Population Pharmacokinetic Analysis and Pharmacogenetics of
Raltegravir in HIV-Positive and Healthy Individuals
Mona Arab-Alameddine,a,dAurélie Fayet-Mello,aRubin Lubomirov,bMichael Neely,eJulia di Iulio,bAndrew Owen,fMarta Boffito,i
Matthias Cavassini,cHuldrych F. Günthard,gKatharina Rentsch,hThierry Buclin,aManel Aouri,aAmalio Telenti,b
Laurent Arthur Decosterd,aMargalida Rotger,bChantal Csajka,a,dand the Swiss HIV Cohort Study Group
Division of Clinical Pharmacology and Toxicology, University Hospital Center and University of Lausanne, Lausanne, Switzerlanda; Institute of Microbiology, University
Hospital Center and University of Lausanne, Lausanne, Switzerlandb; Division of Infectious Diseases, University Hospital Center and University of Lausanne, Lausanne,
Switzerlandc; School of Pharmaceutical Sciences, University of Geneva, and University of Lausanne, Geneva, Switzerlandd; Laboratory of Applied Pharmacokinetics,
University of Southern California, Los Angeles, California, USAe; Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, United Kingdomf;
Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerlandg; Division of Laboratory Medicine,
University Hospital Zurich, Zurich, Switzerlandh; and St. Stephen’s Centre, Chelsea and Westminster Foundation Trust, London, United Kingdomi
DNA into the host genome (8, 32). It is widely used in the treat-
ment of infection with resistant HIV strains and is increasingly
prescribed to treatment-naïve patients (11, 44).
RAL absorption is rapid and is affected by food intake (5) as
10). It is highly bound to plasma proteins (83%) (8) and elimi-
with minor contributions from UGT1A3 and UGT1A9 (22). Al-
though genetic variation in UGT isoenzymes may therefore affect
RAL exposure, two studies performed with healthy volunteers
failed to demonstrate an effect of the well-known decrease func-
tolerated, with only 3% treatment discontinuation due to adverse
effects (8, 40). The most common of these are fatigue, insomnia,
and grade 2 to 4 liver function abnormalities, including aspartate
aminotransferase (AST), alanine aminotransferase (ALT), and
bilirubin elevations (27).
The main objectives of this study were to characterize the RAL
pharmacokinetic profile and variability in HIV-positive (HIV?)
individuals and in healthy volunteers and to identify potential
mediated glucurononidation of RAL. Finally, simulations were
performed in order to compare concentrations at trough (Cmin)
after administration of 400 mg twice daily (BID) versus 800 mg
once daily (QD).
FDA that acts by inhibiting the covalent integration of viral
15 to 13 April 2011, and at the 20th Meeting of the Population
Approach Group in Europe, Athens, Greece, 7 to 10 June 2011.)
MATERIALS AND METHODS
Study design and population. RAL plasma levels in 145 HIV-infected
individuals enrolled in the Swiss HIV Cohort Study (SHCS) were mea-
sured as part of a routine therapeutic drug monitoring program between
October 2007 and November 2009 using a validated method previously
reported (15). A median of 1 sample per patient (range, 1 to 14) was
collected and drawn between 0.5 and 24.3 h after last drug intake under
obtained from 2 clinical trials. The first trial included 19 healthy volun-
teers enrolled in an open-label crossover pharmacokinetic interaction
study of RAL with and without atazanavir (ATV) (29).Treatment arms
were RAL at 400 mg BID alone or 400 mg plus ATV at 400 mg once daily.
In both arms, participants were instructed to take medication at least 1 h
before or 2 h after eating, and the actual time of drug intake was recorded
both by electronic recording (Medication Event Monitoring System
[MEMS]) and in a medication log. After 7 days of treatment, RAL con-
Received 3 August 2011 Returned for modification 4 November 2011
Accepted 15 February 2012
Published ahead of print 27 February 2012
Address correspondence to Chantal Csajka, firstname.lastname@example.org, or Margalida
C.C. and M.R. contributed equally to this article, and M.A.-A. and A.F.-M.
contributed equally to this article.
Supplemental material for this article may be found at http://aac.asm.org/.
Copyright © 2012, American Society for Microbiology. All Rights Reserved.
