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Antimicrobial Agents and Chemotherapy, August 2000, p. 2052-2060, Vol. 44, No. 8
0066-4804/00/$04.00+0
Copyright © 2000, American Society for Microbiology. All rights reserved.
Population Pharmacokinetics and Pharmacodynamic
Modeling of Abacavir (1592U89) from a Dose-Ranging, Double-Blind,
Randomized Monotherapy Trial with Human Immunodeficiency
Virus-Infected Subjects
Stephen
Weller,*
Kristine M.
Radomski,
Yu
Lou, and
Daniel S.
Stein
Worldwide Clinical Pharmacology, Glaxo
Wellcome Inc., Research Triangle Park, North Carolina
Received 23 September 1999/Returned for modification 23 January
2000/Accepted 26 April 2000
 |
ABSTRACT |
Abacavir (formerly 1592U89) is a carbocyclic nucleoside analog with
potent anti-human immunodeficiency virus (anti-HIV) activity when
administered alone or in combination with other antiretroviral agents.
The population pharmacokinetics and pharmacodynamics of abacavir were
investigated in 41 HIV type 1 (HIV-1)-infected, antiretroviral
naive adults with baseline CD4+ cell counts of
100/mm3 and plasma HIV-1 RNA levels of >30,000
copies/ml. Data for analysis were obtained from patients who received
randomized, blinded monotherapy with abacavir at 100, 300, or 600 mg
twice-daily (BID) for up to 12 weeks. Plasma abacavir concentrations
from sparse sampling were analyzed by standard population
pharmacokinetic methods, and the effects of dose, combination therapy,
gender, weight, and age on parameter estimates were investigated.
Bayesian pharmacokinetic parameter estimates were calculated to
determine the peak concentration of abacavir in plasma
(Cmax) and the area under the
concentration-time curve from time zero to infinity
(AUC0-
) for individual subjects. The pharmacokinetics
of abacavir were dose proportional over the 100- to 600-mg dose range
and were unaffected by any covariates. No significant correlations were
observed between the incidence of the five most common adverse events
(headache, nausea, diarrhea, vomiting, and malaise or fatigue) and
AUC0-. A significant correlation was observed between
Cmax and nausea by categorical analysis
(P = 0.019), but this was of borderline significance
by logistic regression (odds ratio, 1.45; 95% confidence interval,
0.95 to 2.32). The log10 time-averaged
AUC0- minus baseline (AAUCMB) values for HIV-1 RNA and
CD4+ cell count correlated significantly with
Cmax and AUC0-, but with better
model fits for AUC0-. The increase in AAUCMB values for
CD4+ cell count plateaued early for drug exposures that
were associated with little change in AAUCMB values for plasma HIV-1
RNA. There was less than a 0.4 log10 difference over 12 weeks in the HIV-1 RNA levels with the doubling of the abacavir
AUC0- from 300 to 600 mg BID dosing. In conclusion,
pharmacodynamic modeling supports the selection of abacavir 300 mg
twice-daily dosing.
| |
INTRODUCTION |
A continuing need for new
antiretroviral agents against human immunodeficiency virus type 1 (HIV-1) exists due to toxicity and development of drug resistance.
Abacavir (formerly 1592U89) is a novel purine carbocyclic nucleoside
that is phosphorylated by a unique metabolic pathway to carbocyclic
guanosine triphosphate, a potent inhibitor of HIV-1 reverse
transcriptase (8). Abacavir has only limited
cross-resistance with other nucleoside reverse transcriptase inhibitors
in vitro (18; J. W. Mellors, K. Hertogs, F. Peeters, R. Lanier, V. Miller, N. Graham, B. Larder, P. Stoffels, and
R. Pauwels, Abstracts 5th Conf. Retroviruses Opportunistic Infections,
abstr. 687, p. 208, 1998), which suggests that it could be a useful
addition to anti-HIV therapy.
