader to understand the methods and be able to appropriately interpret the results.

In vitro OATP1B1 and OATP1B3 inhibition is associated with observations of benign clinical unconjugated hyperbilirubinemia

1.  Transient benign unconjugated hyperbilirubinemia has been observed clinically with several drugs including indinavir, cyclosporine, and rifamycin SV. Genome-wide association studies have shown significant association of OATP1B1 and UGT1A1 with elevations of unconjugated bilirubin, and OATP1B1 inhibition data correlated with clinical unconjugated hyperbilirubinemia for several compounds. 2.  In this study, inhibition of OATP1B3 and UGT1A1, in addition to OATP1B1, was explored to determine whether one measure offers value over the other as a potential prospective tool to predict unconjugated hyperbilirubinemia. OATP1B1 and OATP1B3-mediated transport of bilirubin was confirmed and inhibition was determined for atazanavir, rifampicin, indinavir, amprenavir, cyclosporine, rifamycin SV and saquinavir. To investigate the intrinsic inhibition by the drugs, both in vivo Fi (fraction of intrinsic inhibition) and R-value (estimated maximum in vivo inhibition) for OATP1B1, OATP1B3 and UGT1A1 were calculated. 3.  The results indicated that in vivo Fi values >0.2 or R-values >1.5 for OATP1B1 or OATP1B3, but not UGT1A1, are associated with previously reported clinical cases of drug-induced unconjugated hyperbilirubinemia. 4.  In conclusion, inhibition of OATP1B1 and/or OATP1B3 along with predicted human pharmacokinetic data could be used pre-clinically to predict potential drug-induced benign unconjugated hyperbilirubinemia in the clinic.

Population-Based Assessments of Clinical Drug-Drug Interactions: Qualitative Indices or Quantitative Measures?

Population-based assessments of drug-drug interactions have become more common since the introduction and acceptance of the population pharmacokinetic approach. Unlike traditional methods, population-based studies provide clinically relevant results that can be applied directly to a target patient population. Furthermore, population-based studies do not demand the traditional requirements of intensive pharmacokinetic sampling, rigorous inpatient stays, or stringent assessment schedules. As such, the population-based approach can effectively be used to confirm known drug-drug interactions and further characterize anticipated interactions. A prospectively designed analysis can also reveal drug-drug interactions that might otherwise have gone undetected with traditional methods. Ultimately, these results could help to alleviate clinicians' concerns about using widely marketed drugs in combination therapies and also reduce patients' risk of experiencing unacceptable side effects. This article intends to provide a balanced overview of the population-based approach and its merits, drawbacks, and potential utility in the assessment of drug-drug interactions during clinical drug development.

Population pharmacokinetics of intravenously and orally administered docetaxel with or without co-administration of ritonavir in patients with advanced cancer

AIM

Docetaxel has a low oral bioavailability due to affinity for P-glycoprotein and cytochrome P450 (CYP) 3A4 enzymes. Inhibition of the CYP3A4 enzymes by ritonavir resulted in increased oral bioavailability. The aim of this study was to develop a population pharmacokinetic (PK) model and to evaluate and quantify the influence of ritonavir on the PK of docetaxel.

METHODS

Data from two clinical trials were included in the data analysis, in which docetaxel (75 mg m(-2) or 100 mg) had been administered intravenously or orally (10 mg or 100 mg) with or without co-administration of oral ritonavir (100 mg). Population modelling was performed using non-linear mixed effects modelling. A three-compartment model was used to describe the i.v. data. PK data after oral administration, with or without co-administration of ritonavir, were incorporated into the model.

RESULTS

Gut bioavailability of docetaxel increased approximately two-fold from 19 to 39% (CV 13%) with ritonavir co-administration. The hepatic extraction ratio and the elimination rate of docetaxel were best described by estimating the intrinsic clearance. Ritonavir was found to inhibit in a concentration dependent manner the intrinsic clearance of docetaxel, which was described by an inhibition constant of 0.028 microg ml(-1) (CV 36%). A maximum inhibition of docetaxel clearance of more then 90% was reached.

CONCLUSIONS

A PK model describing both the PK of orally and intravenously administered docetaxel in combination with ritonavir, was successfully developed. Co-administration of ritonavir lead to increased oral absorption and reduced elimination rate of docetaxel.