June 2012 Volume 56 Number 6 Antimicrobial Agents and Chemotherapyp. 2959–2966aac.asm.org
centrations were measured at predose and 1, 2, 4, 8, 12, and 24 h after the
last drug intake. The second trial involved 10 HIV-infected individuals
who took part in a study aiming at determining overall RAL cellular dis-
position (16). RAL was administered at 400 mg BID just after a standard-
8, and 12 h after drug intake. Both studies had been approved by the local
ethics committees, and all patients gave written informed consent to par-
ticipate. The discovery population for genetic analysis was composed of
composed of 219 HIV-infected individuals (119 from the SHCS, 43 from
the St. Stephen’s Centre, and 57 from the Liverpool Therapeutic Drug
the informed consent for genetic testing.
Genotyping. Genotyping was performed with a 127-plex customized
Veracode array (Illumina). We included 102 single nucleotide polymor-
(n ? 51) were selected using HapMap phase III data (release 24) (www
.hapmap.org) with Tagger software (13) to capture SNPs with minor al-
lelic frequencies of ?5% in the HapMap CEU population, known for a
mean maximum pairwise r2of 0.80 between genotyped and not geno-
typed SNPs. The tSNPs covered the genome region of the RefSeq longest
transcript plus 5 kb at the 3= and 5= untranslated regions (UTRs) in the
HapMap Genome Browser (www.hapmap.org). Haplotype tagging SNPs
(htSNPs) (n ? 8) were selected to better cover the allelic diversity of the
locus UGT1A (26). SNPs with proven functional effect (n ? 43) were
in the array is shown in the Table S1 in the supplemental material. SNPs
that failed to be genotyped by the array (rs7643645, rs2741049,
rs11692021, rs17671289, rs7574296, and rs1042640) were genotyped by
commercially available TaqMan allelic discrimination (Applied Biosys-
tems, Foster City, CA). SNPs that were not included in the array due to
technical limitations were genotyped either by direct sequencing
(rs3821242, rs6431625, and rs8175347) or by TaqMan allelic discrimina-
tion (rs1902023). UGT2B17 gene deletion was investigated with a previ-
ously published PCR strategy (26). For replication, the candidate SNP
rs72551330 was genotyped using TaqMan allelic discrimination. Primers
and probes are shown in Table S2 in the supplemental material.
Population pharmacokinetic model. (i) Basic model. One-, two-,
trointestinal tract were compared based on the data obtained from the 19
healthy volunteers with rich plasma sampling, which provided an initial
ent clearance (CL), apparent volume of distribution of the central com-
partment (V1), apparent volume of distribution of the peripheral com-
partment (V2), and intercompartmental clearance (Q). A relative
bioavailability for the HIV?data set was allowed in the model to capture
uals, with FHIV?fixed to 1.
tribution were assumed for the description of between-subject variability
proportional error model with a mean of zero and a variance of ?2was
used as well to describe the intrapatient (residual) variability.
rated sequentially into the pharmacokinetic model. The typical values of
the pharmacokinetic parameters were modeled to depend linearly on a
covariate X (centered on the mean for continuous covariates, e.g., 30
?mol/liter for total bilirubin levels, with categorical covariates being
coded as indicator variables into 0 or 1 ). The available demographic
covariates were sex, age, body weight, and ethnicity; few comedications
and some medications presumed to influence RAL exposure. Laboratory
tests of hepatic function (bilirubin, AST, and ALT) were also analyzed.
Parameter estimation and selection. Data analysis was performed
with NONMEM (version 7.1, NM-TRAN version II) by means of the
first-order conditional estimation method with interaction. As a good-
ness-of-fit statistic, NONMEM uses an objective function (OF), which is
approximately equal to minus twice the logarithm of the maximum like-
likelihood, approximately ?2distributed) of 3.84 for each additional pa-
rameter was used to determine statistical significance (P ? 0.05, two
sided) between two models. Covariate analysis comprised forward selec-
tion of influential factors followed by backward deletion. Model assess-
ment was based on diagnostic plots (goodness-of-fit plots and visual pre-
dictive checks), along with the measure of the standard errors, the
correlation matrix of parameter estimates, and the size of residual errors.
the bootstrap resampling procedure with replacement on 200 replicates.
The median and 95% confidence interval for each parameter were com-
ysis was performed using Perl-speaks-NONMEM version 3.2.4 (http:
//psn.sourceforge.net). In addition, simulations based on the final
pharmacokinetic estimates were performed with NONMEM using 1,000
individuals to calculate 90% prediction intervals (PIs). The concentra-
point were retrieved in order to construct the intervals. The values were
generated using GraphPad Prism (version 4.00 for Windows; GraphPad
Software, San Diego, CA).