Following the administration of single or multiple doses to adults and
children, abacavir is rapidly absorbed, with peak concentrations in
plasma (Cmax) occurring within 1 to 2 h
after dosing, an elimination half-life (t1/2) of
1 to 2 h (11, 12), and penetration into cerebrospinal
fluid (13; J. R. Ravitch, S. S. Good,
J. E. Humpreys, J. W. Poli, W. H. Robertson, and J. L. Jarrett, Abstr. 5th Conf. Retroviruses Opportunistic Infections,
abstr. 636, p. 199, 1998). The pharmacokinetics of abacavir are dose
dependent at steady state following the administration of multiple oral
doses of >600 mg daily to adults (14).
In order to further define the optimum dose of abacavir and to evaluate
the durability of its antiretroviral effects in HIV-1-infected subjects, a phase II clinical trial (CNAB-2002) was conducted to
evaluate the antiretroviral activity and safety of three twice-daily (BID) dosage regimens of abacavir monotherapy of up to 24 weeks. The
clinical efficacy and safety results of the full trial reported previously have shown that abacavir is a potent nucleoside reverse transcriptase inhibitor, providing a median reduction in plasma HIV-1
RNA level of over 1.5 log10 copies/ml by 4 weeks when
administered as monotherapy (16). The sustained reductions
in plasma HIV-1 RNA levels for up to 72 weeks achieved by combination
therapy with abacavir, lamivudine, and zidovudine in the same study (S. Staszewski, C. Katlama, T. Harrer, P. Massip, P. Yeni, A. Cutrell, and
H. M. Steel, Abstr. 12th Int. Conf. AIDS, abstr. 12212, 1998) were
associated with the slow development of resistance to abacavir (18). We report here the results of a substudy which was
designed to determine the population pharmacokinetics of abacavir with the nonlinear mixed-effects model (NONMEM) (2), the
association of pharmacokinetic parameters with demographic or
disease-related variables, and the pharmacodynamic modeling of the
effects of abacavir exposure on safety and antiretroviral activity over
the first 12 weeks of monotherapy.
| |
MATERIALS AND METHODS |
Study population and design.
The study design and criteria
for study participation have been described in a previous publication
(16). Briefly, subjects were eligible for study entry if
they were 18 years of age with confirmed HIV-1 infection, were
antiretroviral therapy naive, and presented at screening (within 14 days prior to study drug administration) with a CD4+ cell
count of 100/mm3 and a plasma HIV-1 RNA level of more
than 30,000 copies/ml. Enrollment was planned for a total of 60 subjects (20 subjects per treatment group) in order to provide an 80%
power to detect treatment differences of 0.45 log10
copies/ml in time-averaged HIV-1 RNA profiles. Subject participation in
the pharmacokinetic component of the study was optional. Sparse samples
for population pharmacokinetic analysis were obtained at the week 12 clinic visit for a subset of the study subjects. In the randomized,
double-blind monotherapy phase, subjects were randomly assigned to
receive 100, 300, or 600 mg of abacavir orally every 12 h (BID).
Subjects could add additional therapy (zidovudine and lamivudine were
provided by the study) if they met prespecified criteria
(16). These criteria included a plasma HIV-1 RNA load
reduction from baseline of <0.7 log10 copies/ml at week 4, a plasma HIV-1 RNA load of >5,000 copies/ml after week 12, a
CD4+ cell count that returned to the baseline count, or a
new Centers for Disease Control and Prevention AIDS-defining event
after 4 weeks.
Blood sampling.
Blood samples (3 ml) for determination of
plasma abacavir concentrations were collected by venipuncture and
placed into Vacutainer tubes that contained powdered dipotassium EDTA.
Blood samples were obtained from each subject in the intervals of 0.5 to 1, 1 to 2, 2 to 3, and 3 to 4 h postdosing, with at least 30 min between the times of sample collection. Blood samples were
centrifuged within 1 h of collection and were stored upright in
labeled biofreeze tubes at 
20°C or lower until shipment to Glaxo
Wellcome for assay of the abacavir concentration.
Abacavir assay.
Plasma samples were analyzed for abacavir
concentration by a validated reverse-phase high-performance liquid
chromatography assay with UV detection over a quantifiable range of 25 to 5,000 ng/ml (12). Briefly, plasma samples (0.2 ml) were
mixed with 0.1 ml of 10% trichloroacetic acid, and the mixture was
centrifuged at 8,800 × g for 10 min. Supernatant (0.1 ml) was injected onto a Rainin (4.6 by 250 mm) C18
Microsorb MV column. The mobile phase of the column consisted of 40%
methanol in 25 mM ammonium phosphate-0.3% triethylamine (pH 7.2) at a
flow rate of 1.0 ml/min. Abacavir was detected by measurement of the UV
absorbance at 284 nm. The retention time for abacavir was approximately
9 to 10 min under these conditions. The interday variability
(coefficient of variation) was <8%, and the bias of the assay was
0%.
