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\begin{document}

\title{A novel population pharmacokinetic model for recombinant factor IX-Fc
fusion concentrate including young children with haemophilia B}



\author[1]{Sjoerd Koopman}%
\author[2]{Tine Goedhart}%
\author[3]{Laura Bukkems}%
\author[3]{Trevor Mulders}%
\author[4]{Frank Leebeek}%
\author[1]{Karin Fijnvandraat}%
\author[5]{Michiel Coppens}%
\author[6]{Mary Mathias}%
\author[7]{Peter Collins}%
\author[8]{Campbell Tait}%
\author[9]{Nicola Curry}%
\author[10]{Jeanette Payne}%
\author[11]{Pratima Crowdary}%
\author[2]{Marjon Cnossen}%
\author[12]{Ron Mathot}%
\affil[1]{Amsterdam UMC Locatie AMC}%
\affil[2]{Erasmus MC Sophia Children Hospital}%
\affil[3]{Amsterdam University Medical Centres}%
\affil[4]{Erasmus MC}%
\affil[5]{Amsterdam UMC Location AMC Department of Vascular Medicine}%
\affil[6]{Great Ormond Street Hospital for Children NHS Foundation Trust}%
\affil[7]{Cardiff University School of Medicine}%
\affil[8]{University of Glasgow}%
\affil[9]{Oxford University Hospitals NHS Foundation Trust Nuffield Orthopaedic Centre}%
\affil[10]{Sheffield Children's Hospital NHS Foundation Trust}%
\affil[11]{Royal Free London NHS Foundation Trust}%
\affil[12]{Amsterdam UMC - Locatie AMC}%


\vspace{-1em}



  
  \date{May 21, 2024}


\begingroup
\let\center\flushleft
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\selectlanguage{english}
\begin{abstract}
Background Recombinant factor IX Fc fusion protein (rFIX-Fc) is an
extended half-life (EHL) factor concentrate administered to haemophilia
B patients. So far, a population pharmacokinetic (PK) model has only
been published for patients [?]12 years of age. Aim Assess the predictive
performance of the published rFIX-Fc population PK model for patients of
all ages and develop a model that describes rFIX-Fc PK using real world
data. Methods We collected prospective and retrospective data from
patients with haemophilia B (FIX activity level [?]5 IU/dL) treated with
rFIX-Fc and included in the OPTI-CLOT TARGET study (NTR7523) or United
Kingdom (UK)-EHL Outcome Registry (NCT02938156). Predictive performance
was assessed by comparing predicted with observed FIX activity levels. A
novel population PK model was constructed using nonlinear mixed-effects
modelling. Results Real world data was obtained from 37 patients (median
age: 16 years, range 2-71) of whom 14 were \textless{}12 years of age.
Observed FIX activity levels were significantly higher than levels
predicted using the published model, with a median prediction error (PE)
of -48.8\%. The novel model showed a lower median PE (3.4\%) and better
described rFIX-Fc PK, especially for children \textless{}12 years of
age. In the novel model, an increase in age was correlated with a
decrease in clearance (p\textless{}0.01). Conclusion The published
population PK model significantly underpredicted FIX activity levels.
The novel model better describes rFIX-Fc PK, especially for children
\textless{}12 years of age. This study underlines the necessity to
strive for representative population PK models, thereby avoiding
extrapolation outside the studied population.%
\end{abstract}%



\sloppy


\textbf{A novel population pharmacokinetic model for recombinant factor
IX-Fc fusion concentrate including young children with haemophilia B}

Sjoerd F. Koopman\textsuperscript{1*}, Tine M.H.J.
Goedhart\textsuperscript{2*}, Laura H. Bukkems\textsuperscript{1},
Trevor M. Mulders\textsuperscript{1}, Frank W.G.
Leebeek\textsuperscript{3}, Karin Fijnvandraat\textsuperscript{4},
Michiel Coppens\textsuperscript{5,6}, Mary Mathias\textsuperscript{7},
Peter W. Collins\textsuperscript{8}, R. Campbell
Tait\textsuperscript{9}, Catherine Bagot\textsuperscript{9}, Nicola
Curry\textsuperscript{10}, Jeanette Payne\textsuperscript{11}, Pratima
Chowdary\textsuperscript{12}, Marjon H. Cnossen\textsuperscript{2*} \&
Ron A.A. Math\selectlanguage{ngerman}ôt\textsuperscript{1*}, for the OPTI-CLOT study group and
SYMPHONY consortium.