An external model validation was performed as well on RAL sparse
concentrations collected from a new cohort of 55 HIV?individuals be-
tween December 2009 and November 2010. The final population param-
eter and variance estimates were used to calculate concentration predic-
tions for the validation data set. Empirical individual Bayesian posterior
observed concentrations and the maximum-likelihood parameter esti-
mates and variances obtained in the final model (by means of the
NONMEM option MAXEVAL ? 0). The predictions of the final model
tion error and root mean square prediction errors were calculated to de-
ual predictions versus observed concentrations.
Genetic association analyses. Genetic association analysis in the dis-
posteriori relative bioavailability estimates (FHIV?) as the phenotype. We
explored 3 different models of inheritance: additive, recessive, and dom-
inant. A genetic score was built up, assigning a value of 0 to a fully func-
function allele. UGT1A haplotypes were constructed using PHASE,
version 2.1 (University of Washington, Seattle, WA) (37, 38). The haplo-
type phylogenetic tree was constructed with MEGA 4.0 software (39) us-
ing maximum-parsimony methods. Epistatic interaction signals includ-
ing combinations of 2, 3, 4, or 5 SNPs and other covariates (glomerular
filtration rate [GFR], age, ATV, etravirine [ETV], tenofovir [TDF], and
proton pump inhibitors [PPIs]) were evaluated using the generalized
multifactor dimensionality reduction method (GMDR) (25).
Population pharmacokinetic analysis. A total of 544 RAL con-
centration values were used for the analysis, of which 335 values
were obtained from 145 HIV?patients and 209 from 19 healthy
individuals. Plasma concentrations ranged between 4 and 10,192
Arab-Alameddine et al.
aac.asm.orgAntimicrobial Agents and Chemotherapy
ng/ml. Demographic characteristics of the HIV?population are
summarized in Table 1, and the description of the population of
healthy volunteers (HIV?) is as reported by Neely et al. (7, 29).
First, the analysis of HIV?rich data was performed. The data
were best described by means of a 2-compartment model with
the data more appropriately than a 1-compartment model
(?OF ? ?172; P ? 0.001). The addition of a third compartment
did not significantly improve the fit (?OF ? ?5; P ? 0.08). Dif-
ferent absorption models were tested, using zero-order absorp-
tion, sequential independent zero- and first-order absorption, se-
quential linked zero- and first-order absorption (minimization
was not attained), parallel first- and zero-order absorption, and
finally 2 parallel first-order transfer processes, which failed to
achieve a better description of the absorption profile than the
first-order absorption model (?OF ? 10). The assignment of be-
tween-subject variability (BSV) to clearance (CL) (?OF ? ?302;
P ? 0.001), volume of distribution of the central compartment
(V1) (?OF ? ?73; P ? 0.001), and absorption rate constant (ka)
BSV on the peripheral compartment (V2) or the intercompart-
mental clearance (Q) was observed (?OF ? ?0.0).
Further analyses combining both rich and sparse data were
be estimated in both populations. Since major differences (40%)
in CL, V1, and kawere observed between HIV?and HIV?indi-
viduals, a relative bioavailability component (FHIV?) was intro-
duced in the model, while fixing bioavailability to 1 for healthy
volunteers (FHIV? ? 1). This model resulted in a significant im-
provement of the fit (?OF ? ?75; P ? 0.001). Two distinct ka
analysis including the entire population. A reduction to a single
value significantly worsened the fit (?OF ? 6.6). As previously
?960.4; P ? 0.001). In addition, assigning variability on both
Since the magnitudes of the BSV on the bioavailability and on the
absorption rate constant were similar for both groups, their vari-
ance was constrained to be the same. With the assignment of BSV
on relative bioavailability, the BSV on CL decreased from 72% to
19% (coefficient of variation [CV]) and did not remain statisti-
describe residual error according to the population (HIV?and
HIV?individuals) slightly improved the fit (?OF ? ?12; P ?
0.01). The final pharmacokinetic parameters and variability (CV)
without covariates were as follows: CL ? 64 liters · h?1, V1? 138
0.24 h?1(97.3%) in HIV?and HIV?individuals, respectively,
and FHIV?? 0.57 (90.6%).
der, body weight, and ethnicity), we observed a 65% higher rela-
of the variability in bioavailability (?OF ? ?6.4; P ? 0.01). Eth-
nicity showed some impact on V1(?OF ? ?6.5; P ? 0.01), sug-
gesting a 60% lower V1for Caucasians than for other ethnicities,
which explained approximately 3.8% of the variability in V1. No
further influences of body weight and age were observed. The
assignment of comedications in the models revealed that only
P ? 0.01). The inclusion of acid-reducing agents, including ant-
acids or proton pump inhibitors, yielded a 66% lower absorption
rate constant, but the effect did not reach statistical significance
(?OF ? ?3.5; P ? 0.06). EFV coadministration resulted in 20%
lower RAL bioavailability, but this effect also was not significant
(?OF ? ?0.5; P ? 0.5). The evaluation of the impact of AST,
ALT, and bilirubin, as markers of hepatic dysfunction, on RAL
pharmacokinetics showed that the RAL FHIV? increased linearly
increase in drug levels is expected in case of grade 1 hyperbiliru-
binemia (total bilirubin higher than 30 ?mol/liter). No further
TABLE 1 Demographic characteristics of the HIV?population
% of study
Median age, yr (range)
Median body wt, kg (range)
Median height, cm (range)
Coadministered drugs (no.)
