Therapeutic drug monitoring (TDM) can be used for the assessment of adequate drug exposure and guides clinicians to perform dosage adjustment at risk situations to prevent drug toxicity or therapeutic failure. Even though TDM is not recommended in routine practice, there is increasing evidence about the importance of TDM guided pharmacotherapy in optimizing ART outcomes in specific patients.
In this chapter, we present an innovative concept to describe how population pharmacokinetic (Pop-PK) approach can be implemented to develop clinically useful tools.
Based essentially on meta-analysis techniques, we pooled the parameters of the published models into a summary mean value and by simulation; we derived the reference curves of plasma concentration over time. These curves can be used to interpret individual drug levels whenever needed.
Pragmatic Approach for Interpreting Antiretroviral Drug Concentrations Based on a Systematic Review of Population
Pharmacokinetic Studies
M.Arab-Alameddine1,2, T. Buclin1, N. Widmer1, L.A. Décosterd1 A. Telenti3 and C.Csajka1,2
1Division of Clinical Pharmacology and Toxicology, University Hospital Center, University of Lausanne
2Department of Pharmaceutical Sciences, University of Geneva, University of Lausanne; Switzerland
3Institute of Microbiology, University Hospital Center, University of Lausanne, Switzerland
In Preparation
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Introduction
The giant advances in antiretroviral therapy (ART) in the last 2 decades have transformed HIV infection from a deadly disease into a manageable chronic condition. Despite the good efficacy of antiretroviral drugs, the response to ART varies greatly between the patients and a large proportion of HIV-infected patients does not achieve or maintain adequate virological suppression [1]. Therapeutic effectiveness and good tolerability of ART have become important issues for HIV lifelong treatment.
Achieving target plasma concentrations is vital for HIV therapy as suboptimal concentrations can lead to the emergence of resistance and high concentrations may lead to toxicity. Since most antiretroviral drugs exhibit a high interpatient variability in their kinetics, therapeutic drug monitoring (TDM) has thus been proposed for the assessment of adequate antiretroviral drug exposure and sometimes adherence. The role of TDM in improving virologic response is however a subject of a big controversy. Despite the fact the antiretroviral drugs are good candidate for TDM, the relationships with clinical outcomes and significant benefits have not been demonstrated in large randomized trials. A recent meta-analysis concluded that the current available evidence was insufficient to support the use of routine TDM. Currently, TDM of antiretroviral drugs is implemented in routine practice in some European countries [2, 3], yet it is not the case for the United States [4] where TDM is only recommended in some specific situations such as drug interactions, co-infection (Hepatitis B, Hepatitis C, malaria, tuberculosis), suboptimal compliance, pathophysiological alterations. TDM may also improve ART response in subgroups of patients like pediatric and adolescent patients, pregnant women and patient with renal or hepatic failure [1, 4-6].
Minimal target trough concentrations associated with virologic efficacy in patients infected with the wild-type virus are readily available for first generation protease inhibitors (PI’s) and non nucleoside reverse transcriptase inhibitors (NNRTI’s) [4] and the TDM of these drugs may thus contribute to improve clinical outcomes [1]. No clear association between drug exposure and therapy outcomes has been demonstrated with more recent antiretroviral compounds such as raltegravir, etravirine and darunavir. However, due the potential of drug interactions and the large reported pharmacokinetic variability, the clinical judgment alone may not be sufficient to get hold of satisfactory response in some patients exhibiting unpredicted extremely high or extremely low plasma levels. Therefore, TDM interventions may play an important role for dosage adjustment in selected groups of patients under raltegravir, etravirine and darunavir despite the fact that these molecules don’t adhere to all prerequisites for TDM.
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In the view of the dispersion of drug levels under standard dosage regimen, numerous population pharmacokinetics (Pop-PK) have been performed that aimed at quantifying variability and at identifying sources of variability. Some concentration-effect studies have also related drug levels with markers of efficacy and toxicity, thus offering optimized therapeutic targets. In addition, Pop-PK studies can be used to predict the range of drug concentration to be expected in the target population of patients under specific assumptions of doses or other influences and are a prerequisite for Bayesian TDM.