\textsuperscript{1}Hospital Pharmacy-Clinical Pharmacology, Amsterdam
University Medical Centers, Amsterdam, The
Netherlands;\textsuperscript{2}Department of Pediatric Hematology and
Oncology, Erasmus MC Sophia Children's Hospital, University Medical
Center Rotterdam; Rotterdam, The Netherlands;
\textsuperscript{3}Department of Hematology, Erasmus MC, University
Medical Center Rotterdam, Rotterdam, The Netherlands;
\textsuperscript{4}Amsterdam UMC, University of Amsterdam, Emma
Children's Hospital, Pediatric Hematology, Amsterdam, The Netherlands;
\textsuperscript{5}Vascular Medicine, Amsterdam UMC, University of
Amsterdam, Amsterdam, The Netherlands;\textsuperscript{6}Amsterdam
Cardiovascular Sciences, Pulmonary Hypertension \& Thrombosis,
Amsterdam, The Netherlands;\textsuperscript{7}Haemophilia Comprehensive
Care Centre, Great Ormond Street Hospital for Children NHS Foundation
Trust, London, United Kingdom; \textsuperscript{8}Arthur Bloom
Haemophilia Centre, School of Medicine, Cardiff University Hospital,
Cardiff, United Kingdom;\textsuperscript{9}Department of Haematology,
Royal Infirmary, Glasgow, United Kingdom; \textsuperscript{10}Oxford
Haemophilia and Thrombosis Centre and Oxford NIHR BRC, Nuffield
Orthopaedic Hospital, Oxford, United Kingdom;
\textsuperscript{11}Department of Paediatric Haematology, Sheffield
Children's NHS Foundation Trust, Sheffield, United Kingdom;
\textsuperscript{12}Katharine Dormandy Haemophilia Centre and Thrombosis
Unit, Royal Free London NHS Foundation Trust, London, United Kingdom

*shared first and last authorships

\textbf{Corresponding author:}

Prof. R.A.A. Mathôt, PharmD, PhD

Hospital Pharmacy-Clinical Pharmacology

Amsterdam University Medical Center, University of Amsterdam

Meibergdreef 9, P.O. Box 22660, 1100 DD Amsterdam, The Netherlands

Email address: r.mathot@amsterdamumc.nl

\textbf{Journal: BJCP}

Words: 3826 (limit: 4000)

Abstract: 250 words (limit: 250)

References: 42 (limit: no limit)

Tables and figures: 2 tables 4 figures (limit: 8 tables/figures total)
(3 tables 1 figure in supplementary)

Short title: Novel rFIX-Fc population PK model

\textbf{ABSTRACT}

\textbf{Background}

Recombinant factor IX Fc fusion protein (rFIX-Fc) is an extended
half-life (EHL) factor concentrate administered to haemophilia B
patients. So far, a population pharmacokinetic (PK) model has only been
published for patients \selectlanguage{english}[?]12 years of age.

\textbf{Aim}

Assess the predictive performance of the published rFIX-Fc population PK
model for patients of all ages and develop a model that describes
rFIX-Fc PK using real world data.

\textbf{Methods}

We collected prospective and retrospective data from patients with
haemophilia B (FIX activity level \selectlanguage{english}[?]5 IU/dL) treated with rFIX-Fc and
included in the OPTI-CLOT TARGET study (NTR7523) or United Kingdom
(UK)-EHL Outcome Registry (NCT02938156). Predictive performance was
assessed by comparing predicted with observed FIX activity levels. A
novel population PK model was constructed using nonlinear mixed-effects
modelling.

\textbf{Results}

Real world data was obtained from 37 patients (median age: 16 years,
range 2-71) of whom 14 were \textless{}12 years of age. Observed FIX
activity levels were significantly higher than levels predicted using
the published model, with a median prediction error (PE) of -48.8\%. The
novel model showed a lower median PE (3.4\%) and better described
rFIX-Fc PK, especially for children \textless{}12 years of age. In the
novel model, an increase in age was correlated with a decrease in
clearance (p\textless{}0.01).

\textbf{Conclusion}

The published population PK model significantly underpredicted FIX
activity levels. The novel model better describes rFIX-Fc PK, especially
for children \textless{}12 years of age. This study underlines the
necessity to strive for representative population PK models, thereby
avoiding extrapolation outside the studied population.

\textbf{Keywords:} pharmacokinetics, haemophilia B, factor IX, extended
half-life

\textbf{INTRODUCTION}

Haemophilia B is an inherited bleeding disorder caused by mutations in
the \emph{F9} -gene on the X-chromosome\textsuperscript{1}. These
mutations result in a coagulation factor IX (FIX) deficiency, leading to
impaired haemostasis. Severely and moderately affected haemophilia B
patients suffer from spontaneous bleeding or bleeding after minor
trauma, especially into joints and muscles. When left untreated, these
bleeds may be life-threatening or lead to arthropathy with ultimately
long-term disability\textsuperscript{2}. FIX replacement therapy - both
prophylactically and on demand - is mainstay of treatment, leading to a
normal life expectancy with good quality of life\textsuperscript{3}.
Extended half-life (EHL) FIX concentrates have further ameliorated the
burden of disease by substantially decreasing the frequency of
intravenous FIX concentrate administration to on average once every
week\textsuperscript{4}.

