Key words
pharmacokinetics - anti-asthma / COPD drugs - clinical trials
Introduction
Asthma is a heterogeneous inflammatory disease that is characterized by a range of
different phenotypes [1]. In recent years, accumulating evidence is showing that prostaglandin D2 (PGD2)
plays an important role in the inflammatory process and pathophysiology in asthma
[2]. Through its interaction with PGD2 receptor 2 (DP2, also known as CRTH2), PGD2 is
involved in the migration and activation of inflammatory cells in asthma, including
eosinophils, basophils, Th2 cells, as well as type 2 innate lymphoid (ILC-2) cells
[3]. The recruitment of these cells into the lung is partially responsible for the intermittent
airway obstruction, which leads to the wheezing and shortness of breath characteristic
of asthma [4]
[5].
Fevipiprant, also known as QAW039, is a selective, competitive and reversible antagonist
of the human DP2 receptor [6]. By binding to DP2 receptors on the inflammatory cells, fevipiprant is expected
to inhibit migration and activation of these cells into the airway tissues, blocking
the PGD2-driven release of Th2 cytokines, and subsequently provide treatment benefits
in asthma patients [7]. Previous clinical evidence showed the treatment potential of fevipiprant in reducing
sputum eosinophil count and improving the lung function in asthma patients [8]
[9].
Luster 1 (CQAW039A2307, abbreviated as A2307, clinicaltrials.org: NCT02555683) and
Luster 2 (CQAW039A2314, abbreviated as A2314, clinicaltrials.org: NCT02563067) were
two Phase III studies investigating the efficacy and safety of fevipiprant in asthma
patients who are on medium or high dose of inhaled corticosteroids plus 1 or 2 additional
controllers [10]
[11]. Sparse pharmacokinetic (PK) samples were collected in these studies to characterize
the exposure of fevipiprant in this patient population.
The purpose of this analysis is to characterize the population PK of fevipiprant in
the patient population from the studies A2307 and A2314. Factors that might affect
the PK of fevipiprant in this population were explored.
Population PK parameters are often modeled on a lognormal scale, assuming a lognormal
distribution of PK data [12]. However, log-transformation of the population PK parameters might not be sufficient,
if the data is over-dispersed and/or skewed. Tukey’s g-and-h (TGH) distribution, which
includes skewness and tail-heaviness parameters, could be more advantageous to capture
such a non-normal distribution [13]. The present population PK analysis uses the TGH distribution to describe the over-dispersed
and skewed PK data observed in fevipiprant Phase III studies.
Methods
Study design
The Phase III studies were multi-center, randomized, double-blind, placebo-controlled
studies in asthma patients with a daily fevipiprant dose of 150 mg, 450 mg or matching
placebo for 52 weeks [10]. PK data were available on 4 different visits in the 52-week follow-up period. Two
PK samples were collected on each visit, including a pre-dose (Cmin) and a 2-hour
post-dose (Cmax) sample.
To support the population PK modeling and stabilize the model parameters, PK data
from 7 Phase I studies in healthy subjects were also included, in which rich PK sampling
was available. Among the included Phase I studies, there were 2 drug-drug-interaction
(DDI) studies and 1 bioequivalence study. To focus the analysis on relevant data,
cohorts in the DDI studies were excluded where fevipiprant was dosed together with
an interacting drug. Additionally, one cohort in the bioequivalence study was excluded
from the analysis, since the drug batch in this cohort was not used in either of the
Phase III studies.
Further details of the studies are given in [Table 1]. All studies were approved by corresponding ethics committee or independent review
board, and conducted according to the ethical principles of Declaration of Helsinki
and Good Clinical Practice. Written informed consent was obtained from all individual
participants prior to study enrolment.