Gastric acid-reducing agents
Median AST concn (U/liter) (Range)
Median ALT concn (U/liter) (range)
Median total bilirubin concn (?mol/
Median CD4?cell count, cells/mm3
Median HIV RNA level, copies/ml
aNRTIs, nucleoside reverse transcriptase inhibitors; NNRTIs, nonnucleoside reverse
Raltegravir Pharmacokinetics and Pharmacogenetics
June 2012 Volume 56 Number 6aac.asm.org 2961
influences of AST and ALT were observed (?OF ? ?0.0). Multi-
variate analyses and backward deletion confirmed that gender,
ATV coadministration, and bilirubin levels significantly influ-
enced FHIV?, as well as ethnicity influencing V1(?OF ? ?25.9
and P ? 0.001 in comparison to the model without any covari-
minal t1/2were 1.9 h and 10.9 h, respectively; and the volume of
distribution at steady state (Vss) was 368.4 liters. Plots of concen-
diction interval are presented in Fig. 1. Goodness-of-fit plots of
presented in Fig. S1 in the supplemental material.
presented in Table 2. The parameter estimates of the final popu-
lation pharmacokinetic model lay within the 95% CI of the boot-
TABLE 2 Final population and bootstrap resampling estimatesa
Final population pharmacokinetic
Median 95% CI Difference (%)c
aFinal model: F ? ?1· (1 ? ?bilirubin· FBIL) · (1 ? ?female· sex) · (1 ? ?ATV· ATV), where FBIL? (bilirubin concentration ? mean bilirubin concentration)/bilirubin
concentration (mean bilirubin concentration ?30 ?mol · liter?1).
bCL/F, mean apparent clearance; V1/F, mean apparent volume of distribution of the central compartment; V2/F, mean apparent volume of distribution of the peripheral
compartment; ka, mean absorption rate constant; Q/F, mean apparent intercompartmental clearance; FHIV?, bioavailability in the HIV? population; FHIV?, bioavailability in the
cDifference ? (bootstrap median value ? typical value from final model)/bootstrap median value.
dEstimate of variability is expressed as CV (%).
eStandard errors of the estimates, defined as SE/estimate.
fStandard errors of the coefficient of variation, taken as?SE ⁄ estimate.
gRelative decrease in V2/F in Caucasians versus other races (mostly black patients).
hRelative influence of ATV coadministration on RAL relative bioavailability.
iRelative influence of sex on RAL relative bioavailability.
jRelative influence of bilirubin on RAL relative bioavailability.
lResidual intrasubject variability.
FIG 1 RAL concentrations (circles) versus time standardized for a 400-mg
line) and the 95% prediction interval (dashed lines).
Arab-Alameddine et al.
aac.asm.orgAntimicrobial Agents and Chemotherapy
nal model validation based on 70 new RAL concentration data
showed nonsignificant biases of ?23% (95% CI, ?43.6% to
2.6%) for population predictions and ?10% (95% CI, 22.9% to
0.7%) for individual predictions. The precisions of population
and individual predictions amounted to 266% and 66%, respec-
tively. Plots of population and individual predictions versus ob-
Concentration-genetic association analysis. Among the 102
single nucleotide polymorphisms (SNPs) assessed, 14 failed qual-
ity control criteria for genotyping and 6 were genotyped by other
deletion were not originally included in the array and were there-
was not in Hardy-Weinberg equilibrium and 2 SNPs (rs7439366
and rs1800961) that were monomorphic were excluded from
a posteriori estimates of RAL FHIV? as the pharmacokinetic phe-
notype. Assuming a dominant model of inheritance, one SNP
(rs72551330, a marker of UGT1A9*3) reached study-wide signif-
icance (beta, ?1.78; P ? 4.18E?4) (Fig. 2A). However, the effect
of this SNP on RAL FHIV?was due to a single individual who was
homozygous for this rare allele and exhibited very high RAL bio-
availability (Fig. 2B). This individual had consistently high RAL
plasma concentrations even though the dose was reduced over
time (from 400 mg twice daily to 200 mg once daily). With both
in a replication data set of 219 HIV-infected individuals and 19
a more detailed analysis of this locus. A genetic score with the 10
associated with the phenotype (see Fig. S3 in the supplemental
material) (P ? 0.097 by the Kruskal-Wallis test). Haplotype anal-
FIG2 Results of the genetic association analysis. (A) Manhattan plot indicating the 96 SNPs analyzed in 8 chromosomes. The y axis represents the negative log
of the associated P value. The line indicates the study-wide cutoff for significance, corresponding to a Bonferroni correction of P ? 5.21 ? 10?4(0.05/96). Only
one SNP, rs72551330, reached study-wide significance (beta, ?1.78; P ? 4.18 ? 10?4). (B) Effect of rs72551330 on RAL bioavailability, assuming a dominant
model of inheritance (0, homozygous for the reference allele; 1, heterozygous for the rare allele; 2, homozygous for the rare allele). The y axis represents RAL
bioavailability. Median values are represented. Each dot is one individual.