Efficacy and safety assessments.
The primary efficacy
measures assessed during abacavir monotherapy administration were
changes from baseline in the log10 HIV-1 RNA copies per
milliliter and changes in the CD4+ lymphocyte cell count.
The plasma HIV-1 RNA was measured by the Roche HIV-1 RNA PCR technique
(limit of quantification, 400 copies/ml; Amplicor HIV-1 Monitor test;
Roche Molecular Systems, Branchburg, N.J.). CD4+ cell
counts were assessed by standard flow cytometry methods at each site.
Only data collected up to the time of pharmacokinetic evaluation (week
12) were included in related pharmacodynamic analyses. During this
interval, assessments were performed at the baseline (day 0) and at
weeks 2, 4, 8, and 12.
Adverse events were managed by investigators unaware of the treatment
assignments by using predetermined guidelines, and the severity of the
adverse events was graded according to criteria developed by the AIDS
Clinical Trials Group, Division of AIDS, National Institute for Allergy
and Infectious Diseases (4).
Data analysis. (i) Population pharmacokinetic analysis.
Mixed-effect modeling techniques with the software package NONMEM,
version 4, level 2 (2), were used to develop a model that
describes abacavir population pharmacokinetics after oral administration and to evaluate the influence of specific covariates. The model-building process involved establishment of a base
pharmacokinetic compartmental model, selected by graphical observation
of the concentration-time data and information from historical
pharmacokinetic experience. This model included no covariates.
Subsequent NONMEM runs were executed separately for each potential
covariate in order to evaluate the effect of inclusion of the covariate
on a pharmacokinetic parameter. Following these univariate analyses, fixed effects (e.g., measurable covariates such as age and weight) that
were considered potentially significant were combined in a multivariate
analysis. Backward elimination of one fixed-effect covariate at a time
(each time replacing the other covariates) was then performed to select
the final model.
The base model selected to describe abacavir pharmacokinetics was a
one-compartment model with first-order absorption and
elimination
(specified to NONMEM by the routines ADVAN2 and TRANS2).
The
pharmacokinetic parameters directly estimated by NONMEM with
this model
specification were apparent oral clearance (CL/
F),
apparent
volume of distribution (
V/F), and absorption rate constant
(
Ka). The elimination rate constant
(
z) was determined
by the ratio of clearance
to volume (CL/
V).
Ka was constrained
to be greater than CL/
V. Bayesian pharmacokinetic parameter
estimates
for individual subjects were obtained by specification of the
POST-HOC option to NONMEM. Individual estimates of the steady-state
Cmax, time to
Cmax
(
Tmax) at steady state, area under the
concentration-time
curve from time zero to infinity
(AUC
0-), and
t1/2 were then
derived from Bayesian estimates of CL/
F,
V/F, and
Ka (
10).
The covariate measures considered for evaluation of the effect on
abacavir pharmacokinetics included the presence or absence
of
combination antiretroviral therapy, dose, and demographic traits
of
age, gender, and body weight. In combination with the statistical
NONMEM output, examination of scatter plots of Bayesian parameter
estimates for individual subjects versus these fixed effects were
used
to select meaningful covariates for inclusion in the pharmacokinetic
model.
Proportional error models were used throughout the analysis for both
interindividual and intraindividual (residual) variability.
The
covariance step was executed with each NONMEM run to obtain
standard
errors of the parameter estimates, the variance-covariance
matrix, and
the correlation matrix. The results were also examined
graphically with
scatter plots that included predicted versus
observed concentrations,
weighted residuals versus time, and weighted
residuals versus predicted
concentrations.
