Open Science Repository Pharmaceutics

doi: 10.7392/Pharmaceutics.70081939


Optimization of Efavirenz Dosing During Treatment of HIV-1 Infected Adults in an African Population


Jackson K. Mukonzo [1, 2] 

[1] Department of Pharmacology & Therapeutics, Makerere University College of Health Sciences, Uganda 

[2] The CTN, CIHR Canadian HIV Trials Network


Abstract

Efavirenz is the currently recommended first choice NNRTI for HIV patients receiving rifampicin TB co-treatment. While efavirenz exhibits a narrow therapeutic window, with plasma drug > 4 µg/ml and < 1 µg/ml being associated with central nervous system symptoms and increased virological failure respectively, it also exhibits significant inter-individual pharmacokinetic and pharmacodynamic variability. 

Africans, a population that is uniquely different in terms of frequency of SNPs relevant for efavirenz pharmacokinetics and treatment outcome is also most affected by both HIV and TB, therefore requiring rifampicin-efavirenz co-treatment. Genes known to predict efavirenz pharmacokinetics include CYP2B6 and ABCB1. Rifampicin is an enzyme inducer for CYP2B6. While CYPB6 SNP frequencies differ greatly for Africans compared to other populations, efavirenz dosing has not been optimized for the population. We aim to use pharmacokinetic-pharmacogenetic–pharmacodynamic modeling in NONMEM to predict efavirenz optimal doses for Uganda patients.

Keywords: efavirenz, dosing, African, HIV patients.



Citation: Mukonzo, J. K. (2013). Optimization of Efavirenz Dosing During Treatment of HIV-1 Infected Adults in an African Population. Open Science Repository Pharmaceutics, Online(open-access), e70081939. doi:10.7392/Pharmaceutics.70081939

Received: February 11, 2013

Published: March 11, 2013

Copyright: © 2013 Mukonzo, J. K. Creative Commons Attribution 3.0 Unported License.

Contact: research@open-science-repository.com



Background


Tuberculosis (TB) remains the most frequently encountered co-morbidity among HIV infected patients particularly in sub-Saharan Africa. Currently rifamycins constitute a cornerstone of anti-TB therapy. Use of rifamycins in patients co-treated with antiretroviral therapy (ART) is marred with drug-drug interactions caused by induction of the activity of the CYP enzymes and the transporter protein, P-gp. Among the rifamycins, rifampicin exhibits the greatest effect on activity of both CYP enzymes and P-gp. Non-availability of rifabutin in low resource countries is a limitation for both ART and anti-TB treatment options during the scourge of the two pandemics.

Efavirenz is currently the recommended first choice Non-Nucleoside Reverse Transcriptase Inhibitors (NNRTIs) for HIV patients particularly during co-treatment with rifampicin. Efavirenz, however, exhibits a narrow therapeutic window, as plasma drug > 4 µg/ml have been associated with more central nervous system (CNS) toxicity while the rate of virologic failure increases with concentration < 1 µg/ml . Inter-patient variations in efavirenz hepatic metabolism and P-gp mediated movement across plasma membranes are some of the main causes of inter-individual variations in the plasma concentration of the drug. Efavirenz undergoes oxidative hydroxylation primarily by CYP2B6 to 8-hydroxyefavirenz, which is its major metabolite, and to 7-hydroxyefavirenz as a minor metabolite .

CYP2B6 (516 G→T and c.136A→G) polymorphisms have been associated with differences in efavirenz plasma exposure and CNS toxicities in most populations suggesting that prescription of lower efavirenz doses could be warranted in subjects harboring the T/T genotype in order to minimize side effects without compromising the efficacy of the drug. Although there are conflicting reports on whether efavirenz is a substrate for P-gp, lower efavirenz plasma levels, better immune recovery in Caucasians who carried the 3435T/T genotype and lack of EFV resistance have been associated with polymorphisms in the ABCB1 gene , while ABCB1 c.4036 A→G influence both efavirenz plasma and intracellular concentrations.

Rifampicin induces expression and activity of CYP2B6 leading to 22 – 26% reduction of in vivo efavirenz plasma exposure . The increase in CYP2B6 activity due to rifampicin varies widely from 2.5 to 13-fold . Additionally, variability in efavirenz concentrations is greater in the presence of rifampicin than without rifampicin, a probable manifestation of inherent differences in the inducibility of CYP2B6 variants. Inter-individual differences in clearance of efavirenz during co-administered with rifampicin complicates decisions about efavirenz dose adjustment in settings of concurrent rifampicin-containing TB therapy .

Africans, a population that is uniquely different in terms of frequency of SNPs relevant for efavirenz pharmacokinetics and treatment outcomes, are also most affected by both HIV and TB, therefore requiring rifampicin-efavirenz co-treatment. The lack of efavirenz dose optimization may lead to clusters of African patients exhibition of sub or supra therapeutic efavirenz concentrations, leading to viral resistance or intolerable toxicities respectively.