Raltegravir Pharmacokinetics and Pharmacogenetics
June 2012 Volume 56 Number 6 aac.asm.org 2963
together with the ancestral haplotype of the chimpanzee, and we
grouped them according to similarity in 34 stems to reduce the
number of haplotypes. However, none of them was found to in-
ence of epistatic interactions between the 14 SNPs with proven
functional effect in the UGT1A and UGT2B genes using the gen-
eralized multifactor dimensionality reduction (GMDR) method
(25). Different epistatic models were investigated (combinations
of 2, 3, 4, or 5 SNPs) using the RAL FHIV? adjusted for discrete
(presence of ATV, ETV, TDF, and PPIs as concomitant medica-
tions) and quantitative (age and GFR) covariates as dependent
phenotypic traits. We did not identify any significant (P ? 0.05)
epistatic interaction signals.
Simulations. Model-based simulations of RAL at 400 mg BID
diction interval [PI], 10 to 1,380 ng/ml). Simulations of RAL at
800 mg QD yielded a median Cminof 52 ng/ml (95% PI, 4 to 817
ng/ml). The simulation of RAL at 400 mg BID associated with
ATV yielded a Cminof 171 ng/ml (95% PI, 14 to 1,783 ng/ml).
Taking the protein-adjusted 95% inhibitory concentration (IC95)
of 15 ng/ml (33 nM) for naïve patients as a cutoff value for the
target Cmin, 5% of the patients were predicted to present concen-
15% of those receiving 800 mg QD. Plots of the average concen-
trations with 95% PIs are presented for the 400-mg BID and
800-mg QD regimens in Fig. 3.
This study presents the first population pharmacokinetic analysis
of RAL in both HIV?individuals and healthy volunteers. The
results show that RAL has a high apparent clearance and is widely
half-life comparable with published data (7, 20, 41). The high
apparent clearance suggests that RAL is a high-extraction drug,
subject to significant first-pass metabolism. It was therefore as-
sumed that differences in the kinetics between HIV?and HIV?
individuals would depend mainly on variations in oral bioavail-
bioavailability was observed. The 25% lower relative bioavailabil-
ity in HIV?individuals than in healthy volunteers might be re-
lated to adherence issues, since HIV?individuals were studied
under more standardized conditions of drug intake and food sta-
tus. We cannot, however, exclude the possibility that this differ-
ence is related to the HIV disease, as gastrointestinal or malab-
20). The RAL absorption half-life was very variable, which is con-
sistent with previously reported data (2, 9, 27, 46). The slower
absorption half-life of RAL in HIV?individuals could be attrib-
uted to HIV-related motor gastrointestinal abnormalities or de-
layed gastric emptying, but it seems to have a modest impact on
time to peak (data not shown) (23, 30). It might also be the con-
tions, since meal type has been shown to explain part of the vari-
ability in RAL absorption (6). The important variability observed
standardized feeding condition indicates, however, that factors
other than food intake state might be involved. The lack of infor-
the interpretation of the results.
In line with previously reported data (10, 49), we observed an
approximately 40% increase in RAL bioavailability induced by
ATV coadministration, as a consequence of ATV-mediated
UGT1A1 inhibition (10, 17, 29). The small and nonsignificant
20% decrease in RAL bioavailability induced by EFV is similar to
previous observations (19, 49). The lack of statistical significance
is probably due to both the limited number of patients (n ? 14)
exposed to the association and the modest influence of EFV. RAL
is commonly used with tenofovir, which has been shown to mod-
estly increase RAL exposure (47), and with etravirine, which is
known to induce UGT1A1, resulting in a slight decrease in RAL
concentrations (1). However, no significant effects were found in
this study for either drug.