Recombinant factor IX Fc fusion protein (rFIX-Fc) is an EHL-FIX
concentrate which consists of a single recombinant FIX molecule fused to
the dimeric Fc domain of human immunoglobulin G1
(IgG1)\textsuperscript{5}. This fusion delays the lysosomal degradation
by recycling rFIX-Fc back into circulation. As a result, half-life is
prolonged from 17h for rFIX to 82h for rFIX-Fc in patients \selectlanguage{english}[?]12 years of
age\textsuperscript{4}. The pharmacokinetics (PK) of FIX concentrates
are complex and demonstrate a high level of interindividual variability
(IIV)\textsuperscript{6--9}. As a result, FIX activity levels vary
substantially between patients\textsuperscript{10--12}. The variability
in PK parameters of individual subjects entails individual adjustments
for administration of FIX replacement therapy by application of Bayesian
forecasting. This application using population PK models has been shown
to be successful to individualize factor concentrate dosing in
haemophilia treatment\textsuperscript{13,14}. Furthermore, Bayesian
forecasting methodology allows for limited sampling in contrast to
traditional modelling methods\textsuperscript{15}.

To establish the PK characteristics of rFIX-Fc and identify covariates,
Diao et al. developed a rFIX-Fc three-compartment population PK
model\textsuperscript{16} using data from several clinical trials. To
our knowledge, this is the only population PK model currently published
for this EHL factor concentrate. Importantly, this model has not been
externally validated. Moreover, this model was constructed using data of
only a limited number (n = 11) of children, all \selectlanguage{english}[?]12 and \textless{}18
years of age. Therefore, the accuracy of this model in children
\textless{}12 years of age may be limited. The aim of this study is to
validate and assess the predictive performance of the published rFIX-Fc
model using new independent real world patient data. The secondary aim
is to develop a novel population PK model describing the PK in a more
extended age range, including children \textless{}12 years of age.

\textbf{METHODS}

\textbf{Data collection}

We collected data from haemophilia B patients (endogenous FIX activity
level \selectlanguage{english}[?] 5 IU/dL) treated with rFIX-Fc (eftrenonacog alfa, Alprolix(r))
included in the OPTI-CLOT TARGET study (NTR7523)\textsuperscript{17} or
United Kingdom (UK)-EHL Outcome Registry (NCT02938156). Briefly,
haemophilia patients in the OPTI-CLOT TARGET study received nine months
of PK-guided dosing to investigate the reliability and feasibility of
the Bayesian forecasting procedure. FIX samples for PK profiling were
obtained pre-infusion and approximately 15-30 minutes, 4, 24, 72-120 and
168h after infusion. During PK-guidance, a minimum of four FIX activity
(at nonspecific time points after infusion) levels per patient was
collected in a minimum of two visits to validate predicted FIX. The
UK-EHL Outcome Registry contains patient characteristics and treatment
information, including FIX infusions (timing and doses) and FIX activity
level measurements. During PK profiling, FIX activity levels were
measured at pre-infusion and approximately 15 minutes, 24, 72, 120 and
168h after infusion. Additional FIX activity levels were sampled during
visits at 10 days, 3, 6, 12 and 18 months after initiation of rFIX-Fc
treatment. In both studies, no wash-out was required during PK profiling
if three prior infusions were documented. Informed consent was obtained
from all patients and/or caregivers.

\textbf{Patient data handling}

In both cohorts FIX activity was measured using the one-stage assay
(OSA) according to local protocol. Laboratory specifications of all
participating sites are shown in Supplementary Table 1. All FIX activity
levels measured during bleeding episodes or
surgeries\textsuperscript{18} were excluded from this analysis.

Since the OSA does not distinguish FIX activity from the respective FIX
concentrates (e.g. residual FIX activity levels from the previous FIX
dose or currently present FIX concentrate), it is required to correct
for previously administered factor concentrates. To do so, we performed
the following corrections in line with Diao et
al.\textsuperscript{16}and previously reported PK analyses with FIX
concentrates\textsuperscript{9,12,19,20}:

\(\left(1\right)\ Residual\ decay\ correction=\left(Predose\ activity-baseline\right)*e^{-kt}\)

\(\left(2\right)\ Corrected\ FIX\ activity=Measured\ FIX\ activity-baseline-residual\ decay\ correction\)

\(\left(3\right)\ k=\frac{\ln\left(2\right)}{t_{1/2}}\);

in which \emph{k} represents the elimination rate constant of the
previously used concentrate for rFIX-Fc (Alprolix(r)), rFIX (Benefix(r)) or
rIX-FP (Idelvion(r)) calculated for each age group (\textless{}6, \selectlanguage{english}[?]6-12,
\selectlanguage{english}[?]12-18 and \selectlanguage{english}[?]18 years). For these calculations, we used typical
half-lives (t\textsubscript{1/2}) of each age group as reported by the
respective European Public Assessment Report
(EPAR)\textsuperscript{21--23}. Other patient characteristics collected
were age, height, body weight, lean body weight (LBW) and fat free mass
(FFM). Occasions were defined as a visit with PK assessment, as
described in literature\textsuperscript{24}.