Table 1 Summary of included studies
|
Study
|
Design
|
Dose and regimen
|
Patient population
|
PK sampling times
|
|
A2307
|
Phase III, efficacy, safety
|
150 and 450 mg QD for 52 weeks.
|
Asthma patients
|
Pre-dose and 2h-post dose on day 1, day 28, day 196 and day 364.
|
|
A2314
|
Phase III, efficacy, safety
|
150 and 450 mg QD for 52 weeks.
|
Asthma patients
|
Pre-dose and 2h-post dose on day 1, day 28, day 196 and day 364.
|
|
A2125
|
Phase I, bioequivalence
|
450 mg SD
|
Healthy subjects
|
0 (pre-dose), 0.25, 0.5, 1, 1.5, 2, 3, 4.5, 6, 8, 12, 24, 48, 72 and 96 h post dose
in period 1 and 2.
|
|
A2116
|
Phase I, DDI with cyclosporine
|
150 mg SD
|
Healthy subjects
|
0 (pre-dose), 0.25, 0.5, 1, 1.5, 2, 3, 4.5, 6, 8, 12, 24, 28, 36, 48, 60, 72, 84 and
96 h post dose in period 1 and 2.
|
|
A2120
|
Phase I, DDI with probenecid
|
150 mg SD
|
Healthy subjects
|
0 (pre-dose), 0.25, 0.5, 1, 1.25, 1.5, 2, 3, 4.5, 6, 8, 12, 24, 48, 72 and 96 h post
dose.
|
|
A1102
|
Phase I, Food effect in Japanese
|
450 mg SD
|
Healthy subjects
|
0 (pre-dose), 0.25, 0.5, 1, 1.5, 2, 3, 4, 6, 8, 12, 24, 48, 72 and 96 h post dose.
|
|
A2107
|
Phase I, Renal impairment
|
450 mg SD
|
Healthy subjects, patients with renal impairment
|
0 (pre-dose), 0.25, 0.5, 1, 2, 3, 5, 6, 8, 10, 24, 30, 48, 54 and 68 h post dose.
|
|
A2108
|
Phase I, Hepatic impairment
|
450 mg SD
|
Healthy subjects, patients with hepatic impairment
|
0 (pre-dose), 0.25, 0.5, 1, 2, 3, 6, 8, 12, 24, 48, 72, 96 and 120 h post dose.
|
|
A2126
|
Phase I, QT prolongation
|
450 mg, 1800 mg QD for 5 days.
|
Healthy subjects
|
0 (pre-dose), 0.25, 0.5, 1, 2, 3, 4, 5, 8, 12, and 24 h post dose on day 1 and day
5 in period 1 to 4.
|
DDI: Drug-drug interaction; QD: once daily; SD: single dose. All Phase I studies were
carried out in healthy subjects, except where indicated (A2107 and A2108); all Phase
III studies were carried out in asthma patients on medium or high dose inhaled corticosteroids
plus 1 or 2 additional controllers.
Analysis of plasma fevipiprant
Fevipiprant in plasma from all studies was analyzed by a validated liquid chromatography
equipped with tandem mass spectrometry (LC-MS/MS). The lower limit of quantification
is 1 ng/mL.
Missing data and samples below the lower limit of quantification (BLOQ)
Out of the 1281 subjects included in the PK analysis, 1 subject was reported to have
missing weight, 3 subjects with missing estimated glomerular filtration rate (eGFR)
and 4 subjects with missing absolute glomerular filtration rate (GFR). The missing
covariates were imputed by the population median values of the corresponding study.
There was no missing categorical covariate.
Approximately 5% of the post-dose PK samples were BLOQ. These samples were included
in the analysis by M3 censoring [14].
Data analysis and modeling methods
Model development and analysis were implemented in NONMEM 7.3 (ICON, Ellicott City,
MD) [15]. Perl-speaks-NONMEM (PsN) 4.6.0 was used to facilitate NONMEM processing [16]. The statistical package R 3.4.3 was used for post-processing of NONMEM outputs
[17]. All NONMEM model analysis was performed using a chain of expectation-maximization
methods consisting of Iterative Two Stage (ITS) method, Stochastic Approximation Expectation
Maximization (SAEM) method and Monte Carlo Importance Sampling (IMP). Goodness-of-fit
plots were used to guide model development.