In contrast to other reports (20), we found a 65% higher RAL
exposure in female than in male patients. Sex-related pharmaco-
kinetic disparities have been reported for other antiretroviral
drugs (34, 42). There are many potential reasons for gender dif-
ferences in RAL pharmacokinetics, such as differences in gastric
pH, which is higher in females (36), lower hepatic expression of
ABCB1 P-glycoprotein in females (31), and lower hepatic blood
flow and consequently lower hepatic metabolic capacity (31, 36),
volume of distribution in Caucasian patients compared to pa-
tients of other ethnicities, who were mostly black patients. Ethnic
intervals (dashed lines) are shown.
Arab-Alameddine et al.
aac.asm.org Antimicrobial Agents and Chemotherapy
differences in drug distribution have been reported with many
drugs, which were attributed mainly to differences in protein
binding and in particular in binding to alpha1-acid glycoproteins
(AAG) (orosmucoid) (21). RAL, formulated as a potassium salt
els could thus explain the difference in the distribution of RAL
(21). The clinical significance of this finding is still not clear and
needs more investigation with larger patient cohorts.
come has not been formally reported (35). However, some recent
evidence suggests a possible role of drug concentration, in addi-
tion to virological parameters, in the efficacy and induction of
virological resistance to RAL (12, 14). In accordance with results
pared to 800 mg QD indicate that a higher percentage of patients
would exhibit Cminunder the protein-adjusted IC95with the
800-mg QD regimen than with 400 mg BID, which might put
viral load, as reported by Eron et al. (14). Considering the very
large BSV variability encountered, some patients might also ex-
hibit very low RAL concentrations with the standard 400-mg BID
regimen, which suggests that therapeutic drug monitoring of this
drug could be relevant in some situations. However, the rather
lationships (12, 14) and on the good correlation between RAL
intracellular concentrations and plasma concentrations (16, 28),
and pharmacokinetic exposure and the potential role of concen-
vent some of the limitations inherent to observational studies.
Genetic variations in UGT isoenzymes and nuclear receptors
were not significantly associated with RAL exposure, except for
loss-of-function allele that is substrate specific. It has been associ-
ated with decreased glucuronidation activity of irinotecan and
mycophenolic acid but not flavopiridol (4, 24, 45). Studies using
RAL metabolite profiles as the phenotype may represent a better
alternative to identify genetic variants influencing RAL exposure.
In conclusion, the RAL pharmacokinetic profile is character-
ized by high interpatient and residual variability. ATV, gender,
and hyperbilirubinemia appear to affect RAL bioavailability,
whereas ethnicity affects the volume of distribution. Except pos-
sibly for UGT1A9*3, no genetic polymorphisms was found to ex-
plain the large RAL pharmacokinetic variability. Owing to this
very large variability, drug concentrations may be very low under
with 800 mg QD, suggesting that therapeutic drug monitoring of
RAL could yet be relevant in some situations. Further studies fo-
cusing on concentration-response relationships should be per-
formed to better define target plasma concentrations.
grant 33CS30-134277) and has been financed in the framework of the
were generated during the RALTA clinical study supported by Médecins
Sans Frontières (11) and by the SHCS research foundation.
for providing the computational resources for the population analyses.
We thank the patients who participate in the SHCS, the physicians and
study nurses for excellent patient care, Martin Rickenbach and Yannick
Marie-Christine Francioli for administrative assistance.
The members of the Swiss HIV Cohort Study Group are M. Battegay,
E. Bernasconi, J. Böni, H. C. Bucher, P. Bürgisser, A. Calmy, S. Cattacin,
P. Francioli, H. Furrer, C. A. Fux, M. Gorgievski, H. Günthard, H. H.
Hirsch, B. Hirschel, I. Hösli, C. Kahlert, L. Kaiser, U. Karrer, C. Kind, T.
Klimkait, B. Ledergerber, G. Martinetti, N. Müller, D. Nadal, F. Paccaud,
G. Pantaleo, A. Rauch, S. Regenass, M. Rickenbach, C. Rudin, P. Schmid,
D. Schultze, J. Schüpbach, R. Speck, B. M. de Tejada, P. Taffé, A. Telenti,
A. Trkola, P. Vernazza, R. Weber, and S. Yerly.
1. Anderson MS, et al. 2008. Minimal pharmacokinetic interaction between
the human immunodeficiency virus nonnucleoside reverse transcriptase
inhibitor etravirine and the integrase inhibitor raltegravir in healthy sub-
jects. Antimicrob. Agents Chemother. 52:4228–4232.
