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Antimicrobial Agents and Chemotherapy, April 1998, p. 821-826, Vol. 42, No. 4
0066-4804/98/$04.00+0
Copyright © 1998, American Society for Microbiology. All rights reserved.
Relationship between Didanosine Exposure and
Surrogate Marker Response in Human Immunodeficiency
Virus-Infected Outpatients
John M.
Adams,1,2,3
Mark J.
Shelton,1,2,3
Ross G.
Hewitt,3,4
Thaddeus H.
Grasela,5
Mary
DeRemer,1 and
Gene D.
Morse1,2,3,4,*
Laboratory for Antiviral
Research1 and Departments of
Pharmacy
Practice2 and
Medicine,4 Schools of Pharmacy and
Medicine, State University of New York at Buffalo, and
the
Antiviral Clinical Pharmacology Unit, Immunodeficiency Clinic, Erie
County Medical Center,3 Buffalo, New York,
and
Pharmaceutical Outcomes Research, Inc., Williamsville, New
York5
Received 13 December 1996/Returned for modification 15 June
1997/Accepted 23 December 1997
 |
ABSTRACT |
We used information available from routine clinic visits to
characterize the pharmacokinetics of didanosine in 82 human
immunodeficiency virus-infected patients. A total of 271 blood samples
were collected for the measurement of didanosine concentrations in
plasma (mean ± standard deviation [SD], 3.30 ± 2.21 samples/patient). Bayesian estimates of didanosine oral clearance
(CLoral) were obtained for these patients by the POSTHOC
option within the NONMEM software package. Population priors from a
previous NONMEM analysis of didanosine pharmacokinetics were used. The
mean ± SD CLoral was 132 ± 27.7 liters/h, which
agrees reasonably well with estimates obtained from previous
pharmacokinetic studies of didanosine. Estimates of individual
didanosine exposure were then used to consider potential relationships
between drug exposure and surrogate marker response over a
6-month period. No correlations were found between the didanosine area
under the concentration-time curve from 0 to 6 months and the absolute
CD4 cell count (r = 0.305; 0.1 < P < 0.2), weight response (r = 0.0857; P > 0.4), or percentage of CD4 lymphocytes
(r = 0.0559; P > 0.4). Future
efforts to characterize didanosine exposure in outpatients by random
sampling methods should involve more directed efforts to limit residual
variability in the data.
| |
INTRODUCTION |
In 1991 didanosine became the second
nucleoside analog to gain Food and Drug Administration approval for the
treatment of human immunodeficiency virus (HIV) infection. Minimal
hematologic toxicity and notable in vitro activity against
zidovudine-resistant strains of HIV made didanosine an
attractive alternative for patients who were intolerant of zidovudine
or who were experiencing clinical or immunologic deterioration during
zidovudine therapy. Early phase I trials with didanosine
demonstrated potentially beneficial changes in weight, CD4 cell count,
p24 antigen levels, and clinical signs or symptoms (2, 28).
The pharmacokinetics of didanosine have been studied in
patients with AIDS and severe AIDS-related complex (14, 15,
17-20, 24, 31). Didanosine exhibits linear pharmacokinetic
behavior after the administration of oral doses of 0.8 to 10.2 mg/kg of body weight. The elimination half-life after oral administration is
approximately 1.4 h, with renal clearance accounting for up to
50% of total body clearance. Renal clearance values exceed the
glomerular filtration rate, indicating renal tubular secretion. The
intracellular half-life of the active form
(2',3'-dideoxyadenosine-5'-triphosphate; ddATP) of
didanosine is at least 12 h (1), allowing
for twice-daily dosing. The bioavailability of didanosine,
an acid-labile drug, differs among the formulations studied and
exhibits significant inter- and intrapatient variability.