Influence of pharmacogenetics on indinavir disposition and short-term response in HIV patients initiating HAART

AIMS

To assess the relationship between genetic polymorphisms and indinavir pharmacokinetic variability and to study the link between concentrations and short-term response or metabolic safety.

METHODS

Forty protease inhibitor-naive patients initiating highly active antiretroviral therapy (HAART) including indinavir/ritonavir and enrolled in the COPHAR 2-ANRS 111 trial were studied. At week 2, four blood samples were taken before and up to 6 h following drug intake. A population pharmacokinetic analysis was performed using the stochastic approximation expectation maximization (SAEM) algorithm implemented in MONOLIX software. The area under the concentration-time curve (AUC) and maximum (C(max)) and trough concentrations (C(trough)) of indinavir were derived from the population model and tested for their correlation with short-term viral response and safety measurements, while for ritonavir, these same three parameters were tested for their correlation with short-term biochemical safety

RESULTS

A one-compartment model with first-order absorption and elimination best described both indinavir and ritonavir concentrations. For indinavir, the estimated clearance and volume of distribution were 22.2 L/h and 97.3 L, respectively. The eight patients with the *1B/*1B genotype for the CYP3A4 gene showed a 70% decrease in absorption compared to those with the *1A/*1B or *1A/*1A genotypes (0.5 vs. 2.1, P = 0.04, likelihood ratio test by permutation). The indinavir AUC and C(trough) were positively correlated with the decrease in human immunodeficiency virus RNA between week 0 and week 2 (r = 0.4, P = 0.03 and r = -0.4, P = 0.03, respectively). Patients with the *1B/*1B genotype also had a significantly lower indinavir C(max) (median 3.6, range 2.1-5.2 ng/mL) than those with the *1A/*1B or *1A/*1A genotypes (median 4.4, range 2.2-8.3 ng/mL) (P = 0.04) and a lower increase in triglycerides during the first 4 weeks of treatment (median 0.1, range -0.7 to 1.4 vs. median 0.6, range -0.5 to 1.7 mmol/L, respectively; P = 0.02). For ritonavir, the estimated clearance and volume of distribution were 8.3 L/h and 60.7 L, respectively, and concentrations were not found to be correlated to biochemical safety. Indinavir and ritonavir absorption rate constants were found to be correlated, as well as their apparent volumes of distribution and clearances, indicating correlated bioavailability of the two drugs.

CONCLUSION

The CYP3A4*1B polymorphism was found to influence the pharmacokinetics of indinavir and, to some extent, the biochemical safety of indinavir.

Mechanism of ritonavir changes in methadone pharmacokinetics and pharmacodynamics: II. Ritonavir effects on CYP3A and P-glycoprotein activities

Ritonavir diminishes methadone plasma concentrations, an effect attributed to CYP3A induction, but the actual mechanisms are unknown. We determined short-term (2-day) and steady-state (2-week) ritonavir effects on intestinal and hepatic CYP3A4/5 (probed with intravenous (IV) and oral alfentanil (ALF) and with miosis) and P-glycoprotein (P-gp) (fexofenadine), and on methadone pharmacokinetics and pharmacodynamics in healthy volunteers. Acute ritonavir increased the area under the concentration-time curve (AUC)(0-infinity)/dose ratio (ritonavir/control) for oral ALF 25-fold. Steady-state ritonavir increased the AUC(0-Infinity)/dose ratio for IV and oral ALF 4- and 10-fold, respectively; reduced hepatic extraction (from 0.26 to 0.07) and intestinal extraction (from 0.51 to 0); and increased bioavailability (from 37 to 95%). Acute ritonavir inhibits first-pass CYP3A > 96%. Chronic ritonavir inhibits hepatic CYP3A (> 70%) and first-pass CYP3A (> 90%). Acute and steady-state ritonavir increased the fexofenadine AUC(0-infinity) 2.8- and 1.4-fold, respectively, suggesting P-gp inhibition. Steady-state compared with acute ritonavir caused mild apparent induction of P-gp and hepatic CYP3A, but net inhibition still predominated. Ritonavir inhibited both intestinal and hepatic CYP3A and drug transport. ALF miosis noninvasively determined CYP3A inhibition by ritonavir.

Simultaneous population pharmacokinetic model for lopinavir and ritonavir in HIV-infected adults

BACKGROUND

Lopinavir is a protease inhibitor indicated for the treatment of HIV infection. It is coformulated with low doses of ritonavir in order to enhance its pharmacokinetic profile. After oral administration, plasma concentrations of lopinavir can vary widely between different HIV-infected patients.