Based on previous knowledge of fevipiprant PK, model development was initiated with
a 2-compartment model with first-order absorption, and parameterized using absorption
rate constant (Ka), apparent clearance (CL/F), apparent central volume of distribution (Vc/F), apparent inter-compartmental clearance (Q/F) and apparent peripheral volume of
distribution (Vp/F). Between-subject variability (BSV) was tested on all structural
parameters assuming a lognormal distribution. In case a parameter showed skewed and/or
over-dispersed distribution, the TGH distribution was evaluated to describe the BSV
as below [13]:
Here, η
i
represents the BSV of parameter θ. g controls skewness, h tail-heaviness and s the scale, respectively. Z
i
follows a standard normal distribution. The value of parameter θ in subject i (θ
i
) is a multiplication of the typical population value (θ) and the BSV (η
i
).
BSV was included on all structural parameters using MU reference to improve model
stability and efficiency [15]. Given the relatively sparse samples in Phase III studies, not all BSVs could be
estimated and, in such cases, they were fixed with a variance equal to 0.0081 (CV%:
9%). The value of 9% CV was chosen as a compromise between the values given in examples
from Bauer RJ and Bonate P., which ensures an efficient estimation of the population
mean parameters with similar individual parameter values in each subject [15]
[18].
A combination of additive and proportional error was tested in the model.
The effect of covariates (bodyweight, renal function, age, race and sex) on the PK
model was investigated after an adequate structural and stochastic model had been
identified. In the case of renal function, two alternative models were tested, one
with estimated glomerular filtration rate (eGFR) normalized to a body surface area
(BSA) of 1.73 m2, and the other with absolute GFR, which was derived from eGFR and individual BSA
[19]. The two models were compared and the one with better performance was carried forward.
Continuous covariates were modeled with a power relationship, after normalization
using population median values. For example, the effect of bodyweight on clearance
was described as:
Here, CLi is the individual CL/F in subject i, CL
pop
is the typical population value of drug clearance, WTi is the bodyweight of subject i, WTmedian is the median value of bodyweight in all subjects, θ is the covariate relationship of bodyweight and clearance, and η
i
is the BSV in subject i that follows a normal distribution with mean 0 and variance ω
2
.
Categorical covariates were modeled as a ratio to the population mean. For example,
the effect of sex on clearance was modeled as:
Covariate-parameter relationships were tested based on clinical interest, physiological
meaning and previous knowledge, supported by diagnostic plots. All the covariates
were included in a single full covariate model and jointly estimated [15]
[20]. Statistical significance was determined by whether the corresponding confidence
interval estimated by NONMEM included 1 (for categorical covariate estimated as ratio)
or 0 (for continuous covariates estimated as power), and supplemented by an assessment
of its impact on steady-state exposure.
Visual predictive checks (VPCs) and diagnostics based on conditional weighted residuals
(CWRES) and normalized prediction distribution errors (NPDE) were used to provide
an assessment of the model's ability to describe the data and suitability for simulation.
Simulations were conducted to investigate the impact of covariates on steady state
exposure. A typical patient was defined as an asthma patient with all covariate values
set to their respective median. Exposure was then simulated and compared with the
typical patient by varying covariate values, one at a time. Continuous covariates
took values at 5th and 95th percentile, while categorical covariates took values of all possible levels.
Results
Demographics and data distribution
Demographic information is summarized in [Table 2]. In total, 1281 subjects were included in the PK dataset, which consisted of 996
asthma patients and 285 healthy subjects. There were 37 adolescent patients in the
dataset (12 to < 18 years), with the remaining subjects being adults.