Potential relationships between didanosine exposure and
surrogate markers of antiretroviral efficacy have been investigated. Beltangady and coworkers (7) evaluated the relations between the average steady-state concentration in plasma
(Cpss) of didanosine and CD4
cell counts, p24 antigenemia, and weight gain in patients with AIDS or
severe AIDS-related complex participating in a phase I study. Median
didanosine Cpss values among
patients having favorable responses in CD4 cell counts, p24
antigenemia, and weight gain at week 12 of therapy were significantly
higher than those among patients without such improvements. After
corrections for baseline CD4 cell counts and prior zidovudine history
with the use of a logistic regression model, didanosine
Cpss values remained positively correlated
with improvements in the surrogate markers studied.
Drusano et al. (11), in an earlier analysis of phase I data,
defined didanosine exposure in terms of the steady-state
area under the concentration-versus-time curve (AUC) when evaluating its relationship to average CD4 cell counts and p24 antigen
concentrations during therapy. Increases in CD4 cell counts were
determined to be independent of didanosine exposure and
proportional to the baseline CD4 cell count. A reduction in circulating
p24 antigen levels, however, was found to be related to both the
single-dose and the cumulative didanosine AUC. Indeed, an
increasing cumulative didanosine AUC appeared to be highly
correlated with p24 antigen suppression.
The purpose of this investigation was to examine the feasibility of
using observational data in a clinic setting to determine didanosine exposure and to further evaluate the
relationship between didanosine exposure (defined as the
cumulative didanosine AUC) and surrogate marker response in
a group of HIV-infected outpatients.
| |
MATERIALS AND METHODS |
Patients.
The criteria for inclusion into the
pharmacokinetics portion of the study (Bayesian estimation of oral
clearance [CLoral]) included HIV infection and current
treatment with didanosine. Patients receiving zidovudine in
combination with didanosine were included in this analysis,
because didanosine pharmacokinetics appear to be unaltered
by zidovudine (5, 9, 23).
Inclusion criteria for the pharmacodynamic analysis were a minimum of
180 days of didanosine monotherapy (as initial
didanosine exposure) and a minimum of three surrogate
marker observations within the period of didanosine
therapy. These included a baseline determination which had been
obtained within 60 days of the initiation of didanosine but
not later than 11 days after therapy was begun.
Eighty-two HIV-infected patients (76 males and 6 females) from the
Immunodeficiency Clinic at Erie County Medical Center were included in
the pharmacokinetics segment of the study. These individuals met the
previously stated inclusion criteria, and adequate demographic and
pharmacokinetic information about these patients was available from
clinic records. For 22 of these patients evaluable surrogate marker
data were available, and these data were included in the pharmacodynamic analysis. All patients gave written informed consent and were enrolled in the study from November 1991 through February 1994.
After enrollment in the study, patients filled out a questionnaire
during each visit to the clinic. Information obtained from
the
questionnaire included the date, time, and dose of
didanosine
taken most recently, as well as whether any
doses had been missed
in the 24 h prior to the clinic visit. In
addition to dosing history,
the questionnaire also provided information
regarding the time
of the patient's last meal and recent
over-the-counter medication
use. Also at each clinic visit, in addition
to blood collected
for routine laboratory tests, a whole-blood sample
(approximately
5 ml) was collected into an EDTA-containing tube labeled
with
the patient's identification number, the date and time that the
sample was obtained, and the dose of didanosine taken by
the patient.
The blood sample was centrifuged at 2,000 rpm in a
Marathon centrifuge
for 15 min. The plasma layer was then transferred
into a polypropylene
test tube and frozen at
20°C until the sample
was assayed. In
a number of cases the whole-blood sample was not
processed on
the day of collection but was refrigerated until
centrifugation.
The patients' clinic charts were reviewed to verify
the didanosine
dosing histories and to obtain demographic
information, risk factors
for HIV disease, and routine laboratory and
surrogate marker data.
Pertinent surrogate markers were considered to
be the absolute
CD4 cell count, the percentage of CD4 lymphocytes, and
the patient's
weight.