The numerous Pop-PK analyses of NNRTIs and PIs published in the literature provide a wealth of information on the pharmacokinetics and variability of these drugs. The collection of pharmacokinetic parameters with variability across all available studies would offer the possibility to elaborate a practical tool for the interpretation of drug levels measurements, which could help clinicians in dosage individualization.
The objectives of this study is to review the published Pop-PK of NNRTIs and PIs an integrase inhibitors in order to derive reference percentile curves for adults patients that could help in the TDM-guided dosage adjustment of these drugs. In addition, the reported factors showing some influence on their pharmacokinetics have been outlined in order to provide a full description of the known influences on the drugs’s kinetics.
Material & Methods
Selection of studies and data retrieval
The drugs of interest for this review are the NNRTI’s efavirenz, nevirapine and etravirine, the PI’s lopinavir, atazanavir and darunavir, and the integrase inhibitor raltegravir. A systematic search of Pop-PK studies on those three drugs was performed in Pubmed using the terms
“population pharmacokinetics” “non-linear mixed effect modeling” and “NONMEM”. Pop-PK published as research articles or abstracts were included if they contained the necessary pharmacokinetic information. The study populations included healthy and HIV- infected adult individuals, and excluded special populations like pregnant women and pediatric patients. In each study, the final structural models without covariate effect, the mean pharmacokinetic parameters with their relative standard errors (RSE) and the inter- and intra-individual variability were retrieved. In case only the final model estimates including the covariate effect were reported, the average value of the pharmacokinetic parameter based on the median values for continuous covariates and on absence of effect for dichotomous covariates was derived.
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Summary of models and pharmacokinetic parameters
The description of the drug concentration vs. time data used a one-compartment (1 CMT) model in most studies, with the exception of few studies that included large datasets with intense pharmacokinetic sampling that used a 2 compartment (2CMT) model. We assumed that the use of one versus two compartments was related mostly to different study design (i.e. rich versus sparse sampling) and we simplified those models to a single compartment model. This assumption was based on the fact that the first distribution phase is very rapid and not determinant for the description of the terminal elimination phase, mostly relevant for the scope of this project.
The derivation of the average population pharmacokinetic parameters across selected studies was performed using a meta-analysis approach. As this meta-analysis accumulates data from studies performed by independent researchers, using heterogeneous mixes of populations and different study designs, we can assume that these data are not equivalent.
Therefore, a random effect meta-analysis was chosen to address the variation in the data and to perform the summary of the pharmacokinetic parameters [135].
The one-compartment model was built on the following parameters: clearance (CL), volume of distribution of the terminal phase (Vz) and the absorption constant (ka). CL was readily accessible from all studies. For models using a one-compartment model, Vz equals the central volume of distribution (Vc); for models using a two-compartment model, Vz was derived using the following relationship:
z z
V CL
=
λ
(eq.1)
where λz is the terminal disposition rate constant that is derived from Vc, from the peripheral volume of distribution (Vp), and the inter-compartmental clearance (Q) using classical equations.
Most of the studies reported that a first order absorption model best described the absorption phase. Some studies however reported zero order absorption, sequential first and zero order absorption with or without a lag-time. In order to harmonize the results, we derived in each of a mean absorption time (MAT) and assumed that MAT was inversely related to Ka (MAT = 1/Ka). MAT was calculated using the following relationships:
MAT=D1
2 (eq. 2)
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MAT= 1
Kaoriginal+Lagtime
(eq.3)
MAT= 1
Kaoriginal+D1
2 +Lagtime
(eq.4)
where D1 is the estimate of the duration of the zero order absorption model and Kaoriginal is the original estimate of KA.