\textbf{Validation of published population pharmacokinetic model}

The predictive performance of the published rFIX-Fc population PK model
by Diao et al.\textsuperscript{16} was assessed with our data using
NONMEM software (v7.4.1, Icon Development Solutions, Gaithersburg,
Maryland, United States)\textsuperscript{25}. Data visualization and
evaluation were performed in R (version 4.1.1), Pirana (version 2.9.8)
and PsN (version 4.8.1). Predictive performance was visualized in
goodness-of-fit (GOF) plots showing predicted versus observed FIX
activity levels. A priori population predicted (PRED) activity was
obtained using typical PK parameters which can be calculated on basis of
patient characteristics (e.g. body weight). Individual PK parameters
were obtained after Bayesian estimation providing a posteriori
individual predicted activity (IPRED). Next, predictive performance was
evaluated by comparing predicted versus observed FIX activity levels.
The prediction error (PE, Eq. 4) was determined to assess bias. The root
mean squared error (RMSE, Eq. 5) was determined to elaborate on
differences between individual predictions of the published and novel
model.

\begin{equation}
\left(4\right)\text{\ PE}=\ (\frac{C_{\text{pred}}-C_{\text{obs}}}{C_{\text{obs}}})*100\%\ \nonumber \\
 \end{equation}\begin{equation}
\left(5\right)\ RMSE=\ \sqrt{\frac{\sum_{j=1}^{n}{(C_{\text{ipred}}-C_{\text{obs}})}^{2}}{n}}\nonumber \\
 \end{equation}

\emph{Cpred} represents the population predicted and \emph{Cipred} the
individually predicted FIX activity level of measurement \emph{j}
.\emph{Cobs} represents the observed FIX activity level. The total
number of measurements is denoted by \emph{n} . A negative or positive
PE indicates a systematic under- or overestimation of population
predicted FIX activity levels. A median PE between -5\% and 5\% is
deemed as not biased. RMSE was determined for peak (time after dose 0-2
h), mid (time after dose 2-120 h) and trough (time after dose 120-300 h)
FIX activity levels separately.

Furthermore, for patients \textless{}12 years of age, we investigated
potential bias due to possible relationships between covariates and
population PK parameters volume of central compartment (V1), volume of
peripheral compartment (V2), clearance (CL) and intercompartmental
clearance (Q). Therefore, we plotted interindividual variability
(ETA;\selectlanguage{greek}\emph{η} \selectlanguage{english}) in these PK parameters against the patient
characteristics age and body weight. Plots of an unbiased model should
not show trends, indicating that \selectlanguage{greek}\emph{η} \selectlanguage{english}in these PK parameters are
divided randomly over patient characteristics.

Finally, terminal elimination half-lives (t\textsubscript{1/2}) were
determined by post hoc calculation for patients \textless{}12 years of
age, patients [?]12 and \textless{}18 years of age and adults. Results
were compared with results from the novel model (see below). As the
t\textsubscript{1/2} estimates are influenced by the number of
compartments\textsuperscript{26}, the respective compartments of both
models were taken into account.

\textbf{Development of a novel population pharmacokinetic model}

When the predictive performance of the published model was inadequate,
an alternative population PK model was constructed. During construction,
the number of compartments was evaluated. In this study, the initial
visit with PK profiling was considered as the first occasion. Subsequent
occasions were defined as a visit with a PK assessment. PK parameters
were expressed by CL, Q, and V; inter-individual (IIV) and
inter-occasional variability (IOV) of these parameters was estimated.
Residual error is described with a combined additive and proportional
model. We evaluated candidate models by examination of PK parameter
estimates, their respective residual standard errors (RSE), objective
function value (OFV), GOF plots and visual predictive checks (VPC).

Stepwise covariate modelling (SCM) was used to perform covariate
analysis applying the generalized additive models (GAM)
approach\textsuperscript{27,28}. This approach allows to test if
potential patient characteristics are able to explain IIV and IOV in PK
parameters. We applied a forward inclusion and backward elimination
process. Age, height, body weight, LBW, FFM, BMI and centre of inclusion
were available and explored as covariates. Allometric scaling was
applied with fixed exponents of 0.75 for CL and 1.00 for
V\textsuperscript{29,30}. As height was not available in two patients,
their height was fitted by a linear regression model based on available
height and age of other patients, and used to calculate LBW and BMI. We
explored the impact of the centre on FIX predictions as haemophilia
treatment centres used different laboratory specifications according to
local protocol. This was tested by incorporating a residual error per
centre.

In the SCM, covariates were screened for relevance by univariate
analysis. Improvement of the model was deemed significant if addition of
a covariate to the model decreased the OFV (\selectlanguage{greek}Δ\selectlanguage{english}OFV) with 3.84
(p\textless{}0.05, Chi-square distribution, 1 \emph{df} ). When two
parameters were added simultaneously, e.g. during expansion of a
two-compartment model to a three-compartment model, a \selectlanguage{greek}Δ\selectlanguage{english}OFV of -5.99
(p\textless{}0.05, Chi-square distribution, 2 \emph{df} ) was warranted.
Subsequently, all significant covariates were simultaneously added to
the model, followed by backward elimination. Elimination of a covariate
that resulted in an OFV increase of \textgreater{}6.64
(p\textless{}0.01, Chi-square distribution, 1 \emph{df} ) was regarded
as a significant improvement to the model.