Prior to assay, all samples were heat inactivated in a water bath for
30 min at 56°C. Plasma didanosine concentrations were
determined by a radioimmunoassay method (
10). Intra-assay
variation
(coefficient of variation) averaged 4.73% at 1 ng/ml and
2.99%
at 30 ng/ml, while the interassay variation averaged 5.66% at
2.5 ng/ml, 6.89% at 5 ng/ml, and 10.6% at 30 ng/ml.
The plasma didanosine concentrations and dosing history
information were input into a Lotus database and were later converted
into a NONMEM-ready input file. Empiric Bayesian estimates of
CL
oral were obtained for each patient using the POSTHOC
option
within the NONMEM software package (
6,
30,
30a).
The clearance estimates obtained from a prior multivariate analysis of
factors influencing the pharmacokinetics of didanosine
in a
similar population of patients were taken to be the population
priors
(unpublished data). That analysis found clearance to be
a linear
function of weight and creatinine clearance. The mean
population
CL
oral estimate, obtained by using one-compartment
model
with oral absorption, was 1.97 liters/h/kg.
Individual estimates of CL
oral, along with
didanosine daily dose information, were used to determine
the didanosine 24-h AUC
(AUC
0-24) for each
patient in the pharmacodynamics component
of the study
(AUC
0-24 = daily dose/CL
oral). The cumulative
didanosine AUC throughout 6 months of therapy
(AUC
6) was calculated
by using the dosing histories
obtained from each patient's clinic
chart. Only two patients had dose
changes during this 6-month
period, and one patient experienced an
interruption in therapy
of more than 2 weeks.
Relationships between didanosine exposure (as measured by
the AUC
0-6) and surrogate marker response over the first 6
months of therapy were investigated. For 22 of the total number
of
patients at least three lymphocyte subset measurements were
taken (for
18 of 22 patients at least three weight measurements
were taken) for
the pharmacodynamic analysis.
Graphs of each patient's weight, CD4 count, and percent CD4
lymphocytes versus duration of didanosine therapy were
constructed.
The area under the surrogate marker-versus-duration
of therapy
curve was obtained with the LAGRAN program
(
27). The normalized
AUC (NAUC) for the surrogate markers in
each patient was calculated
by dividing the LAGRAN-generated cumulative
AUC from 0 to 6 months
(AUC
0-6) by the AUC which would
have been observed if the
surrogate marker value had remained at the
baseline for 6 months
(
29). The cumulative NAUC from 0 to 6 months (NAUC
0-6)
of 1.1 indicated a 10% increase in the
AUC over 6 months; an NAUC
0-6 of 0.9 represented a 10%
decrease in the AUC over the same period.
Graphs of normalized surrogate marker AUCs (NAUC
0-6)
versus the didanosine AUC
0-6 were then
constructed. Potential
relationships were evaluated via simple linear
regression and
correlation analysis. A Student's
t test was
used to determine
if the observed correlation coefficients
(
r) were different from
zero at a predetermined alpha error
of 0.05.
| |
RESULTS |
Pharmacokinetics.
Data for 82 patients (76 males and 6 females) were included in the Bayesian estimation of CLoral
(Table 1). Risk factors for HIV disease
included homosexual activity (65% of patients), intravenous drug use
(11% of patients), and both homosexual activity and intravenous drug
use (6% of patients). Eighteen percent of the patients had either
unidentified or other risk factors (e.g., heterosexual transmission).
The mean ± standard deviation (SD) age at the time that each
patient's first blood sample was collected was 37.7 ± 8.29 years. The mean weight at that time was 73.3 ± 13.8 kg, and the
mean serum creatinine level was 1.01 ± 0.191 mg/dl. None of the
patients had a calculated creatinine clearance of less than 50 ml/min.
The mean CD4 count at the initiation of didanosine therapy
for a subset of these patients (for which data were available) was
187 ± 119 cells/mm3.
The average number of evaluable samples obtained from each patient
during the course of this study was 3.30 ± 2.21. As indicated
in
Fig.
1, the disposition of
didanosine in our patients appeared
to be linear in nature.