The criteria for acceptance of a NONMEM model estimation included the
following: (i) convergence of the objective function
(i.e., a
"successful termination" statement from the NONMEM program),
(ii)
attainment of parameter estimates free of boundary conditions,
(iii)
standard error estimates <30% of the estimate itself, (iv)
termination of the covariance step without warnings, and (v)
correlations
between model parameters of <0.95. A model was declared
superior
over another one when the value of the objective function was
reduced by >7.8 with the inclusion of one additional parameter.
A
superior model was also expected to reduce the intersubject
variance
terms and/or the residual error term. Finally, a superior
model should
improve the random distribution of
residuals.
(ii) Pharmacokinetic-pharmacodynamic analyses. (a) Correlation of
pharmacokinetics with efficacy.
The efficacy measurements used in
this analysis included changes in HIV-1 RNA (in log10
copies per milliliter) and CD4+ cell count (in number of
cells per cubic millimeter) from the baseline values. As an initial
analysis, nonparametric Spearman's rank-order correlation analysis was
performed with SAS software, version 6.12 (SAS Inc., Cary, N.C.), to
assess the degree of association between pharmacokinetic parameters and
efficacy measures.
To more fully characterize the relationship between antiviral activity
and drug exposure, pharmacodynamic modeling was performed.
Since
changes in CD4
+ cell count while on therapy are highly
correlated with baseline
values (
5,
7,
16), log
transformation of the CD4
+ cell count provided an
assessment of the difference from the
baseline count, similar to the
determination of a percent change
from baseline. In this way, the
absolute change in the CD4
+ cell count would no longer be
linked to the patient's baseline
value. The HIV-1 RNA load was log
transformed given its exponential
distribution. After log
10
transformation of the HIV-1 RNA load
and CD4
+ cell count,
calculation of the area under the curve, subtraction
of the baseline
value, and division by the duration of monotherapy
(up to 12 weeks)
were performed in order to yield a time-weighted
average change from
the baseline (denoted as the time-weighted
area under the curve minus
baseline [AAUCMB]
value).
Two pharmacodynamic modeling analyses were performed because not all of
the subjects in the pharmacokinetic substudy remained
on abacavir
monotherapy up to the week 12 evaluation. Specifically,
of the 41 subjects who participated in the pharmacokinetic substudy,
27 subjects
remained on their initial randomized monotherapy regimen
through week
12 and 14 subjects switched from monotherapy to combination
therapy
prior to the week 12 pharmacokinetic sampling. Of the
latter group,
nine subjects switched from 100-mg-BID or 600-mg-BID
abacavir
monotherapy to combination antiretroviral therapy with
300 mg of
abacavir BID and five subjects switched from monotherapy
with abacavir
at 300 mg BID to combination antiviral therapy without
a change in the
abacavir regimen. These subjects switched therapies
at or after the
week 8 visit. For the first analysis, only those
subjects who finished
the first 12 weeks of monotherapy without
changing their dose of
abacavir were analyzed (
n = 27). For the
second
analysis, the dose-adjusted estimates of
Cmax
and AUC
0- were used for all subjects for whom
pharmacokinetic data were
available at week 12 and who had switched
from their initial randomized
monotherapy regimen to the combination
antiretroviral therapy
with 300 mg of abacavir BID prior to the week 12 pharmacokinetic
sampling (
n = 41). Adjusted
Cmax and AUC
0- values
were
determined, assuming dose proportionality, by scaling the
original
estimates to the monotherapy dose. In this way, pharmacokinetic
parameters could be obtained for the same dose for which
pharmacodynamic
measures were taken. For both pharmacokinetic-efficacy
analyses,
since the effect of abacavir monotherapy was to be
investigated,
efficacy data obtained after subjects had switched to
open-label
combination therapy were
excluded.
Pharmacokinetic and pharmacodynamic modeling was conducted with the
WinNonlin software package (WinNonlin-Pro version; Scientific
Consulting, Inc., Cary, N.C.). The analyses included standard
Emax and sigmoid
Emax
models with uniform weighting. The general
form of the model equation
used was given by
|
(1)
|
where
E is effect,
E0 is the
baseline effect (possibly fixed equal to zero),
Emax is the maximal effect,
C denotes
the pharmacokinetic
variable (e.g., AUC
0- or
Cmax), EC
50 is the value
of the
pharmacokinetic variable that corresponds to 50% of the
maximum
effect, and
denotes the shape parameter that describes
the degree
of sigmoidicity. For the simple
Emax model,
was assigned
a fixed value of
unity.