The novel population PK model was internally validated with a visual
predictive check (VPC) to compare the distribution of the observations
with the distribution of the predictions. The robustness of the
parameter estimates was assessed by bootstrap analysis. Bias of the
novel population PK model were assessed throughout the PE (Eq. 4).

\textbf{Individual dosing advice}

To evaluate the clinical impact of the choice of model on dosing
regimens, we compared the dose (IU) for each individual with a PK
profile assessment of rFIX-Fc (n=36) as calculated by application of
Bayesian forecasting using both the published and the developed novel
model. Individual PK parameters were calculated for the clinical
situation in which a peak, trough and random mid FIX activity level were
available. Doses were targeted at maintaining a FIX level
\textgreater{}3 IU/dL at 168h after infusion of rFIX-Fc during
steady-state (Dose\textsubscript{3\%}). We wanted to perform Bayesian
forecasting on data that was not included in the development of the
model. Hence, five separate datasets including 29-30 patients were
created on which population PK parameters were estimated. These
estimates were used for Bayesian forecasting using three individual
samples of the remaining 6-7 patients not included in the dataset.
Differences in calculated doses between the two models were explored by
the permutation test, as the doses were not normally distributed and
contained too many ties to perform a Wilcoxon signed rank test. This
analysis was also performed separately for children \textless{}12 years
of age, since the previously published model did not include children
\textless{}12 years of age, whereas the newly developed model did.

\textbf{RESULTS}

\textbf{Patient characteristics and pharmacokinetic profiling}

Real world data from 35 severe and 2 moderately-severe haemophilia B
patients was available for assessment of the predictive performance of
the published rFIX-Fc population PK model (Table 1). Median age was 15.8
years (range 2.3-71.0), and 14 patients were below the age of 12 years.
Patients received a median dose of 36 IU/kg rFIX-Fc concentrate (range
10 -- 132 IU/kg). In total, 287 FIX activity levels measured by OSA were
available for analysis. Three FIX activity levels (1\% of the data) were
below LLOQ and therefore excluded in the
analysis\textsuperscript{29,31}. During PK profiling, a median of five
FIX activity levels (range 3-7) in adolescent and adult patients ([?]12
years of age) and four FIX activity levels (range 3-7, mean 4.5) in
children (\textless{}12 years of age) were sampled. PK data was obtained
during a median of 2 occasions per individual (range 1-9).

\textbf{Predictive performance of the published model}

The predictive performance of the published population PK model for
rFIX-Fc was evaluated by comparing the model-predicted and observed FIX
activity levels. Figure 1A and Figure 1B present the population
predictions goodness-of-fit (GOF) plots for all patients and children
\textless{}12 years of age separately. Observed FIX activity levels are
higher than their respective predictions (Fig. 1A and 1B) and a clear
deviation of trend lines from identity lines can be seen in all patients
(Figure 1A), but especially in children \textless{}12 years of age
(Figure 1B). These observations indicate structural bias
(underprediction) of the published model. This is also illustrated by
the median PE of -48.8\% (IQR: -29.9 -- -63.9) for all patients and
-54.1\% (IQR: -43.3 -- -65.8) for children \textless{}12 years of age
(Supplementary Table 2A). The RMSE is shown in Supplementary Table 2B.

Furthermore, deviations were observed in plots of conditional weighted
residuals (CWRES) versus population predictions (PRED, Sup. Fig. 1A) and
time after dose (TAD, Sup. Fig. 1B).

Bayesian analysis was performed to obtain individual PK parameter
estimates. For children \textless{}12 years of age, Figure 2A and 2B
show the deviation from the individual PK estimate from the typical
population value over the weight range. For the evaluation of these
graphs, it is important to realize that an adequate population model
would have random inter-patient variability with an average of zero and
no trend with weight. Figure 2A and 2B clearly demonstrate that
children's CL and V1 are lower than would typically be expected over the
studied weight range and advocate the development of a new model.

\textbf{Development of the novel model}

\emph{Structural model}

A novel rFIX-Fc population PK model using clinical data including
children \textless{}12 years of age was developed (Table 2). A
two-compartment model with a central and a peripheral compartment
adequately described our data. Addition of a second peripheral
compartment did not improve the fit of the model to the data. All PK
parameters were allometrically scaled. With allometric scaling, body
weight is included in the structural model. IIV could be estimated for
CL, V1 and V2, with a correlation between CL and V1. The clinical data
supported the estimation of IOV on CL.