We aim to optimize efavirenz HIV treatment outcomes among Africans using pharmacokinetic-pharmacogenetic–pharmacodynamic modeling in NONMEM. Model predictions of drug concentrations, associated virologic decay and immunologic recovery rates will be compared to provide efavirenz dosing recommendations for Ugandan HIV patients with and without TB co-treatment as well as the different CYP2B6 genotype clusters determined

Materials and methods

This study will utilize data that was collected from a recently completed clinical study for which a total 105 healthy volunteersand 263 patients ART naïve HIV patients, with (n =157) or without concomitant TB (n=106), attending Mulago and Butabika National referral and Bwera hospitals in Uganda, recruited during 2008 to 2009.

Study area and population


Study participant recruitment was conducted in three districts including two urban districts (Kampala and Mukono) and one rural district (Kasese). Kampala and Mukono districts are located in central Uganda. The population in Kampala and Mukono districts is estimated at 3.0 and 1.07 million people respectively and is largely drawn from the various rural parts of Uganda, bringing all tribes together. Kasese district is, on the other hand, located in the western part of the country bordering with the Democratic Republic of Congo. The district population that is estimated at 660,000 people is largely of the Konjo tribe.

Adult healthy volunteers(n=105), confirmed by clinical examination, renal and liver function tests, HIV and hepatitis B serology, were treated with single dose efavirenz (600mg) before intensive blood sampling of 0-72 hours was performed. Health volunteer participants were genotyped for 30 SNPs in the CYP2B6, CYP3A5 and ABCB1. SNPs genotyped for included CYP2B6 (*6 and *11), CYP3A5 (*3,*6 and*7) and ABCB1 (rs3842 and 3435C>T). HIV/AIDS ART naïve patients were recruited at the HIV clinics at Butabika National referral hospitals in Kampala (n= 60) and Bwera Hospital, Kasese (n= 47). TB co-infected HIV patient’s (n= 152) naïve to both ART and anti-TB therapy were recruited from the TB-HIV clinic at Mulago National Referral Hospital. All patients were treated for HIV with efavirenz 600mg daily doses in combination with zidovudine and lamivudine. Follow up period was 32 weeks. Patients co-infected with TB received rifampicin based anti-TB regimen for 6 month (2EHRZ/4HR) in addition to ART. CD4 analysis was performed to ensure eligibility for HIV treatment. All patient participants were genotyped for CYP2B6 (*6 and *11), CYP3A5 (*3,*6 and *7) and ABCB1 (rs 3842 and 3435C>T). Mid-dose plasma efavirenz concentrations samples were drawn on days 1 and 3 then weeks 1, 2, 4, 6, 8, 12, 16, 20, 24, 28 and 32. Twenty nine of the patients were intensively sampled within 24 hours of treatment initiation and compared with healthy volunteers. Patients were also evaluated for adverse events including neuropsychotic ones (sleep disorders, hallucinations and cognition impairment).

Laboratory analysis


HPLC determination of efavirenz

Plasma was prepared from blood samples by centrifugation at 3000 g for 10 min and stored at -70°C until high performance liquid chromatography (HPLC) analysis was performed. Plasma efavirenz was determined by reverse-phase with ultraviolet (UV) detection. The HPLC machine, Agilent series 1100, consisting of column compartment G1316A, Degasser G132A, Quat pump G1311A, an auto-sampler ALS, G1329A, and G1315B diode array detector was used. The mobile phase consisted of 30% acetonitrile, 30% methanol, 4 mmol l-1 potassium hydroxide and 10 mmol l-1 acetic acid (pH 4.3). Plasma proteins were precipitated with acetonitrile before centrifuging. Elution was performed at 0.80 ml min-1 for 3.5 min. The retention time for efavirenz was 2.42 min as detected at UV-VIS 1, 210 nm, UV-VIS 2,220 nm. The method was linear, with a within-day coefficient of variation of 3.2, 3.3 and 5.1% at concentrations of 2.0 mM (n = 17), 8.0 mM (n = 17), and 20 mM (n = 16), respectively, and a between-day coefficient of variation of 4.1% (n = 50). The limit of quantification for the method was set at 0.35 mM.



Genotyping


Restricted Fragment Length PCR (RFLP)

RFLP was used to genotype for C3435T, C1236T and G(A)2677T in the ABCB1 gene according to Tang et al and Cascorbi et al. PCRs were performed in a reaction mixture (25 ml) containing buffer X10, 0.125 ml Smart Taq hot DNA polymerase, 1.6–2.0 ml MgCl2 (25 mM l-1), 6.25 mM dNTPs and primers. Endonucleases Bsp1431, Eco01091 (Drall) and BshNi(HgiCl) were used to digest PCR products for C3435T, C1236T and G(A)2677T, respectively, followed by gel electrophoresis.