Estimation of
E0 was problematic for the CD4
analyses in that final estimates generally converged toward any lower
bound placed
on the parameter. This convergence was a result of the
roughly
hyperbolic profile given by the available data. A lower bound
of
0.4 was selected for the analysis to be consistent with the
data
and the maximal possible physiological change in the CD4
count in the
absence of therapy (about a 40% decline) (
17).
Use of other
lower bounds such as
0.2 or
0.3 made little difference
in estimated
EC
50 or
Emax.
Goodness of fit was assessed from adjusted
r2
(Adj
r2) and associated
P values.
Adjusted
r2 was calculated by using the
following standard formula:
|
(2)
|
where the residual and corrected sums of squares (SS) were those
supplied by the WinNonlin-Pro output. The associated
P
values
were determined from the
r value after calculation of
the
z statistic
for a normal distribution from the standard
equation
|
(3)
|
Akaike's information criterion (
1) was used to
differentiate models by comparison of the same exposure-activity
relationships.
(b) Correlation of pharmacokinetics with safety.
Safety data
from the first 12 weeks were included in the analyses without regard to
combination therapy status as long as the subjects continued to receive
abacavir. For this analysis, the abacavir AUC0- and
Cmax values used for a given subject were those
which corresponded to the abacavir dose received over the majority of
the 12-week interval. A subanalysis which excluded any adverse event
that occurred after the end of monotherapy was also conducted.
Statistical analyses of the associations between pharmacokinetic
measures of drug exposure (AUC
0- and
Cmax) and
measures of safety were determined by
both categorical analysis
and logistic regression methods.
Subject-specific estimates of
AUC
0- and
Cmax were determined from Bayesian
pharmacokinetic
parameter estimates for individual subjects generated
by the final
population model. The five most common adverse events
(headache,
nausea, diarrhea, vomiting, and malaise or fatigue) were
selected
for analysis on the basis of their incidence (the number of
subjects
who experienced the adverse event during the first 12 weeks of
therapy) and potential for attribution of the adverse event to
the
study
drug.
Categorical analysis was performed by interval analyses in which the
range of AUC
0- values was divided into three
equally
populated intervals (tertiles) and the range of
Cmax estimates
was divided into two equal
intervals on the basis of the median
value. The frequency of each
adverse event was represented as
the number of subjects who fell into
each of the respective intervals.
The association between adverse event
incidence and drug exposure
was evaluated by Mantel-Haenszel chi-square
statistics with modified
ridit scores (SAS PROC
FREQ).
Logistic regression (SAS PROC LOGISTIC) was conducted to estimate the
relationship between the AUC
0- or
Cmax and each adverse event. Odds ratios were
determined along with
their 95% confidence intervals, in which the
odds ratio denotes
the scalar multiplier for the likelihood of a given
adverse event
associated with a unit change in AUC
0- or
Cmax.
| |
RESULTS |
The demographic and baseline characteristics for the subjects in
the pharmacokinetic substudy are summarized in Table
1. All characteristics were similar
across the three treatment groups.
Population pharmacokinetic analysis.
The population
pharmacokinetic parameter estimates (95% confidence intervals of the
estimates) associated with the base model were 60.0 liters/h (53.5 to
66.5 liters/h) for CL/F, 67.6 liters (57.6 to 77.6 liters)
for V/F, and 1.70 h
1 (1.04 to 2.43 h1) for Ka. The estimated
intersubject coefficient of variation in CL/F was 34% (20%
to 43%), and that for Ka was 101% (79% to 120%). Intersubject variability in V/F could not be
estimated from the study data. This same result was obtained for model
executions with or without inclusion of the intersubject variability
parameter for Ka.
Results obtained from the models that incorporated fixed-effect
covariates are presented in Table
2. No
differences in the
CL/
F or
V/F of abacavir were
observed between subjects who received
abacavir as monotherapy and
subjects who received abacavir in
combination with other antiretroviral
therapy. Multiplicative
models that related CL/
F to dose and
V/F to dose did not produce
a significant reduction in the
objective function, indicating
dose proportionality over the dose range
examined. There were
no significant associations of age, gender, and
body weight with
pharmacokinetic parameters. Evaluation of the effect
of age on
CL/
F resulted in an apparently significant
reduction in the objective
function (change =
8.55), but the
value of the added parameter
in this model was not significantly
different from its null value
(i.e., the 95% confidence interval of
the estimate included zero).