2. Ashby J, et al. 2011. Pharmacokinetic and safety profile of raltegravir and
ribavirin, when dosed separately and together, in healthy volunteers. J.
Antimicrob. Chemother. 66:1340–1345.
3. Baroncelli S, et al. 2010. Raltegravir plasma concentrations in treatment-
experienced patients receiving salvage regimens based on raltegravir with
and without maraviroc coadministration. Ann. Pharmacother. 44:838–
metabolism of mycophenolic acid and the effects of naturally occurring
variants. Drug Metab. Dispos. 32:775–778.
5. Brainard DM, et al. 2011. Effect of low-, moderate-, and high-fat meals
on raltegravir pharmacokinetics. J. Clin. Pharmacol. 51:422–427.
6. Brainard DM, Wenning LA, Stone JA, Wagner JA, Iwamoto M. 2011.
Clinical pharmacology profile of raltegravir, an HIV-1 integrase strand
transfer inhibitor. J. Clin. Pharmacol. 51:1376–1402.
7. Brown KC, Paul S, Kashuba AD. 2009. Drug interactions with new and
investigational antiretrovirals. Clin. Pharmacokinet. 48:211–241.
8. Burger DM. 2010. Raltegravir: a review of its pharmacokinetics, pharma-
cology and clinical studies. Expert Opin. Drug Metab. Toxicol. 6:1151–
9. Cattaneo D, et al. 2012. Inter- and intra-patient variability of raltegravir
pharmacokinetics in HIV-1-infected subjects. J. Antimicrob. Chemother.
10. Cattaneo D, et al. 2010. Exposure-related effects of atazanavir on the
pharmacokinetics of raltegravir in HIV-1-infected patients. Ther. Drug
11. Croxtall JD, Scott LJ. 2010. Raltegravir: in treatment-naive patients with
HIV-1 infection. Drugs 70:631–642.
antiretroviral-experienced patients with virological failure on raltegravir-
containing regimens. J. Antimicrob. Chemother. 65:1262–1269.
13. de Bakker PIW, et al. 2005. Efficiency and power in genetic association
studies. Nat. Genet. 37:1217–1223.
14. Eron JJ, Jr et al. 2011. Raltegravir once daily or twice daily in previously
untreated patients with HIV-1: a randomised, active-controlled, phase 3
non-inferiority trial. Lancet Infect. Dis. 11:907–915.
15. Fayet A, et al. 2009. A LC-tandem MS assay for the simultaneous mea-
surement of new antiretroviral agents: raltegravir, maraviroc, darunavir,
and etravirine. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 877:
16. Fayet-Mello A, et al. 2011. Cell disposition of raltegravir and newer
antiretrovirals in HIV-infected patients: high inter-individual variability
in raltegravir cellular penetration. J. Antimicrob. Chemother. 66:1573–
17. Iwamoto M, et al. 2008. Atazanavir modestly increases plasma levels of
raltegravir in healthy subjects. Clin. Infect. Dis. 47:137–140.
Raltegravir Pharmacokinetics and Pharmacogenetics
June 2012 Volume 56 Number 6 aac.asm.org 2965
19. Iwamoto M, et al. 2008. Minimal effects of ritonavir and efavirenz on the Download full-text
pharmacokinetics of raltegravir. Antimicrob. Agents Chemother. 52:
20. Iwamoto M, et al. 2008. Safety, tolerability, and pharmacokinetics of
raltegravir after single and multiple doses in healthy subjects. Clin. Phar-
macol. Ther. 83:293–299.
21. Johnson JA. 2000. Predictability of the effects of race or ethnicity on
pharmacokinetics of drugs. Int. J. Clin. Pharmacol. Ther. 38:53–60.
22. Kassahun K, et al. 2007. Metabolism and disposition in humans of ralte-
gravir (MK-0518), an anti-AIDS drug targeting the human immunodefi-
ciency virus 1 integrase enzyme. Drug Metab. Dispos. 35:1657–1663.
23. Konturek JW, Fischer H, van der Voort IR, Domschke W. 1997.
Disturbed gastric motor activity in patients with human immunodefi-
ciency virus infection. Scand. J. Gastroenterol. 32:221–225.
24. Levesque E, et al. 2007. The impact of UGT1A8, UGT1A9, and UGT2B7
genetic polymorphisms on the pharmacokinetic profile of mycophenolic
acid after a single oral dose in healthy volunteers. Clin. Pharmacol. Ther.