Table 2 Demographics
|
Study
|
N
|
Age (Year)
|
Weight (kg)
|
eGFR (mL/min/1.73m2)
|
GFR (mL/min)
|
|
A1102
|
14
|
31.5 [20–42]
|
56.8 [46.2–70.4]
|
120 [93.7–162]
|
114 [86.1–137]
|
|
A2107
|
45
|
60 [38–74]
|
81 [50.5–103]
|
74.8 [4.19–143]
|
78.3 [4.63–171]
|
|
A2108
|
42
|
59.5 [26–68]
|
80.8 [49.1–108]
|
85.6 [60.4–185]
|
92.9 [62.5–220]
|
|
A2116
|
16
|
28 [20–52]
|
78 [64.5–88.7]
|
99.9 [78.9–139]
|
116 [88.8–138]
|
|
A2120
|
16
|
27.5 [19–55]
|
65.2 [50.9–87.3]
|
104 [82.1–120]
|
105 [79–138]
|
|
A2125
|
108
|
34.5 [19–54]
|
73.7 [60.2–89.5]
|
97.2 [60.3–158]
|
104 [66.8–159]
|
|
A2126
|
44
|
30 [19–48]
|
77.6 [62–100]
|
104 [69.9–138]
|
118 [76.3–161]
|
|
A2307
|
507
|
52 [12–85]
|
74.9 [33–188]
|
85.6 [55–159]
|
90.4 [48.5–174]
|
|
A2314
|
489
|
52 [12–82]
|
76.6 [40–142]
|
86.4 [55.7–152]
|
91.8 [49–171]
|
|
Adolescents (12 – <18)
|
37
|
14 [12]
[13]
[14]
[15]
[16]
[17]
|
54 [33–114]
|
99.5 [67.7–132]
|
88.8 [54.1–128]
|
|
Adults (>= 18)
|
1244
|
50 [18–85]
|
75.4 [34–188]
|
87.8 [4.19–185]
|
93.2 [4.63–220]
|
|
Total asthma
|
996
|
52 [12–85]
|
75 [33–188]
|
86.1 [55–159]
|
91.2 [48.5–174]
|
|
Total HV *
|
285
|
38 [19–74]
|
74.6 [46.2–108]
|
97.2 [4.19–185]
|
105 [4.63–220]
|
|
All
|
1281
|
50 [12–85]
|
75 [33–188]
|
88.1 [4.19–185]
|
93 [4.63–220]
|
N: number of subjects. eGFR: estimated glomerular filtration rate. GFR: absolute glomerular
filtration rate. HV: healthy volunteers. Values are reported as median [Min–Max].
* Includes subjects with renal and hepatic impairments from A2107 and A2108 study.
The distributions of observed Cmax and Cmin are shown in [Fig. 1] on the logarithmic scale. There is a clear skewness in the Cmax. The distribution
of Cmin is approximately symmetric (no skewness) with long tails at both ends of the
distribution, indicating an over-dispersion.
Fig. 1 Distribution of observed Cmax and Cmin in Phase III studies. Data only included steady-state
PK samples from both Phase III studies with time after previous dose being 2 ± 1 hr
for Cmax and 24 ± 2 h for Cmin, respectively.
Population PK model
Fevipiprant PK was well described by a 2-compartment model with first-order absorption
and first-order elimination. Age, weight and renal function had a small, statistically
significant impact on CL/F. The model with eGFR and absolute GFR showed similar performance
and the one using absolute GFR was carried forward [21]
[22].
Data explorations revealed that observed Cmin in asthma patients from the Phase III
studies were consistently higher than those in healthy subjects from Phase I studies
(Supplement Fig. S1). To characterize such a systematic difference, a disease effect
was added on CL/F, Vc/F and Q/F, which used asthma patient as reference and estimated
the ratio in healthy subjects. The model also included a study effect on CL/F and
Ka to account for apparent differences that could not be explained by other covariates.
Additionally, a hepatic impairment factor was added on clearance to account for the
higher exposure observed in patients with moderate and severe hepatic impairment from
the hepatic impairment study.
The model estimated the BSV on CL/F, Vc/F and Q/F. A lognormal distribution was assumed
on Vc/F and Q/F. Given the over-dispersed and skewed distribution of observed data,
BSV on CL/F was modeled using a TGH distribution, which showed better description
of the observed data than a lognormal distribution (Supplement Fig. S2 and S3). BSV
on Ka, Vp/F and the TGH distribution parameters (g, h and s) were fixed with a variance of 0.0081.