Mean Bayesian pharmacokinetic parameter estimates for each dose group
are presented in Table
3.
Cmax and AUC
0- estimates for the
300- and 600-mg doses were approximately three
and seven times greater,
respectively, than those for the 100-mg
dose. Estimates of
Ka,
Tmax, and
t1/2 were similar among the dose
cohorts.
CL/
F estimates appeared to decrease with increasing dose,
although as indicated above, this was not found to be significant.
Pharmacokinetic and pharmacodynamic analyses. (i) Correlation of
pharmacokinetics with efficacy.
Spearman's rank correlation
analysis showed that AUC0- and
Cmax were significantly correlated with a change
in HIV-1 RNA from baseline to the end of 12 weeks of monotherapy (P < 0.05; data not shown). In contrast,
AUC0- and Cmax were
significantly correlated with a change in CD4+ cell count
only up to 4 weeks of monotherapy (data not shown). The significant
results obtained from this initial correlation analysis led to further
characterization of the relationships by
pharmacokinetic-pharmacodynamic modeling (see Materials and Methods).
Simple
Emax models were found to best describe
the data (i.e., inclusion of the exponential shape parameter,
, was
not justified
in any of the model fits). The final model parameters for
the
relationships between AAUCMB values for HIV-1 RNA level or
CD4
+ cell count over the 12-week evaluation period versus
the abacavir
AUC
0- and
Cmax
values are summarized for the two
data sets in Table
4. Graphical results for the model-fit
relationships
with abacavir AUC
0- are presented in Fig.
1 and
2.
AUC
0- produced better model fits than
Cmax for both
data sets, as indicated by the
r2 values (Table
4). As predicted by the
Emax model, the doubling
of the level of
abacavir exposure from 300 mg BID to 600 mg BID
dosing was associated
with a 0.23- to 0.38-log
10 difference in
the time-averaged
change in the log
10 HIV-1 RNA change from baseline,
depending on the population modeled (Table
5). When data for
all subjects
(
n = 41) up through just 8 weeks of treatment were
modeled, the results were similar (data not shown). The increase
in
drug exposure from 300 to 600 mg BID was also associated with
small
increases (approximately 12%) in the CD4
+-cell count (or
an increase of approximately 43 cells/mm
3 from the baseline
median cell count of 359 cells/mm
3) for both sets of
analyses (Table
5). The maximum increase in
the log
10
CD4
+ cell count from baseline (
Emax)
was 0.12 (Table
4), which represents
a 31.8% increase in the
CD4
+ cell count from baseline (or an increase of
approximately 114
cells/mm
3 from the baseline median cell
count of 359 cells/mm
3). The increase in the
CD4
+ cell count also reached a plateau at
AUC
0- values
that were associated with minimal changes
in HIV-1 RNA load, regardless
of the data set used (Fig.
1 and
2).
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TABLE 4.
Pharmacodynamic-pharmacokinetic modeling: model parameter
estimates for log10 AAUCMB values of HIV-1 RNA load and
CD4+ cell count versus AUC0-
and Cmax
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FIG. 1.
Effect of abacavir AUC0- on HIV-1 RNA
suppression. Shown here are data for individual subjects, the mean ± 95% confidence intervals for each dose cohort, and the model fit
curve. Ordinal values denote the AAUCMB values for the
log10 plasma HIV-1 RNA load in plasma. Model parameters are
given in Table 4. The mean ± standard deviation HIV-1 RNA AAUCMB
values for each dose cohort are given in Table 5. Smooth curves denote
the pharmacokinetic- pharmacodynamic model fit from the
Emax model. (A) Pharmacokinetic-pharmacodynamic
relationship for those subjects who were maintained on abacavir
monotherapy for 12 weeks (n = 27). (B)
Pharmacokinetic-pharmacodynamic relationship for all subjects while on
abacavir monotherapy for up to 12 weeks (n = 41).
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FIG. 2.