25. Lou XY, et al. 2007. A generalized combinatorial approach for detecting
gene-by-gene and gene-by-environment interactions with application to
nicotine dependence. Am. J. Hum. Genet. 80:1125–1137.
26. Menard V, Girard H, Harvey M, Perusse L, Guillemette C. 2009.
Analysis of inherited genetic variations at the UGT1 locus in the French-
Canadian population. Hum. Mutat. 30:677–687.
27. Merck Sharp and Dohme. 2010. Isentress prescribing information.
Merck & Co., Whitehouse Station, NJ.
28. Molto J, et al. 2011. Plasma and intracellular (peripheral blood mononu-
HIV-infected patients. Antimicrob. Agents Chemother. 55:72–75.
29. Neely M, et al. 2010. Pharmacokinetics and pharmacogenomics of once
30. Neild PJ, et al. 2000. Delayed gastric emptying in human immunodefi-
ciency virus infection: correlation with symptoms, autonomic function,
and intestinal motility. Dig. Dis. Sci. 45:1491–1499.
31. Nicolas JM, Espie P, Molimard M. 2009. Gender and interindividual
variability in pharmacokinetics. Drug Metab. Rev. 41:408–421.
32. Powderly WG. 2010. Integrase inhibitors in the treatment of HIV-1 in-
fection. J. Antimicrob. Chemother. 65:2485–2488.
population-based linkage analyses. Am. J. Hum. Genet. 81:559–575.
34. Rotger M, Csajka C, Telenti A. 2006. Genetic, ethnic, and gender differ-
ences in the pharmacokinetics of antiretroviral agents. Curr. HIV/AIDS
35. Scherrer AU, et al. 2010. Implementation of raltegravir in routine clinical
and characteristics of failures. J. Acquir. Immune Defic. Syndr. 53:464–
36. Soldin OP, Mattison DR. 2009. Sex differences in pharmacokinetics and
pharmacodynamics. Clin. Pharmacokinet. 48:143–157.
37. Stephens M, Donnelly P. 2003. A comparison of Bayesian methods for
haplotype reconstruction from population genotype data. Am. J. Hum.
38. Stephens M, Smith NJ, Donnelly P. 2001. A new statistical method for
haplotype reconstruction from population data. Am. J. Hum. Genet. 68:
39. Tamura K, Dudley J, Nei M, Kumar S. 2007. MEGA4: Molecular Evo-
40. Teppler H, et al. 2011. Long-term safety from the raltegravir clinical
development program. Curr. HIV Res. 9:40–53.
41. Ter Heine R, et al. 2009. Identification and profiling of circulating me-
tabolites of atazanavir, a HIV protease inhibitor. Drug Metab. Dispos.
42. Umeh OC, Currier JS. 2006. Sex differences in pharmacokinetics and
toxicity of antiretroviral therapy. Expert Opin. Drug Metab. Toxicol.
43. U.S. Department of Health and Human Services. 2009. Common Ter-
minology Criteria for Adverse Events (CTCAE). http://evs.nci.nih.gov
Department of Health and Human Services, Washington, DC.
44. U.S. Department of Health and Human Services. 2010. Panel on Anti-
antiretroviral agents in HIV-1-infected adults and adolescents. U.S. De-
partment of Health and Human Services, Washington, DC.
45. Villeneuve L, Girard H, Fortier LC, Gagne JF, Guillemette C. 2003.
Novel functional polymorphisms in the UGT1A7 and UGT1A9 glucu-
ronidating enzymes in Caucasian and African-American subjects and
their impact on the metabolism of 7-ethyl-10-hydroxycamptothecin and
flavopiridol anticancer drugs. J. Pharmacol. Exp. Ther. 307:117–128.
46. Wang L, et al. 2011. Pharmacokinetic modeling of plasma and intracel-
lular concentrations of raltegravir in healthy volunteers. Antimicrob.
Agents Chemother. 55:4090–4095.
47. Wenning LA, et al. 2008. Lack of a significant drug interaction between
raltegravir and tenofovir. Antimicrob. Agents Chemother. 52:3253–3258.
48. Wenning LA, et al. 2009. Pharmacokinetics of raltegravir in individuals
with UGT1A1 polymorphisms. Clin. Pharmacol. Ther. 85:623–627.
49. Zhu L, et al. 2010. Pharmacokinetics and safety of twice-daily atazanavir
300 mg and raltegravir 400 mg in healthy individuals. Antivir. Ther. 15:
Arab-Alameddine et al.
aac.asm.orgAntimicrobial Agents and Chemotherapy