\emph{Covariate analysis}

Age, height, LBW, FFM, BMI and centre of inclusion were explored as
covariates. In univariate analysis - using the structural model without
application of allometric scaling -- the separately weight-related
covariates body weight, FFM and LBW were significantly related to CL and
V1. We chose however to allometrically scale these parameters with body
weight as it is easy and routinely measured, as opposed to LBW and FFM.
Incorporation of a separate residual error for one haemophilia treatment
centre improved the model (p\textless{}0.05), but was not significant
after the backward selection reprocess (p\textless{}0.01). Therefore,
all centres were described by the same residual error model. CL
decreased with age; the latter was the only covariate that improved the
fit of the model to the data. On basis of this relationship typical
clearance of a 73 kg patient would decrease from 1.89 dL/h at an age of
20 years to 1.36 dL/h at an age of 70 years.

\emph{Diagnostics of the novel model}

The internal validity throughout the visual predictive check (Figure 3)
shows observed FIX activity levels being adequately predicted by the
novel model. Bootstrap results are presented in Table 2. The trend lines
in the GOF plots are close to the line of identity for both all patients
(Fig. 1C) and children \textless{}12 years of age (Fig. 1D). The trend
line in children \textless{}12 years of age (Fig. 1D) shows a slight
deviation at high FIX activity levels, but this may be caused by the
sparse number of samples in this range. A slight bias was detected for
the novel model, as the median PE was 3.4\% (IQR -22.2 -- 25.8) for all
patients and 6.5\% (IQR -20.8 -- 27.5) for children \textless{}12 years
of age (Supplementary Table 2A).

CWRES plots (Sup. Fig. 2C and Fig. 2D) show an improved fit compared to
the published model (Sup. Fig. 2A and 2B). In addition, the vast
majority of values in the new model is within the warranted -2 and 2
range\textsuperscript{32}, in contrast to the values of the published
model.

Typical parameter values (Table 2) differed between the published and
novel model. For a typical 16 year old 73 kg patient, CL was 1.41 dL/h
and lower than the value of the published model (2.39 dL/h). In
addition, distribution volume at steady-state (Vss) was lower as well
with respective values of 153 dL and 198 dL. Likewise, terminal
t\textsubscript{1/2} was lower in the novel model compared to the
published model for children \textless{}12 years of age (70 h vs 88 h),
adolescents [?]12 and \textless{}18 years of age (76 h vs 99 h) and adults
(88 h vs 101 h) (Supplementary Table 3).

Lastly, the validity of the model for children \textless{}12 years of
age is illustrated in Figure 2C and 2D. The figure demonstrates random
variability of CL and V with an average not different from zero. Of
note, an adequate covariate model shows no trend and deviations from
zero in the IIV.

\textbf{Individual dosing advice}

To maintain a FIX level \textgreater{}3 IU/dL 168h after rFIX-Fc
infusion, individual doses were calculated by application of Bayesian
forecasting using the published and the novel model by taking three
clinically relevant samples into account (Figure 4). The individually
predicted Dose\textsubscript{3\%} was significantly higher
(p\textless{}0.01) when predicted by the published model (median 1750 IU
(range 250-3500)) than with the novel model (median 1500 IU (range
250-2000)) when all patients were considered. Surprisingly, however,
when focusing on children \textless{}12 years no significant differences
in Dose\textsubscript{3\%} were found. Median dose was 750 IU (range
250-2500) and 1000 IU (range 250-1750) (p=0.63).

\textbf{DISCUSSION}

In this study, the predictive performance of a published rFIX-Fc
population PK model was evaluated using independent real world
data\textsuperscript{16}. The published model was based on patients [?]12
years whereas in this study children with age \textless{}12 years were
included as well. The published model significantly underpredicted the
observed FIX activity levels in all patients, especially for children
\textless{}12 years of age. Consequently, a new population PK model was
developed which should preferably be used to perform PK guided dosing in
young children.

Compared to the previously published model, our newly developed model
better describes the PK profiles of children \textless{}12 years of age
that were included. These improvements are not surprising as weight
normalized CL and V1 are generally larger in children compared to
adults\textsuperscript{12}. This phenomenon has also been reported for
recombinant factor VIII-Fc fusion protein
(rFVIII-Fc)\textsuperscript{33}. For children \textless{}12 years
specifically, the novel model shows adequate characterization of CL and
V1 (Fig. 2C and 2D).

Observed inter-patient variability of CL and V1, and within-patient
variability of CL were somewhat increased in comparison to reported
values (Table 2). As real word data is obtained from a highly
heterogenic population, a larger variability is imminent compared to
selected clinical study populations. This also explains why the residual
proportional error in the novel model (16.3\%) was slightly higher
compared to the published model (10.6\%) (Table 2). Real world clinical
data may contain more noise due to variability in assay precision,
variability in administration and sample times.

Surprisingly, this study found a near two-fold lower typical clearance
than reported by Diao et al.\textsuperscript{16} (Table 2). A possible
explanation for this may be related to the neonatal Fc receptor (FcRn),
to which the Fc domain of the IgG1 molecule in rFIX-Fc binds. FcRn
concentrations are negatively correlated with body
weight\textsuperscript{34}. Consequently, children have higher
concentrations of weight-adjusted FcRn, possibly resulting in lower CL.
This is in contrast to the expected higher FIX CL in children, as is
found in factor VIII (FVIII)\textsuperscript{35,36}. As half of our
population was paediatric (\textless{}18 years) and 38\% was
\textless{}12 years of age, the age related effect on FcRn may have
influenced CL estimation.