Effect of abacavir AUC0- on
CD4+ cell count. Shown here are data for individual
subjects, the mean ± 95% confidence intervals for each dose
cohort, and the model fit curve. Ordinal values denote the AAUCMB
values for the log10 CD4+ cell count in plasma.
Model parameters are given in Table 4. The mean ± standard
deviation AAUCMB CD4+ values for each dose cohort are given
in Table 5. Smooth curves denote the pharmacokinetic-pharmacodynamic
model fit from the Emax model. (A)
Pharmacokinetic-pharmacodynamic relationship for those subjects who
were maintained on abacavir monotherapy for 12 weeks (n = 27). (B) Pharmacokinetic-pharmacodynamic relationship for all
subjects while on abacavir monotherapy for up to 12 weeks (n = 41).
|
|
(ii) Correlation of pharmacokinetics with safety.
No
significant associations were found between the incidence of the
adverse events and the abacavir AUC0- from the
Mantel-Haenszel analysis (data not shown). A statistically significant association was noted for nausea and
Cmax of 1.93 µg/ml (P = 0.019). No significant association with the abacavir Cmax was seen for the other adverse events.
Logistic regression analyses between the abacavir AUC0-
and the incidence of adverse events did not reveal any statistically
significant associations. There was a borderline association with
increasing Cmax and nausea (odds ratio,
1.45; 95% confidence interval, 0.95 to 2.32).
| |
DISCUSSION |
The results of the population analysis for this study indicate
that the pharmacokinetics of abacavir are dose proportional over the
range of 100 to 600 mg BID. The analysis was unable to detect any
pharmacokinetic differences associated with combination antiretroviral
therapy, age, gender, or body weight. The power to detect effects due
to these factors may have been limited by the relatively small number
of subjects in the analysis. The pharmacokinetic-pharmacodynamic relationships for changes from the baseline values in both
time-averaged HIV-1 RNA load and CD4+ cell count were
strongly associated with abacavir AUC0-. The
EC50 for the time-averaged change in the HIV-1 RNA load was always appreciably greater than that for the CD4+ cell
count, indicating the early saturation of the CD4+ cell
count change, with little associated change in HIV-1 RNA load. This
finding is consistent with the pharmacokinetic analysis of indinavir
monotherapy (7). A modest increase in HIV-1 RNA suppression,
but no increase in the CD4+ cell count, was observed at 600 mg BID relative to 300 mg BID as monotherapy. No differences in
abacavir pharmacokinetics were observed when the drug was administered
as monotherapy or in combination with other antiretroviral medications.
This finding is consistent with results from earlier pharmacokinetic
drug interaction studies (14, 19).
The present study did not reveal any effects of gender on abacavir
pharmacokinetics. A significant gender effect was found in a previous
trial that used higher levels of exposure to abacavir (14).
In that study, women (n = 11) demonstrated a 54%
greater AUC0- and a 30% greater
Cmax than men (n = 68). Reasons for the difference between the population pharmacokinetic findings and
those from the previous trial are unclear and may be related to the
small number of female subjects in both studies. Abacavir has been well
tolerated
except for dose-related nauseaat single doses of up to
1,250 mg and multiple doses of up to 1,800 mg/day (600 mg three times
daily). Thus, if a gender effect did exist, it is unlikely to be of
clinical significance at the dosage regimen intended for further
clinical investigation (300 mg BID).
In addition to the present study, pharmacokinetic analysis of abacavir
after 12 weeks of dosing with 300 mg BID was previously investigated
with nine subjects (14). In the present study, the mean ± standard deviation individual Bayesian estimates were 58.0 ± 13.2 liters/h for CL/F and 67.6 liters for V/F.
z was calculated to be 0.86 ± 0.20 h1. Estimates obtained in the study by McDowell et al.
(14) were 55.0 ± 22.7 liters/h for CL/F,
99.3 ± 20.9 liters for V/F, and 0.54 ± 0.13 h1 for z. The estimates for
CL/F are in good agreement between the two studies. The
reasons for the difference in estimates of the volume of distribution
between these two studies are not clear but may be due to the sparse
sampling used in the present study and the inability to estimate
interindividual variability for V/F.