Micro-array assay

Genomic DNA was isolated using QIAgen kit and genotyped for SNPs in the three genes: CYP2B6, CYP3A5 and ABCB1 by minisequencing using micro-tag arrays method. Cyclic minisequencing reactions with fluorescently labeled dideoxynucleotides were performed using multiplex polymerase chain reaction (PCR) product as template and detection primers, designed to anneal immediately adjacent to and upstream of the SNP site. Primer sequences are available upon request. The microarrays were prepared using detection primers carrying unique 5′ tag sequences and oligonucleotides complementary to the tag sequence of the minisequencing primers, immobilized on a microarray. Hybridization was performed as according Lindroos et al and Lovmar et al. The QuantArray file was exported and analysed using the SNPSnapper analysis software, version 4.0 beta.


TaqMan method

Allelic discrimination reactions were performed using TaqMan (Applied Biosystems, CA, USA) genotyping assays: (C___7586657_20 for ABCB1 3435C>T, C___7817765_60 for ABCB1 rs3842T>C, C__29560333_20  for CYPB6 516G>T [CYP2B6*6 ] for CYP2B6 136A>G [CYP2B6*11], C__26201809_30 for CYP3A5 6986A>G [CYP3A5*3], C__30203950_10 for CYP3A5 14690G>A [CYP3A5*6]) and C__32287188_10 for CYP3A5 g.27131_27132insT [CYP3A5*7] on ABI 7500 FAST (Applied Biosystems, Foster City, CA). The final volume for each reaction was 10 μl, consisting of 2x TaqMan Universal PCR Master Mix (Applied Biosystems), 20 X drug metabolising genotype assay mix and 10 ng genomic DNA. The PCR profile consisted of an initial step at 50oC for 2 min and 50 cycles with 95oC for 10 minutes and 92oC for 15 sec.

Modeling


Data, including efavirenz pharmacokinetic data, pharmacogenetic data (CYP2B6 (*6 and *11), ABCB1 c.4046A→T), pharmacodynamic data (CD4 T-cell counts, viral load, liver, renal and CNS profiles) and demographic profiles collected from healthy volunteers, TB/ HIV or HIV alone patients will be pooled together before performing population pharmacokinetic/pharmacodynamic modeling to predict optimal efavirenz doses during and without TB co-treatment and for individual CYP2B6 genotypes. Data modeling will be performed using first order conditional estimation with interaction (FOCE-I) in NONMEM 7. Model discrimination will be based on relative objective function values (OFV), precision of parameter estimates and goodness-of-fit plots, including a visual predictive check.

Model development


From existing literature, a two compartment model will be applied in the current project. Clearance and volume related parameters will be scaled allometrically by body weight and centered to the median body weight (Eq. 1).


 Equation 1


Pi being the individual parameter estimate and Ppop the parameter estimate for a patient with median body weight. The scaling factor will be set a priori to ¾ for clearances (i.e., Cl and Q) and to 1 for volumes of distribution. A variety of absorption models will be tested in the model development process, including: 1) a single first-order process, 2) sequential first-order processes, 3) a sequential zero-order followed by first-order process, and 4) a transit absorption model where the number of theoretical absorption compartments (NT) and the transit rate constant (ktr) are estimated. Upon repeated administration, autoinduction of efavirenz clearance occurs to different degrees depending upon the CYP2B6 genotype. Initially, induction in clearance will be modeled as a time-dependent change in enzyme level from a pre-induced clearance (Clpre) to the post-induction value (Clmax). The model will assume that clearance is correlated to enzyme level, which increases exponentially with time. Clearance at any time, t, (Clt) will thus be described as:


 Equation 2


The parameters tlag and kind represent the time before start of induction and the first-order rate constant describing the rate of induction, respectively.


Alternatively, clearance will be described using a time dependent Hill-model structure:


 Equation 3


Where Emax represents the maximum fractional increase in enzyme levels, T50 the time after the lag-time that induction is half-complete, and tlag is the lag time for the initiation of induction. To account for CYP2B6 genotype differences in induction using either model, Clmax or Emax will be allowed to differ by genotype. The model will be flexible enough for the best predictions to be made.

Inter-individual variability

Inter-individual variability (IIV) in pharmacokinetic parameters will be modeled using log-normally distributed errors (Eq 4):


 Equation 4


Where Ppop is the population typical parameter estimate, and is a random normal deviate with mean = 0 and variance = . IIV parameters will not be retained in the model if they lead to numerical difficulties in minimization or the covariance step, or when they have inadequate precision as seen by large standard errors (SE).