OBJECTIVE

To develop and validate a population pharmacokinetic model for lopinavir and ritonavir administered simultaneously in a population of HIV-infected adults. The model sought was to incorporate patient characteristics influencing variability in the drug concentration and the interaction between the two compounds.

METHODS

HIV-infected adults on stable therapy with oral lopinavir/ritonavir in routine clinical practice for at least 4 weeks were included. A concentration-time profile was obtained for each patient, and blood samples were collected immediately before and 1, 2, 4, 6, 8, 10 and 12 hours after a morning lopinavir/ritonavir dose. Lopinavir and ritonavir concentrations in plasma were determined by high-performance liquid chromatography. First, a population pharmacokinetic model was developed for lopinavir and for ritonavir separately. The pharmacokinetic parameters, interindividual variability and residual error were estimated, and the influence of different patient characteristics on the pharmacokinetics of lopinavir and ritonavir was explored. Then, a simultaneous model estimating the pharmacokinetics of both drugs together and incorporating the influence of ritonavir exposure on oral clearance (CL/F) of lopinavir was developed. Population analysis was performed using nonlinear mixed-effects modelling (NONMEM version V software). The bias and precision of the final model were assessed through Monte Carlo simulations and data-splitting techniques.

RESULTS

A total of 53 and 25 Caucasian patients were included in two datasets for model building and model validation, respectively. Lopinavir and ritonavir pharmacokinetics were described by one-compartment models with first-order absorption and elimination. The presence of advanced liver fibrosis decreased CL/F of ritonavir by nearly half. The volume of distribution after oral administration (Vd/F) and CL/F of lopinavir were reduced as alpha1-acid glycoprotein (AAG) concentrations increased. CL/F of lopinavir was inhibited by ritonavir concentrations following a maximum-effect model (maximum inhibition [Imax] = 1, concentration producing 50% of the I(max) [IC50] = 0.36 mg/L). The final model appropriately predicted plasma concentrations in the model-validation dataset with no systematic bias and adequate precision.

CONCLUSION

A population model to simultaneously describe the pharmacokinetics of lopinavir and ritonavir was developed and validated in HIV-infected patients. Bayesian estimates of the individual parameters of ritonavir and lopinavir could be useful to predict lopinavir exposure based on the presence of advanced liver fibrosis and the AAG concentration in an individual manner, with the aim of maximizing the chances of treatment success.

Bioavailability and Pharmacokinetic Model for Ritonavir in the Rat

The aim of this study is to investigate in vivo the oral bioavailability of ritonavir and to evaluate the pharmacokinetic model that best describes the plasma concentration behavior after oral and intravenous administration. Male Wistar rats were intravenously administered at 3 mg dose of pure ritonavir and oral administered at 4.6 +/- 2.5 mg of diluted Norvir. Blood samples were taken by means of the jugular vein for a 24 h period of time. An analytical high-performance liquid chromatography (HPLC) technique was developed in order to quantify ritonavir plasma concentrations. A nonlinear modeling approach was used to estimate the pharmacokinetic parameters of interest. Results showed that a two-compartmental model with zero-order kinetic in the incorporation process of ritonavir into the body better fitted intravenous and oral data. The estimated oral bioavailability by means of noncompartmental and compartmental approaches resulted in 74% and 76.4%, respectively. These values confirm the ones obtained by other authors in the rat. In conclusion, a zero-order kinetic in the incorporation process at the administered doses suggests the saturation of the possible specialized transport mechanisms involved in the incorporation of ritonavir into the body. These results could justify the use of low doses of ritonavir when improving the bioavailability of other protease inhibitors (PIs) is required.

Population pharmacokinetics of indinavir alone and in combination with ritonavir in HIV-1-infected patients

AIMS

The aim of the study was to characterize the population pharmacokinetics of indinavir, define the relationship between the pharmacokinetics of indinavir and ritonavir, and to identify the factors influencing the pharmacokinetics of indinavir alone or when given with ritonavir.

METHODS

HIV-1-infected patients being treated with an indinavir-containing regimen were included. During regular visits, 102 blood samples were collected for the determination of plasma indinavir and ritonavir concentrations. Full pharmacokinetic curves were available from 45 patients. Concentrations of indinavir and ritonavir were determined by liquid chromatography coupled with electrospray tandem mass spectrometry. Pharmacokinetic analysis was performed using nonlinear mixed effect modelling (NONMEM).