For pharmacodynamic measures, cumulative assessments of antiviral
activity were obtained by calculating AAUCMB for the log10 HIV-1 RNA load and the log10 CD4+ cell count
over the first 12 weeks of therapy. The pharmacokinetic-pharmacodynamic relationships evaluated by these measures are less subject to the
biological variability associated with evaluation at a single time
point and represent the global effect in the HIV-infected population
over a meaningful interval for evaluation of clinical activity. The
initial pharmacokinetic-pharmacodynamic analysis included subjects who
remained on fixed-dose abacavir monotherapy for the 12 weeks up to the
time of pharmacokinetic evaluation (n = 27). This
analysis thus excluded subjects who switched from abacavir monotherapy
because of recognized treatment failure and may therefore be
potentially biased toward subjects in whom abacavir had greater or more
prolonged antiviral activity. The second
pharmacokinetic-pharmacodynamic analysis was conducted with data for
all subjects who participated in the pharmacokinetic study
(n = 41) and used either all available monotherapy data
or a subset of data for up to 8 weeks of therapy. Because 12-week
exposure data for 9 of the 41 subjects had to be adjusted for their
prior dosage regimens, there is the possibility of error in estimation
to the extent that strict dose proportionality may not apply. Any
overestimation of exposure at the 100-mg dose as a consequence of this
assumption could result in a decrease in the slope of the
pharmacokinetic-pharmacodynamic relationships (i.e., a shift to the
right) and result in higher EC50s and/or lower
Emaxs.
The pharmacodynamic relationships from the analyses in this study
indicate a small incremental effect of 600 mg BID versus that of 300 mg
BID in HIV-1 RNA load suppression. The pharmacodynamic effects
associated with both regimens are on the near-plateau portion of the
Emax relationship. Depending on the model, this doubling of the abacavir dose resulted in a 0.23- to
0.38-log10 difference in the time-averaged change in
log10 HIV-1 RNA load from baseline. This modest difference
would be difficult to detect clinically, especially since abacavir
would be used in combination therapy under usual clinical
circumstances. The wide range of exposures below those that result in
the maximum effect is somewhat different from that observed for other
nucleosides; a more pronounced Emax or sigmoid
Emax relationship has been described for
zidovudine, didanosine, and 3'-deoxy-3'-fluorothymidine (5, 6,
9), although the relationship for zidovudine did not attain
significance. Each of these nucleosides has some rate-limiting step in
their phosphorylation pathways. By comparison, the pharmacodynamic
relationships observed with abacavir in this study are consistent with
in vitro data that show a linear relationship between the level of the active triphosphate of abacavir and exposure to abacavir over the range
of 0.1 to 100 µM (3). Thus, while it appears that some
additional incremental antiviral activity can be obtained with
increasing doses of abacavir, it is clear from the
Emax relationships that such increases would be
progressively smaller with increasing systemic exposure. Results from
analyses of the relationship between safety and abacavir exposure in
the present study are consistent with those of other studies that have
indicated an increase in nausea with increasing doses of abacavir
(15, 16).
In conclusion, the pharmacokinetic-pharmacodynamic results of this
study support the selection of abacavir at 300 mg BID as the approved
dose. (Also, the results from these trials have shown that abacavir in
combination with other antiretroviral agents provides potent and
durable suppression of HIV-1 RNA [15, 16; M. Fischl, S. Greenberg, N. Clumeck, B. Peters, R. Rubio, B. Pobiner, and
L. Verity, 12th World AIDS Conference, abstr. 12230, 1998].)
| |
ACKNOWLEDGMENTS |
We gratefully acknowledge the assistance of Bill Mahony with
performing the bioanalytical studies, Keith Muir for helpful discussions and review of the manuscript, and Belinda Ha for assistance with manuscript preparation.
| |
FOOTNOTES |
*
Corresponding author. Mailing address: Division of
Clinical Pharmacology, Glaxo Wellcome Inc., 5 Moore Dr., Research
Triangle Park, NC 27709. Phone: (919) 483-1273. Fax: (919) 483-6380. E-mail: sw46385{at}glaxowellcome.com.
Present address: Pharmaceutical Sciences Department, St. Jude
Children's Research Hospital, Memphis, Tenn.
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Abstract
Introduction