Apparent CL, apparent volume of distribution of the terminal phase (Vz) and the absorption rate constant (Ka) were then pooled into a summary mean value, weighted by the number of plasma levels reported for each studies, using the following equation:
Log(θs)=Log(θi)×ni ni
(eq.5)
where θS is the summary mean apparent CL, Vz and ka derived from all the studies, i is the individual pharmacokinetic parameter CLi, Vzi, kai reported or derived from each Pop-PK, ni is the number of plasma levels for each study. In case a study reported 2 distinct values for the same pharmacokinetic parameter, both values were used for the computation of the summary mean value, but the weight assigned for each value was limited to the half of the weight of the study.
Standard errors on estimated parameters were directly retrieved from the published papers.
For derived parameters (Vz and MAT), the RSE was computed by applying the error propagation method (ref). The standard error of the summary mean (SEM) was computed using the equation:
SEM= SDPOOL2 ni
+SD2Inter−Study
NStudy
(eq.6)
where SDpool is the pooled standard deviation of the parameters found in each studies and calculated using the equation:
SDPOOL= SDi×(ni−1) (ni−1)
(eq.7)
where SDi is the individual standard deviation of the parameter in each study, ni is the individual number of plasma levels in each study. SD Inter-Study represents the variance of the pharmacokinetic parameters between the studies, and finally N Study is the number of studies.
Inter-individual variability on CL, Vc and ka were directly collected from the published studies when available. An inter-individual variability on both Vc and Vp was observed in a single
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study that used a two-compartment model. A sensitivity analysis allowed us to identify that the inter-individual variability of Vp had the most relevant impact on the estimation of Vz and a inter-individual variability on this estimate was chosen for the summary statistics.
Finally, summary mean inter individual variability (OmegaS) of the pharmacokinetic were calculated and weighted by the number of individuals included in the study, as follows:
OmegaS= Omegai×npatients npatients
(eq.8)
where OmegaS is the inter-individual variability of each pharmacokinetic parameter within each study and npatients is the number of patients in each study.
We chose to report in the summary of the intra-individual variability (sigmaS) only models with a proportional error since it can be assumed that a heteroscedastic error models is more representative of the concentrations over the whole concentration range. The intra-individual variability was calculated as follows:
SigmaS= Sigmai×ni ni
(eq.9)
where SigmaS is the residual error of each study and ni is the number of plasma levels in each study reporting a proportional residual error.
Simulations of reference percentile curves
Simulations of the three NNRTI, three PI’s and one integrase inhibitor, based on the summary pharmacokinetic parameters were performed in 1000 individuals, using the standard approved dosage regimens available for each drug. The median as well as the 10th, 25th, 75th and 90th percentiles over a dosing interval were retrieved to construct the percentile curves graphs. The simulations were performed with NONMEM® (version 7.1, NM-TRAN version II)[22]. The percentiles were derived using the statistical software R (R.2.12.1 http://www.R-project.org).
Relationships between drug exposure and target concentrations
In order to relate drug exposure with target concentrations associated with improved outcome, a literature search was performed to collect evidence relating concentrations and efficacy of toxicity outcome. Target minimal concentrations (Cmin)have been proposed for the older generation of drugs and HIV naïve patients. For newer drugs and experienced patients, target Cmin are unknown. Other cut-off concentrations such as protein adjusted 50% (IC50) or 95% (IC95) inhibitory concentrations can be used in clinical practice. Some evidence
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suggests indeed that the individual Cmin may need to exceed the IC50 by more than 2- folds to be effective [6]. Little is known regarding the upper limit of concentrations that might be associated with toxicity, especially for the new generation of drugs that are well tolerated.
Reported Cmin values associated with efficacy or toxicity, or IC50 and IC95 have been added to the percentile graphs.
Model validation
Forest-plots were used to graphically assess the concordance between individual estimates of the pharmacokinetic parameters, their relative standard errors and inter-individual variances with their summary mean parameters. The plots were generated with GraphPad Prism (Version 4.00 for Windows, GraphPad Software, San Diego California USA, www.graphpad.com).