Our real world clinical data was best described by a two-compartment
model and not by a three-compartment model as previously constructed by
Diao et al.\textsuperscript{16} This is due to differences in sampling
times during PK profiling between both study populations. More
specifically, the published model was constructed based on a rich
sampling schedule during a 10-day period, whereas the current study used
a maximum of six FIX activity levels sampled during a 7-day period. In
the present study, less FIX activity levels were sampled at early time
points. This could explain why we were not able to describe a third
compartment that characterizes the rapid distribution phase of rFIX-Fc
occurring within 2-3 hours after the end of
administration\textsuperscript{37}. Notwithstanding these limitations,
our model adequately described the terminal elimination phase which
determines the trough concentration on which doses are generally
adjusted for in clinical practice.

The observed difference in terminal t\textsubscript{1/2} between the
models is due to the difference in the estimated PK parameters.
Nevertheless, the t\textsubscript{1/2} of the novel model (70, 76 and 88
h for \textless{}12 years, [?]12 and \textless{}18 years and adults,
respectively) are closer to the reported t\textsubscript{1/2} in the
Alprolix\selectlanguage{ngerman}® SmPC\textsuperscript{21} (70, 82 and 82 h) than those
calculated for the published model (88, 99 and 101 h).

In this study, we have illustrated the clinical impact of underlying
population PK models on dosing advice when personalizing treatment. In
general, a population PK model should be applied that is representative
for the patients for which individual PK are characterized. In our
study, however, we did not observe a difference in dose for patients
\textless{}12 years of age which could be due to the limited number of
patients. When considering data from all patients, a significant dose
difference was observed, probably caused by the difference in population
PK parameters.

In this context, it is important to realize that individual PK
parameters are calculated by combining information from both the
population and the individual. When more samples are available (5 or
more) per individual, individual PK parameters are mainly determined by
information from this individual. In the present study, an intermediate
clinically representative (3) number of samples was available, hence
individual PK parameters were mostly determined by the individual
observations. It should however be realized that large differences in
dose predictions may occur when less samples are available for an
individual patient.

The strength of the present study is that it contains real world data
reflecting clinical variability. A study limitation is the relatively
sparse sampling method with aforementioned consequences at early time
points. The impact of FIX extravascular distribution is recognized by a
growing body of literature and should be incorporated in future
models\textsuperscript{38--40}. Investigation of extravascular binding
of FIX could be of clinical importance, as studies in mice suggest a
haemostatic function of extravascular FIX\textsuperscript{41,42}. We,
carefully, advocate the use of other techniques, like physiology-based
pharmacokinetic (PBPK) models, to investigate an estimation of this
extravascular compartment.

\textbf{CONCLUSION}

Population PK parameters derived from our novel model differ
considerably from those reported previously. The novel model better
describes the real world PK as opposed to the published model,
underlining the necessity to strive for representative population PK
models and avoiding extrapolation when performing PK-guided dosing.

\textbf{ACKNOWLEDGEMENTS}

\textbf{SYMPHONY consortium}

The SYMPHONY consortium which aims to orchestrate personalised treatment
in patients with bleeding disorders, is a unique collaboration between
patients, health care professionals and translational \& fundamental
researchers specialised in inherited bleeding disorders, as well as
experts from multiple disciplines. It aims to identify best treatment
choice for each individual based on bleeding phenotype. In order to
achieve this goal, workpackages have been organized according to three
themes e.g. Diagnostics (workpackages 3\&4); Treatment (workpackages
5-9) and Fundamental Research (workpackages 10-12). This research
receives funding from the Netherlands Organisation for Scientific
Research (NWO) in the framework of the NWA-ORC Call grant agreement
NWA.1160.18.038. Principal investigator: Dr. M.H. Cnossen. Project
coordinator: Dr. S.H. Reitsma. \selectlanguage{english}

Beneficiaries of the SYMPHONY consortium: Erasmus University Medical
Center-Sophia Children's Hospital, project leadership and coordination;
Sanquin Diagnostics; Sanquin Research; Amsterdam University Medical
Centers; University Medical Center Groningen; University Medical Center
Utrecht; Leiden University Medical Center; Radboud University Medical
Center; Netherlands Society of Hemophilia Patients (NVHP); Netherlands
Society for Thrombosis and Hemostasis (NVTH); Bayer B.V., CSL Behring
B.V., Swedish Orphan Biovitrum (Belgium) BVBA/SPRL.\selectlanguage{english} Additional
beneficiaries, not included in the SYMPHONY consortium, currently
funding parallel projects are: Novonordisk (OPTI-CLOT TARGET), Roche
(Partitura), Stichting Haemophilia (patient-reported outcomes project).

\textbf{OPTI-CLOT study group}

OPTI-CLOT/To WiN study group aims to implement personalized treatment
by\emph{pharmacometric-guided} dosing of factor concentrates,
desmopressin and non-factor therapies in patients with bleeding
disorders.