RESULTS

The disposition of indinavir was best described by a single compartment model with first order absorption and elimination. Values for the clearance, volume of distribution and the absorption rate constant were 46.8 l h(-1) (24.2% IIV), 82.3 l (24.6% IIV) and 02.62 h(-1), respectively. An absorption lag-time of 0.485 h was detected in patients also taking ritonavir. Furthermore this drug, independent of dose (100-400 mg) or plasma concentration, decreased the clearance of indinavir by 64.6%. In contrast, co-administration of efavirenz or nevirapine increased the clearance of indinavir by 41%, irrespective of the presence or absence of ritonavir. Female patients had a 48% higher apparent bioavailability of indinavir than males.

CONCLUSIONS

The pharmacokinetic parameters of indinavir were adequately described by our population model. Female gender and concomitant use of ritonavir and non-nucleoside reverse transcriptase inhibitors strongly influenced the pharmacokinetics of this drug. The results support the concept of ritonavir boosting, maximum inhibition of indinavir metabolized being observed at 100 mg.

Analyzing multi-response data using forcing functions

INTRODUCTION

Two analytic strategies can be taken to the analysis of multi-response data: a multivariate output model can be fit to all the response components simultaneously (SIM), or each response component can be fit separately to a univariate output model, conditioning in some way on the non-modeled components, the so-called forcing function approach (FFA). Focusing on a special case of multi-response model corresponding to a (pharmacokinetic) physiological f low model (PFM), the aims of this study are to (i) provide an algorithm for applying FFA to multi-response data from a PFM; (ii) examine the performance of FFA vs. SIM under optimal conditions for both, and in the presence of model misspecification; (iii) make recommendations regarding the use of FFA for multi-response data analysis.

METHODS

The basic PFM we use (variants of the basic model are used for simulation) has four homogenous compartments among which drug distributes. All are sampled arterial blood (A), non-eliminating tissue (N), eliminating tissue (E), and venous blood (V), which is also the drug dosing site. Parameters are blood f low rates to E and N, volumes of distribution of A, E, N, and V, elimination rate constant from E, and observation error variances. Observations from a generic individual under various study designs and parameter values are simulated. Using data-analytic models (DAM) both the same as, and different than the data simulation model (DSM), SIM fits the PFM to all data simultaneously; FFA first fits each type of response (one per tissue) separately, approximating the tissue's input by linearly interpolating the observed concentrations from the donor tissue(s), estimates the identifiable parameter combinations for the response type, and then solves the simultaneous equations linking these across tissues, to obtain the primary model parameters of interest. This simulation and analysis steps are repeated to generate reliable performance statistics. Performances are compared with respect to parameter estimation error (when DAM and DSM are identical), and interpolated prediction error (when DAM and DSM are/are-not identical). The ability of SIM and FFA to identify the correct analytic model is also examined by comparing their failure rates in rejecting the wrong DAM.

RESULTS

The parameter estimation errors with FFA are generally about two times greater than those with SIM when the DAM is identical to the DSM. The prediction errors of FFA are about ten times greater than those of SIM when the DAM is identical to the DSM, and are about three times greater when the two are different. However, SIM fails to identify the correct model twice as often as FFA.

CONCLUSIONS

Despite its greater convenience for model building, and its clear advantages for model identification, FFA's final parameter estimates cannot be trusted when the multi-response system being modeled involves feedback. The size of the ratio of the two FFA residuals (obtained from the response-specific fits and from predictions made with the final FFA parameters) can, however, be used to indicate when FFA's final estimates may be trustworthy.

Use of nonlinear mixed effect modeling for the intestinal absorption data: Application to ritonavir in the rat

The aim of this study is to investigate in situ the mechanisms involved in the gastrointestinal absorption of ritonavir in the rat, as an animal model for preclinical studies of anti-HIV agents in vivo. Four ritonavir solutions (40, 27, 13 and 7 microM) in the presence of 1% dimethylsulfoxide (DMSO) were perfused in the small intestine of anaesthetised rats. Effects of DMSO on the intestinal permeability were investigated using solutions containing antipyrine 1.33 mM and ritonavir 7 microM with and without 1% of DMSO. Antipyrine and ritonavir transport was not modified in the presence of 1% of DMSO. The population pharmacokinetic parameters of the ritonavir intestinal transport were obtained by means of nonlinear mixed effect modelling approach according to a nonlinear absorption and nonlinear secretion. The absorption and secretion kinetic parameters for ritonavir were: Vm=47.6 microM/h; Km=8.77 microM; Vms=3.66 microM/h and Kms=0 microM. The interindividual variability found to ritonavir Vm 13.1%, and the residual variability was 8.98%. The Kms value support the saturation of the carrier at the range of concentrations of ritonavir assayed. The interindividual variability value of the Vm could explain, at least in part, the variability in absorption rate constants observed.