A combination of proportional and additive error was used. The additive component
of the residual error converges towards 0. To stabilize the model, it was subsequently
fixed to a small value (1 × 10-4), which is significantly lower than the lower limit of quantification (1 ng/mL) and
thus should not have any observable impact on modeling results.
The parameter estimates for the final model are presented in [Table 3]. For a typical 48-year-old asthma patient with GFR of 93 mL/min and bodyweight of
75 kg, the total volume of distribution is approximately 558 L. Fevipiprant is cleared
from the central compartment with a linear clearance of 32.8 L/h. The relationship
of CL/F with its covariates is described by:
Table 3 Final model parameters
|
Parameter
|
Value (95% CI)
|
Shrinkage (%)
|
|
Structural parameters
|
|
CL/F (L/h)
|
32.8 (31.6–34)
|
|
|
Vc/F (L)
|
110 (99.6–121)
|
|
|
Ka (1/h)
|
0.572 (0.534–0.613)
|
|
|
Q/F (L/h)
|
8.58 (7.48–9.85)
|
|
|
Vp/F (L)
|
448 (435–461)
|
|
|
Covariates
|
|
CL/F: weight (power)
|
0.41 (0.296–0.525)
|
|
|
CL/F: age (power)
|
−0.108 (-0.178–0.0384)
|
|
|
CL/F: GFR (power)
|
0.218 (0.142–0.293)
|
|
|
CL/F: disease (ratio)
|
1.62 (1.52–1.73)
|
|
|
CL/F: A1102 (ratio)
|
0.857 (0.725–1.01)
|
|
|
CL/F: A2126 (ratio)
|
1.36 (1.2–1.53)
|
|
|
CL/F: Hepatic Impairment * (ratio)
|
0.487 (0.404–0.588)
|
|
|
Vc/F: disease (ratio)
|
1.3 (1.1–1.53)
|
|
|
Vc/F: Hepatic Impairment * (ratio)
|
0.445 (0.203–0.976)
|
|
|
Vc/F: weight (power)
|
0.835 (0.576–1.09)
|
|
|
Ka: A2107 (ratio)
|
1.32 (1.14–1.52)
|
|
|
Q/F: disease (ratio)
|
2.79 (2.36–3.31)
|
|
|
TGH distribution parameter
|
|
g
|
−0.046 (−0.149–0.0572)
|
|
|
h
|
0.602 (0.539–0.672)
|
|
|
s
|
0.255 (0.235–0.278)
|
|
|
Residual error (Standard deviation)
|
|
Proportional
|
0.649 (0.64–0.659)
|
8.5
|
|
Additive (ng/mL)
|
1×10-4 Fixed
|
|
|
Between subject variability (Variance)
|
|
BSV: CL/F #
|
0.0081 Fixed
|
|
|
BSV: Vc/F
|
0.606 (0.517–0.695)
|
23.6
|
|
BSV: Ka
|
0.0081 Fixed
|
|
|
BSV: Q/F
|
0.369 (0.292–0.447)
|
47.0
|
|
BSV: Vp/F
|
0.0081 Fixed
|
|
|
BSV: g
|
0.0081 Fixed
|
|
|
BSV: h
|
0.0081 Fixed
|
|
|
BSV: s
|
0.0081 Fixed
|
|
|
Z (TGH distribution)
|
1 Fixed
|
|
BSV: between subject variability. CI: confidence interval. CL/F: apparent clearance.
g: skewness parameter in TGH distribution. GFR: glomerular filtration rate. h: tail-heaviness
parameter in TGH distribution. Ka: absorption rate constant. Q/F: apparent inter-compartment
clearance. s: scale parameter in TGH distribution. TGH: Tukey’s g-and-h distribution.
Vc/F: apparent central volume of distribution. Vp/F: apparent peripheral volume of
distribution. Z: standard normal distribution component in TGH distribution. * Hepatic
Impairment includes patients from A2108 with moderate and severe hepatic impairments.