Eighty-seven percent of the 271 plasma
samples collected were obtained
while the patients were taking
the chewable tablet formulation; the
remainder were from patients
taking the sachet (powder). Nine percent
of the samples were collected
from patients receiving concomitant
zidovudine therapy.
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|
FIG. 1.
Didanosine (DDI) concentration versus times since last
dose. The doses were normalized to 200 or 250 mg. Open circle, tablet;
closed circle, sachet.
|
|
Bayesian estimation of the CL
oral for 82 patients resulted
in a mean value of 132 ± 27.7 liters/h (range, 77.8 to 216 liters/h).
Threefold variability in individual estimates was noted
among
patients receiving the most commonly prescribed regimen of 400
mg/day (Fig.
2). In order to investigate
this variability, patients
were divided into three groups (upper
quartile, middle half, and
lower quartile) on the basis of the
estimated CL
oral (Table
2).
No significant differences among these groups were found
(
P >
0.05; Kruskal-Wallis, chi-square test).
The didanosine AUC
6 was calculated for the 22 patients for whom surrogate marker data were available and averaged
505 ± 113
mg·h/liter (range, 255 to 755 mg·h/liter).
Pharmacodynamics.
Surrogate marker responses revealed
significant variability within the most common dosing regimen of 400 mg
of didanosine per day (200 mg twice daily). Normalized
AUC0-6 values for CD4 count, percent CD4 lymphocytes, and
weight were regressed against didanosine cumulative
AUC0-6 (Fig. 3). The graph of the CD4 NAUC0-6 versus didanosine
AUC0-6 shows that the variability in response is
approximately fourfold in the didanosine
AUC0-6 range of 400 to 600 mg·h/liter. This was largely
responsible for the poor correlation that was observed (r = 0.305; 0.1 < P < 0.2). In
addition, the largest response was seen in a patient with the
second lowest level of didanosine exposure. The range
of weight responses was relatively narrow, and individual
responses did not appear to be correlated with didanosine exposure (r = 0.0857;
P > 0.4). The percent CD4 lymphocyte response
exhibited almost threefold variability and was not correlated with
didanosine exposure (r = 0.0559;
P > 0.4).
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|
FIG. 3.
Relation between didanosine (DDI) exposure
and surrogate marker response. WT, weight; CUM, cumulative.
|
|
Twenty of the 22 patients in the surrogate marker analysis had received
zidovudine prior to the initiation of didanosine therapy.
None of these patients received zidovudine and didanosine
concomitantly.
The period between the initiation of zidovudine and the
start
of didanosine averaged 765 ± 283 days. CD4
NAUC
0-6 did not
appear to be correlated with the duration
of prior zidovudine
exposure (
r = 0.295; 0.2 <
P < 0.4). Interestingly, the largest
responses were
seen in patients who had at least 700 days of prior
zidovudine therapy.
This observation also held true for percent
CD4 lymphocyte responses,
which appeared to be less variable and
which were poorly correlated
with the duration of prior zidovudine
exposure (
r = 0.295; 0.2 <
P < 0.4). Weight responses,
although
less variable, also were not correlated with the duration of
prior
zidovudine therapy (
r = 0.150;
P > 0.4).
| |
DISCUSSION |
Pharmacokinetics.
Our results indicate that a study design
which uses the random collection of plasma didanosine
concentrations during routine patient care may yield bayesian estimates
of CLoral which appear to be reasonable when differences in
bioavailability among formulations are taken into consideration
(14, 17-19, 24). Our analysis produced a mean
CLoral estimate of 132 ± 27.7 liters/h (1.89 liters/h/kg for a 70-kg patient). However, largely unexplained
variability in CLoral, coupled with a Bayesian estimate of
CLoral that was very similar to the Bayesian estimated mean
population value (132 versus 138 liters/h), forced us to consider the
possibility that we had simply converged on the Bayesian estimated mean
population value during our estimation. To address this possibility, we
considered eliminating from the analysis patients from whom only one or
two blood samples were collected during the study. It was felt that this might improve the accuracy and precision of the individual Bayesian CLoral estimates. However, we did not observe
appreciable differences in the median CLoral among patients
sampled with various degrees of frequency. Therefore, data for all
patients were included in the final analysis.