Application and impact of population pharmacokinetics in the assessment of antiretroviral pharmacotherapy

Population pharmacokinetics has been an important technique used to explore and define relevant sources of variation in drug exposure and response in patient populations. This has been especially true in the area of antiretroviral therapy where the assurance of adequate and sustained drug exposure of multiple agents is highly correlated with therapeutic success. Population pharmacokinetic analyses across the four drug classes and 20 US FDA-approved products used to treat HIV have been published to date. The published reports were predominantly based on actual clinical trials conducted in HIV-infected patients with one or more agents administered. Modelling and simulation approaches have been used in the evaluation of antiretroviral agent outcomes incorporating problematic design and analysis factors such as sparse plasma sampling, data imbalance and censored data. Additional benefits of population modelling approaches applied to the investigation of antiretroviral agents include the ability to assess dosing compliance, understanding and quantifying drug-drug interactions in order to select dosing regimens and the screening of new drug candidates. Pharmacokinetic/pharmacodynamic models have been used to characterise the relationship between drug exposure and virological and immunological response, and to predict clinical outcome. These models offer the best opportunity for individualising and optimising patient therapy, particularly when adjusted for adherence/compliance. The impact of population pharmacokinetics in the area of antiretroviral therapy can be directly assessed by its role in the validation of surrogate markers such as viral RNA load, therapeutic drug monitoring and the management of individual patient outcomes via exposure-toxicity relationships. Each of these population pharmacokinetic outcomes has contributed to the current regulatory environment, specifically in the area of accelerated approval of new antiretroviral agents.

Development and validation of a population pharmacokinetic model for ritonavir used as a booster or as an antiviral agent in HIV-1-infected patients

AIMS

The aim of this study was to develop and validate a population pharmacokinetic model of ritonavir, used as an antiviral agent or as a booster, in a large patient population and to identify factors influencing its pharmacokinetics.

METHODS

Ambulatory HIV-1-infected patients from the outpatient clinic of the Slotervaart Hospital, Amsterdam, the Netherlands, who were being treated with a ritonavir-containing regimen were included. During regular visits, blood samples were collected for the determination of ritonavir plasma concentrations and several clinical chemistry parameters. Furthermore, complete pharmacokinetic curves were available in some patients. Single and multiple compartment models with zero-order and first-order absorption, with and without absorption lag-time, with linear and nonlinear elimination were tested, using nonlinear mixed effect modelling (NONMEM). Pharmacokinetic parameters and interindividual, interoccasion and residual variability were estimated. In addition, the influence of several factors (e.g. patient characteristics, comedication) on the pharmacokinetics of ritonavir was explored.

RESULTS

From 186 patients 505 ritonavir plasma concentrations at a single time-point and 55 full pharmacokinetic profiles were available, resulting in a database of 1228 plasma ritonavir concentrations. In total 62% of the patients used ritonavir as a booster of their protease inhibitor containing antiretroviral regimen. First order absorption in combination with one-compartment disposition best described the pharmacokinetics of ritonavir. Clearance, volume of distribution and absorption rate constant were 10.5 l h(-1) (95% prediction interval (95% PI) 9.38-11.7), 96.6 l (95% PI 67.2-121) and 0.871 h(-1) (95% PI 0.429-1.47), respectively, with 38.3%, 80.0% and 169% interindividual variability, respectively. The interoccasion variability in the apparent bioavailability was 59.1%. The concomitant use of lopinavir resulted in a 2.7-fold increase in the clearance of ritonavir (P value < 0.001). No patients characteristics influenced the pharmacokinetics of ritonavir.

CONCLUSIONS

The pharmacokinetic parameters of ritonavir were adequately described by our population pharmacokinetic model. Concomitant use of the protease inhibitor lopinavir strongly influenced the pharmacokinetics of ritonavir. The model has been validated and can be used for further investigation of the interaction between ritonavir and other protease inhibitors.