# Added on CL/F for MU-reference.
where θStudy equals to 0.857 for A1102 and 1.36 for study A2126. θHI, which represents the ratio of CL/F with moderate and severe hepatic impairment from
study A2108, was estimated to be 0.487. The BSV component is described by a TGH distribution:
Consistent with observed lower exposure, healthy subjects showed 62% higher CL/F,
30% higher Vc/F and 179%-fold higher Q/F.
All the structural and random effects have good precision with narrow 95% CI.
Model diagnostics and VPC
The goodness-of-fit was graphically evaluated ([Fig. 2]). Both the CWRES and NPDE showed an even distribution around the line of zero, when
plotted against time and population prediction, indicating a good description of observed
data by the final model. There is a deviation from the line of unity in the plot of
population prediction vs observation. Given that the VPC showed a proper characterization
of the data ([Fig. 3]), both at the central trend and the extremes, the model is deemed as appropriate
to describe the data. VPC for all the Phase I studies showed proper characterization
of the central trend, and there was a tendency that the model over-predicted variability
in healthy subjects (Supplement Fig. S4).
Fig. 2 Model diagnostics. Plot a and b are population prediction (EPRED) and individual
prediction vs observed concentration, respectively. Data were log-transformed before
plotting. Plot c and d are the NPDE (normalized prediction distribution errors) versus
population prediction (EPRED) and time after previous dose, respectively. Plot e and
f are the conditional weighted residuals (CWRES) versus population prediction and
time after previous dose, respectively. Crosses are the BLOQ (below the lower limit
of quantification) samples. Dashed lines are line of unity (y=x) in plot a & b and
line equal to 0 in plot c to f. Solid lines are the smooth regression line as implemented
in ggplot2 package under R.
Fig. 3 VPC plots of phase III studies. QD: once daily dose. VPC: visual predictive check.
Black open circles are observed concentrations. Central shaded areas represents the
95% CI (confidence interval) of the simulated median, and peripheral shaded area are
the 95% CI of 2.5th and 97.5th percentile of the simulated profiles. Solid and dashed
lines represent the median, 2.5th and 97.5th percentile of the observed concentrations.
Simulation
Simulations were conducted to investigate the impact of age, weight, absolute GFR
and disease effect on fevipiprant steady-state exposure. Exposure in healthy subjects
was simulated to be 37% lower than that in asthma patients (Supplementary Table S1 and Fig. S5). Age, weight and absolute GFR only showed limited impact on fevipiprant exposure;
by varying the covariate values from 5th to 95th percentile, the difference relative to a typical patient is at most 16% (Supplementary Table S2–S4 and Fig. S6–S8).
Simulation estimation with the final model
To investigate the appropriateness of the parameter estimation of the TGH distribution
in the final model, 24 datasets were simulated using the final model structure and
parameter values, including all the BSV and residual errors. The final model was then
used to estimate the parameter values on the simulated datasets.
An illustrative distribution of the simulated profiles is presented in Supplementary Fig. S9. Consistent with observed data, the simulated distribution also showed a skewed Cmax
and an over-dispersed Cmin.
Among the 24 simulated datasets, 21 datasets had their models converge successfully.
The estimated parameters from simulated datasets were compared to the parameter value
and their 95% CI of the final model. The majority of parameters from the simulated
datasets lie within the 95% confidence intervals of their respective parameters from
the final model, indicating that the TGH distribution as described in this report
was adequately estimated by the estimation algorithm ([Fig. 4]).
Fig. 4 Simulation estimation of the final model. HI: hepatic impairment. PropError: proportional
residual error. In total, 24 datasets were simulated and re-estimated from the final
model, and black open circles are the point estimates of parameter values from each
simulated dataset. Central dashed line and shaded area represent the parameter values
from the final model used for simulation and their corresponding 95% confidence intervals.
Parameters with fixed values in the model are not included in the plot.
Discussion
The population PK of fevipiprant in both healthy subjects and asthma patients was
well described by a two-compartment mixed effect model. Both the NPDE and VPC showed
that the model had an adequate prediction ability.