Covariate analysis

Covariate analysis will be performed using a forward-selection (α=0.05) followed by backward elimination (α=0.01) method. Each covariate-parameter relationship will first be tested in a univariate manner. Covariates with one degree of freedom will be included in the forward selection if they reduce the OFV by at least 3.84, corresponding to a p-value of <0.05. The full covariate model will be reached when the addition of further covariate-parameter relationships does not decrease the OFV to the specified criteria. The covariate-parameter relationships will be re-examined in the backward deletion step in a manner similar to the forward inclusion step but reversed with stricter criteria, corresponding to a significance level of α = 0.01.

Model evaluation

The bootstrap method will be used. This will involve a re-sampling technique where a sufficiently large number of new datasets will be generated by randomly sampling with replacement individuals out of the original dataset. Parameter estimates will then obtained for each bootstrap dataset and summary statistics will be applied to the distribution of the estimates. 1000 bootstrap datasets will be generated and each evaluated using the respective PK –PD PD model. The results from those estimation run where NONMEM was able to calculate the variance-covariance matrix (i.e., a successful covariance step), the 5th and 95th percentiles of the parameter distribution were derived representing the lower and the upper bound of a nonparametric 90% confidence interval. The bootstrap analysis will be performed with PsN (Perl speaks NONMEM, versions 2.2.3 to 2.3.1).

Simulation methods

Pharmacokinetic simulations will be performed using the final population model to evaluate the influence of rifampicin co-treatment and CYP2B6*6 status on concentrations in the steady-state dosing interval in patients after 21 weeks of treatment, a time that allows enzyme induction to be maximal. Simulations will be performed for rifampicin co-treatment and each CYP2B6*6 status, with simulated patients receiving a 600 mg oral daily dose as reference. Log-normally distributed patient weights will be simulated with a variance of the log-weight distribution similar to that of the observed patient dataset. Proportions of subjects with trough concentrations falling below the minimum effective concentration (<1 µg/mL or 3.2 µM) or at greater risk for toxicity (>4 µg/mL or 12.7 µM) will be determined for each sub population before predictions of drug concentrations and associated virologic decay and immunologic recovery rates using viral loads and CD4 counts are compared to provide efavirenz dosing recommendations for Ugandan HIV patients with or without TB co-treatment, as well as the different CYP2B6 genotype clusters.

The outcomes of this study will include efavirenz dose recommendations for Ugandan HIV-1 infected patients with or without rifampicin co-treatment and individuals carrying different genotypes of CYP2B6 516GT (GG, GT & TT). The study findings will guide population and sub-population based efavirenz dozing with the ultimate aim of optimizing its use in HIV management particularly among the African population. The model that best fits the data will be documented and published for future efavirenz population dose optimization studies.

Acknowledgment

The authors thank all patients for their participation in the study, the CTN, CIHR Canadian HIV Trials Network for offering to sponsor the current project through an international postdoctoral grant and the Swedish International Development Cooperation Agency, SIDA, for the grants (grant number. SWE 2004–098, HIV-2006-031, SWE 2007–270, Makerere University - Karolinska Institutet research collaboration) that were used for collection of data used for this project.

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Cite this paper

APA

Mukonzo, J. K. (2013). Optimization of Efavirenz Dosing During Treatment of HIV-1 Infected Adults in an African Population. Open Science Repository Pharmaceutics, Online(open-access), e70081939. doi:10.7392/Pharmaceutics.70081939

MLA

Mukonzo, Jackson K. “Optimization of Efavirenz Dosing During Treatment of HIV-1 Infected Adults in an African Population.” Open Science Repository Pharmaceutics Online.open-access (2013): e70081939. Web. 10 Mar. 2013.

Chicago

Mukonzo, Jackson K. “Optimization of Efavirenz Dosing During Treatment of HIV-1 Infected Adults in an African Population.” Open Science Repository Pharmaceutics Online, no. open-access (March 11, 2013): e70081939. http://www.open-science-repository.com/optimization-of-efavirenz-dosing-during-treatment-of-hiv-1-infected-adults-in-an-african-population.html.

Harvard

Mukonzo, J.K., 2013. Optimization of Efavirenz Dosing During Treatment of HIV-1 Infected Adults in an African Population. Open Science Repository Pharmaceutics, Online(open-access), p.e70081939. Available at: http://www.open-science-repository.com/optimization-of-efavirenz-dosing-during-treatment-of-hiv-1-infected-adults-in-an-african-population.html.

Science

1. J. K. Mukonzo, Optimization of Efavirenz Dosing During Treatment of HIV-1 Infected Adults in an African Population, Open Science Repository Pharmaceutics Online, e70081939 (2013).

Nature

1. Mukonzo, J. K. Optimization of Efavirenz Dosing During Treatment of HIV-1 Infected Adults in an African Population. Open Science Repository Pharmaceutics Online, e70081939 (2013).


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