In addition, in order test the robustness of our approach and compare predictions the trajectory of concentrations over a dosing interval, we performed simulations in 1000 individuals based on one hand on the summary pharmacokinetic parameters derived by the meta-analysis approach and on the other hand using the parameter estimates of each of the study taken separately. We chose efavirenz 600 mg once daily at steady state as a probe drug. The mean percentage difference between the predicted percentiles i.e. concentrations at each time point, based on the parameter estimated of the published studies and the summary model has been calculated.
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Results
I - Non Nucleoside Reverse Transcriptase Inhibitors 1. Efavirenz (EFV)
Efavirenz (EFV) is among the preferred regimens for first line therapy in HIV treatment guidelines because it exhibits excellent virological and immunological response [9, 10]. Once daily dosage promotes adherence, which is a crucial element since first generation NNRTI have a low barrier to resistance that rapidly develops after virologic failure, and is mainly attributed to a single mutation in the reverse transcriptase [5, 10, 11].
Six Pop-PK studies have been reported in the literature [12-17]. A 1-CMT model with first order absorption and elimination were generally used to fit the data. Two studies reported a 2-CMT model with more intensive pharmacokinetic sampling. Furthermore, one study found a minimal increase in EFV concentration in the early absorption phase that was best described by a transit compartment model [15]. The pharmacokinetic parameters as well as inter and intra-individual variability obtained from these models are relatively coherent. EFV absorption is fast and the derived MAT ranged from 0.3 to 2 hours, suggesting important differences in the estimation of the absorption rate. The pharmacokinetic parameters obtained from the Pop-PK models are summarized in Table 1 and their forest plots are depicted in Figure 1a.
The percentile representation of plasma concentration over time is presented in Figure 1b.
EFV is characterized by its frequent induction (25 -70 %) of central nervous system (CNS) side effects [18]. Early CNS symptoms include dizziness, depression, anxiety, irritability, headache and sleep disturbance [9, 10, 18]. The relationship between EFV plasma concentrations and clinical efficacy and toxicity was observed in several studies [19-22]. A therapeutic range of 1000 mg to 4000 mg to optimize clinical efficacy while minimizing the CNS side effects has been proposed [14, 22] Targeting this interval guided by TDM has been shown to improve neuropsychiatric symptoms and reduced treatment discontinuation. [23-26]. EFV is extensively metabolized primarily by hepatic cytochrome P450 (CYP) 2B6 with partial involvement of CYP3A4 and CYP2A6 [27]. EFV is a dose-dependent inducer of CYP2B6, CYP3A4 and P-gp [28-31] and is a CYP2B6 reversible inhibitor as well [32], therefore drug interactions are expected.
Several factors have been shown some influence on EFV drug concentrations. Among those, genetic polymorphisms of the cytochrome (CYP) CYP2B6 has been shown to have the strongest impact on EFV plasma concentrations and thus on treatment discontinuation [12, 25,
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30, 33-35]. Some evidence is in favor of the importance of CYP2A6 and CYP3A4/A5 minor metabolic pathways in EFV disposition, independently from CYP2B6, or in case of loss of function of this pathway [12, 27, 33, 36, 37]. Other non-genetic factors have been shown to affect EFV pharmacokinetics. Some studies postulated higher EFV exposure and more frequent therapy failure in females [15, 38-40] and non Caucasian HIV-infected patients owing to treatment discontinuation [17, 38]. However, the variation in EFV pharmacokinetics related to sex and ethnicity were attributed to the significant differences in CYP2B6 expression [41]. The influence of body weight on EFV plasma levels is not conclusive. Some studies did not find any relationship between EFV metabolism and body weight [33, 38] (whereas some others reported a direct correlation [13, 42-44]. EFV dose adjustment based on the patient’s weight is recommended by the US Department of Health and Human Services [4] but not by the manufacturer [18].