Despite pharmacokinetic parameter estimates which appear to be
reasonable, there may be shortcomings with the use of our observational
data. We relied on accurate patient recall and compliance with
a dosing
history questionnaire. Since didanosine has a relatively
short plasma half-life, a discrepancy between the reported time
of
administration of the last dose and the actual administration
time of
only an hour could significantly affect our estimate of
clearance. The
plasma uric acid level was not used to assess compliance
because of the
paucity of these data for our patients (
26).
For those
samples which were not processed in a timely manner
after they were
collected, the metabolism of didanosine by erythrocytes
may
have contributed to an underestimation of the concentration
(and
overestimation of clearance) of didanosine in plasma
(
3,
4).
On the basis of our results, we believe that didanosine
exposure may be reasonably estimated by obtaining random blood samples
during routine clinic visits. To optimize sample collection accuracy,
we would recommend the controlled administration of a dose in
the
clinic and retrieval of a random blood sample (which is immediately
centrifuged) later the same day. This should minimize any recall
bias
that a patient may have in describing the administration
of the doses
prior to blood sampling. Other options for minimizing
this bias, such
as the use of a medication diary to document exact
dosing times, could
eliminate the need for an extended clinic
visit (or a return visit that
day) to obtain a blood sample for
didanosine concentration
measurement.
Pharmacodynamics.
We did not observe a relationship between
our measure of response, CD4 NAUC0-6, and
didanosine AUC0-6 (r = 0.305; 0.1 < P < 0.2). When the CD4 response was
measured as an average CD4 cell count during therapy, the correlation
appeared even worse (r = 0.0771; P > 0.4). This is consistent with other reports and would suggest that CD4
responses are largely independent of the magnitude of
didanosine exposure. Although this argument may not apply
to an extreme example (i.e., little or no didanosine
exposure), it appears to be reasonably valid for the usual dosing
range.
Peak CD4 cell count responses usually occurred in the first 8 to 16 weeks of therapy. For 13 of 22 patients (59%) the CD4
cell count
remained at or above the baseline CD4 cell count after
6 months of
therapy with didanosine. Furthermore, for 14 of 22
patients
(64%) the CD4 NAUC
0-6 was at least 1; for another
3 patients the NAUC
0-6 value was at least 0.9. These
responses
are comparable to those observed in another investigation in
which
some patients were switched to didanosine after a
course of zidovudine
(
16).
Didanosine exposure was correlated with an increase in the CD4 cell
count in another investigation (
7). However,
didanosine
exposure was measured by the average
Cpss, and the period of assessment
was only
12 weeks. Perhaps if we had limited our period of analysis
to 3 months
we could have found such a relationship. However,
due to the lack of
availability of CD4 cell count data for that
time period for the
calculation of CD4 NAUC
0-6, we selected
6 months. Those
same investigators (
7) also noted correlations
between the
didanosine
Cpss and a decrease
in the p24 antigen
concentration as well as an increase in body weight.
Drusano et
al. (
11) noted a sigmoidal relationship between
p24 antigen
suppression and the single-dose didanosine AUC.
We did not have
adequate p24 antigen data for consideration, and we
were unable
to find a relationship between the body weight
NAUC
0-6 and
didanosine AUC
0-6
(
r = 0.0857;
P > 0.4). Furthermore, no
relationship between the percent CD4 lymphocyte NAUC
0-6 (
r = 0.0559;
P > 0.4) and
didanosine exposure was noted.
Kozal et al. (
21) found a high frequency of zidovudine
resistance mutations at codon 215 of reverse transcriptase in
combination
with didanosine mutations at codon 74 during 48 weeks of didanosine
therapy preceded by zidovudine therapy.