\emph{OPTI-CLOT/ To WiN Steering Committee, the Netherlands:} M.H.
Cnossen (principal Investigator \& chair OPTI-CLOT-To WiN) and R.A.A.
Mathot (co-investigator). F.W.G. Leebeek, Rotterdam; , M. Coppens K.
Fijnvandraat, Amsterdam; K. Meijer, Groningen, S.E.M. Schols, Nijmegen;
H.C.J. Eikenboom, Leiden; R.E.G. Schutgens, Utrecht; F. Heubel-Moenen,
Maastricht; L. Nieuwenhuizen, Veldhoven; P. Ypma, The Hague; M.H.E.
Driessens, Nijkerk.

Trial bureau: I. van Vliet, Rotterdam.

\emph{Local collaborators the Netherlands} : M.J.H.A. Kruip, S.
Polinder, Rotterdam; P. Brons, Nijmegen; F.J.M. van der Meer, Leiden; K.
Fischer, K. van Galen, Utrecht

\emph{Principal investigators and local collaborators in the United
Kingdom:} P. W. Collins, Cardiff; M. Mathias, P. Chowdary, London; D.
Keeling, Oxford.

\emph{OPTI-CLOT-To WiN, DAVID and SYMPHONY PhDs:}

PhDs: J. Lock, H.C.A.M. Hazendonk, T. Preijers, N.C.B. de Jager, L.
Schutte, L.H. Bukkems.

PhDs ongoing: M.C.H.J. Goedhart, J.M. Heijdra, L. Romano, W. Al Arashi,
M.E. Cloesmeijer, A. Janssen, S.F. Koopman, C. Mussert.

\textbf{CONFLICTS OF INTEREST}

Dr. M.H. Cnossen's institution has received investigator-initiated
research and travel grants as well as speaker fees over the years from
the Netherlands Organisation for Scientific Research (NWO) and
Netherlands National research Agenda (NWA), the Netherlands Organization
for Health Research and Development (ZonMw), the Dutch Innovatiefonds
Zorgverzekeraars, Stichting Haemophilia, Baxter/Baxalta/Shire/ Takeda,
Pfizer, Bayer Schering Pharma, CSL Behring, Sobi Biogen, Novo Nordisk,
Novartis, Roche, and Nordic Pharma, and for serving as a steering board
member for Roche, Bayer and Novartis. All grants and fees go to the
Erasmus MC as an institution. She is coordinator of Erasmus MC as a
Health Care Provider within the European Reference Network (ERN) for
rare hematological diseases EuroBloodNet and (co)leader of the local
Erasmus MC Expert Centers for Rare Bleeding Disorders and Sickle Cell
and Thalassemia Comprehensive Care Center.

R.A.A.M. has received grants from governmental and societal research
institutes such as NWO, ZonMW, Dutch Kidney Foundation and Innovation
Fund and unrestricted investigator research grants from Baxter/ Baxalta/
Shire/Takeda, Bayer, CSL Behring, Sobi and CelltrionHC. He has served as
advisor for Bayer, CSL Behring, Merck Sharp \& Dohme, Baxter/ Baxalta/
Shire/Takeda. All grants and fees paid to the institution.

F.W.G. Leebeek has received unrestricted grants from CSL Behring,
Takeda, Sobi and uniQure. He is a consultant for CSL Behring, Takeda,
Biomarin and uniQure, of which the fees go to the University. He was
DSMB member of a study sponsored by Roche.

M. Coppens has received financial support for research from Bayer, CSL
Behring, Roche and Novo Nordisk, and honoraria for lecturing or
consultancy from Alexion, Bayer, CSL Behring, Daiichi Sankyo, Sobi and
Viatris.

Other authors have no conflict of interest to declare for this paper.

\textbf{AUTORSHIP CONTRIBUTIONS}

T.M.H.J.G. enrolled patients, performed blood sampling for PK analysis
and validation and collected in the OPTI-CLOT TARGET study. Patient
inclusion in the UK EHL registry was monitored by M.M., P.W.C.,
C.R.M.H., R.C.T., C.B., S.M., B.M., G.E., B.B., N.C., J.P., and S.A.
T.M.H.J.G. collected data from the UK-EHL registry and checked all
clinical data from the EHL registry. S.F.K. and R.A.A.M. performed the
analysis and developed the population pharmacokinetic model. S.F.K. and
T.M.H.J.G. wrote the manuscript. L.H.B., M.H.C. and R.A.A.M. supervised
the study. All authors contributed substantially to the critical
revision of the manuscript and approved the final draft.

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\textbf{Hosted file}

\verb`Figures Tables Supplementary - Novel rFIX-Fc population PK model - Final Manuscript.docx`  available at \url{https://authorea.com/users/610768/articles/645579-a-novel-population-pharmacokinetic-model-for-recombinant-factor-ix-fc-fusion-concentrate-including-young-children-with-haemophilia-b}

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