2. Nevirapine (NVP)
NVP, the first approved NNRTI is used either in combination with other antiretroviral agents for the treatment of HIV-1 infection or in a single prophylactic dose to prevent mother to child transmission [45]. One of the primary concerns of NVP use is the significant incidence of hepatotoxicity (3%) and cutaneous adverse reactions (9%) which could occasionally be life threatening due to Stevens-Johnson or toxic epidermal necrolysis transition syndrome (0.3%)
[46](Popovic 2010).
The evaluation of NVP pharmacokinetic profile using a Pop-PK approach has been performed by 11 research groups [16, 47-56]. A common 1 CMT model with first order absorption and elimination was identified in all the studies except for one [56] in which the absorption was modeled as a zero-order input, with the duration of the infusion (D1) fixed to 0.25 h (first sampling time point) for fast absorbers or estimated for slow absorbers. The pharmacokinetic parameters in these 11 models as well as their inter and intra-individual variability were in close agreement. Elsherbini et al. [51] reported a lag-time for NVP absorption and also a circadian variation in NVP pharmacokinetic parameters, suggesting a higher clearance and lag-time in the absorption at night while absorption rate constant was lower . Important differences in the absorption were noticed, with MAT ranging from 0.3 to 2.5 hours this difference could possibly be attributed to food effect.
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The pharmacokinetic parameters obtained from the Pop-PK models are summarized in Table 2 and their forest plots in Figure 2a. The percentile representation of plasma concentration over time of NVP is presented in Figure 2b.
Two main mechanisms have been suggested as causal elements of NVP idiosyncratic toxicity. The first is through direct immune response since NVP is contraindicated in patients with high CD4+ count [45]. The second is due to the NVP molecule itself though reactive intermediates and thus dose-dependent [57].
The association between higher NVP trough concentration and NVP toxicity yielded conflicting results [57] and was mostly related to hepatitis C co-infection. A case-control study indeed found that NVP levels higher than 6250 ng/ml were associated with hepatotoxicity [58]
and higher than 5300 ng/ml were associated with higher risk of rash [59]. On the other hand, the relationship between NVP plasma concentration and treatment efficacy in terms of achieving durable undetectable viral load has been demonstrated in several studies [53, 57, 60-64]. It is recognized that Cmin of at least 3000 ng/ml are required to attain virologic efficacy [4]. As NVP is a substrate and inducer of CYP3A4 and CYP2B6, strong inducers and inhibitors are awaited to influence NVP levels. Among other factors, several reports evidenced a clear effect of CYP2B6 polymorphism on NVP elimination [35, 37, 47, 52, 55, 65, 66], whereas some others did not confirm thoses results [67, 68]. Among the demographic factors, black ethnicity was associated with greater NVP exposure, probably related to genetic polymorphism [44, 69]. The impact of body weight on higher NVP plasma levels was also extensively reported [16, 50, 52, 54-56, 70]. One study identified female gender as a predictive factor of higher NVP plasma levels
and higher than 5300 ng/ml were associated with higher risk of rash [59]. On the other hand, the relationship between NVP plasma concentration and treatment efficacy in terms of achieving durable undetectable viral load has been demonstrated in several studies [53, 57, 60-64]. It is recognized that Cmin of at least 3000 ng/ml are required to attain virologic efficacy [4]. As NVP is a substrate and inducer of CYP3A4 and CYP2B6, strong inducers and inhibitors are awaited to influence NVP levels. Among other factors, several reports evidenced a clear effect of CYP2B6 polymorphism on NVP elimination [35, 37, 47, 52, 55, 65, 66], whereas some others did not confirm thoses results [67, 68]. Among the demographic factors, black ethnicity was associated with greater NVP exposure, probably related to genetic polymorphism [44, 69]. The impact of body weight on higher NVP plasma levels was also extensively reported [16, 50, 52, 54-56, 70]. One study identified female gender as a predictive factor of higher NVP plasma levels