Other researchers have
shown that this combination of mutations may
confer an even greater
degree of didanosine resistance than
the codon 74 mutation by
itself (
12,
32). Hence, the
isolates infecting patients with
longer zidovudine exposures (and an
increased prevalence of isolates
with mutations at codon 215) may be
more likely to develop more
significant didanosine
resistance. Despite these considerations,
surrogate marker responses
did not appear to correlate with previous
zidovudine exposure in our
patients. In fact, the greatest response
in CD4 counts was seen in
patients with a minimum of 700 days
of previous zidovudine therapy.
Another consideration is the fact that we used the concentrations of
didanosine in plasma as indirect measurements of drug
exposure. Perhaps the intracellular concentrations of ddATP would
allow
estimates of drug exposure which would be positively correlated
with
surrogate marker responses. Since little or no information
on
intracellular ddATP concentrations or on the relationship between
intracellular ddATP and plasma didanosine
concentrations is available,
we were left with plasma as the most
feasible compartment in which
to measure didanosine
exposure.
Numerous factors potentially exerting an influence on the surrogate
marker response highlight the imperfect nature of the
surrogate markers
that we investigated and make any clear relationship
between
didanosine exposure and response less likely. Changes
in a
surrogate marker over time should correlate with the risk
of disease
progression, and any effect of drug treatment on this
risk of clinical
progression should be explainable and predictable
by its effect on the
marker (
22,
25). As has been previously
demonstrated
(
8,
13,
33), CD4 counts prior to the initiation
of therapy
are reasonably strong predictors of disease progression.
However, those
studies note that the CD4 count, in particular,
can explain only a
portion of the favorable effects of zidovudine
in delaying the
progression of disease. In other words, zidovudine
appears to convey to
patients benefits which are independent of
the effect of zidovudine on
the CD4 count. Therefore, the CD4
count does not meet the strict
definition of a complete surrogate
marker (
22,
25). Hence,
it is possible that there does exist
a relationship between drug
exposure and a beneficial effect for
patients: we are simply measuring
suboptimal indices. Other markers
of disease activity, such as plasma
HIV RNA levels (which were
not available at the time of this study),
may be more highly correlated
with didanosine exposure.
Conclusion.
In summary, we believe that it is possible to
obtain reasonable estimates of individual didanosine
pharmacokinetic parameters in the course of routine patient care.
However, concerted efforts to minimize residual variability in the data
are essential. These estimates can be used to obtain measures of
cumulative drug exposure for individual patients. Didanosine exposure,
however, did not appear to be related to changes in CD4 counts, percent
CD4 lymphocytes, or body weight over the initial 6 months of therapy. A
multivariate analysis of these data including evaluation of nonlinear
models is warranted. Other surrogate markers, such as viral
RNA levels, should be investigated for their potential relationships
with didanosine exposure.
| |
ACKNOWLEDGMENTS |
We thank the nurses and staff of the Immunodeficiency Clinic at
Erie County Medical Center for assistance in conducting this study. Tim
Adams, Heidi Chiang-Lew, and Cindy Steinwandel assisted in chart
reviews and data collection. Jill Fiedler-Kelly provided NONMEM
assistance, while Marianne Foley and Amy Hothow provided computer
support. William Jusko is acknowledged for his assistance with LAGRAN.
This project was supported by grant 01874-14-RGR from the American
Foundation for AIDS Research.
| |
FOOTNOTES |
*
Corresponding author. Mailing address: State University
of New York at Buffalo, 247 Cooke, North Campus, Amherst, NY 14260. Phone: (716) 645-3635, ext. 252. Fax: (716) 645-2001. E-mail: emorse{at}acsu.buffalo.edu.
| |
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Antimicrobial Agents and Chemotherapy, April 1998, p. 821-826, Vol. 42, No. 4
0066-4804/98/$04.00+0
Copyright © 1998, American Society for Microbiology. All rights reserved.
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Abstract
Introduction