In particular, for the data in asthma patients from phase III studies, the model described
well both the central trend and the variability. For healthy volunteers in the Phase
I studies, the VPC showed in general a good prediction of the central trend but over-predicted
the variability. This is largely due to the lower variability in phase I studies compared
to phase III studies. Considering the good VPC for the asthma patient population and
the good residual based diagnostics, the derived final model is deemed adequate to
describe the observed data.
We observed a deviation from the line of unity in the plot of population prediction
versus observation. Due to the non-normal distribution of the observed data, such
an observation should be expected, and points to the fundamental flaws of this type
of diagnostic plots as discussed in a previous report [23]. After accounting for the BSV, the individual prediction vs observation showed good
agreement with the line of unity and CWRES and NPDE versus predictions did not show
any obvious residual trend. Consequently, this deviation should not be of concern
in terms of model quality.
The exact reason for the over-dispersion and skewness in observed PK data is unclear;
however, this was a major challenge of modeling, since the usual modeling methodology
was not designed to accommodate such a distribution. Modeling the data on log scale
did not provide any improvement in model performance. Although there are different
methods proposed in the past to model the non-Gaussian data, the TGH distribution
used in the current model is a transformation of standard normal distribution with
different parameters for skewness and over-dispersion [13]
[24]. The observed PK data could be modeled on the original scale without any complex
transformation. Together with the scaling factor, it is a flexible alternative to
characterize the non-normal data. Diagnostic plots and VPCs showed that it is better
than a lognormal distribution, and appropriate to describe the observed data.
The simulation-estimation using the final model showed that NONMEM could successfully
estimate the parameters. Even though such an experiment is not a fully-fledged evaluation
of the distribution for population PK modeling in general, the successful re-estimation
of model parameters indicates that at least for the current model the distribution
could be properly characterized in NONMEM. The SAEM estimation algorithm implemented
in NONMEM and used here is especially good at this type of non-normal data, which
might be another factor for the successful application of TGH in the model [25].
Overall, the model structure and distribution assumptions are appropriate for the
observed data, although there remain some limitations. For example, the QQ-plot of
BSV on CL/F with the TGH distribution, even though an improvement compared with lognormal
distribution was seen, there was still certain level of deviation from line of unity
(Supplement Fig. S2 and Fig. S3). This might be a result of the possibility that parameters
other than CL/F also follow a non-normal distribution. However, due to model complexity,
it was not possible to include further TGH components in the model. Considering the
overall good VPC, this limitation is not deemed to interfere with the interpretation
and application of the modeling results.
The model confirmed a significant difference between healthy volunteers and asthma
patients. Such a difference was also reported for salbutamol, where mild asthmatic
patients showed higher exposure than healthy volunteers after oral dosing [26]. One possible explanation for this phenomenon is the increased gastrointestinal
permeability leading to the higher exposure in asthma patients [27]
[28]
[29]. Human ADME study estimated that approximately 44% of fevipiprant was absorbed in
healthy volunteers after oral dosing [30]. The fact that CL/F, Vc/F and Q/F are all significantly higher in healthy volunteers
might be a further indication of a lower bioavailability in healthy volunteers than
that in asthma patients. Nonetheless, it should be noted that disposition of fevipiprant
in human involves multiple phase II metabolism enzymes, uptake and efflux transporters
[30]. Previous reports showed that asthma disease and the commonly used medications all
had the potential to affect drug metabolizing enzymes and transporters [31]
[32]
[33]. Thus, we cannot exclude the possibility that mechanisms other than absorption could
also have contributed to the observed difference.
Simulation with the final model showed that steady-state exposure in asthma patients
is expected to be 37% higher than that in healthy volunteers. Due to the relatively
small covariate values, all the other statistically significant covariates only produced
a limited impact on exposure, which is at most 16% and not considered clinically relevant.
Conclusions
Fevipiprant PK was described by a two-compartment model with first-order absorption
and first-order elimination. Asthma patients had approximately 37% higher exposure
than that in healthy subjects. Other covariates changed exposure by at most 16%. The
TGH distribution was appropriate to describe the over-dispersed and skewed data observed
with fevipiprant.
