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Population Pharmacokinetics Analysis of Infliximab in up to 10-Year-Old Patients with Paediatric Inflammatory Bowel Disease: Label-Recommended Dose Fails to Achieve Therapeutic Target Concentration
Younger patients with paediatric inflammatory bowel disease (IBD) may require higher infliximab (IFX) doses to attain similar target trough levels compared with older paediatric and adult patients with IBD. This study aimed to investigate the population pharmacokinetics (popPK) of infliximab (IFX) in young paediatric patients with inflammatory bowel disease (IBD) (≤ 10 years old), optimize the IFX dosage in this special population and explore whether different IFX assays were comparable.
Methods
IFX therapeutic drug monitoring (TDM) data from young paediatric patients with IBD (≤ 10 years old) were retrospectively collected from 14 European and Canadian centres (2015–2019). External validation of published popPK models was performed by calculating relative mean predictive errors (rMPE) and relative root mean square errors (rRMSE) to assess bias and imprecision. A refined popPK model was then developed using nonlinear mixed-effects modelling in NONMEM version 7.4. PopPK parameters in young paediatric patients with IBD (≤ 10 years old) were compared with those in published paediatric patients (> 10 to < 17 years) and adults.
Results
In total, 2150 IFX concentrations from 104 young paediatric patients with IBD (≤ 10 years old) were measured by six different IFX assays. The median (min–max) age and body weight at start of IFX treatment of the population was 8.2 years old (1.2–10.0) and 25 kg (9.5–40.9), respectively. For the external validation, six published popPK models were evaluated. The best-performing model showed values for rMPE and rRMSE at 33.73% and 1189.59%, which rendered all models inadequate for our study population. Subsequently, a refined two-compartmental popPK model was developed. Typical clearance (CL) values (min–max, L/day/65 kg) were 0.615–0.943 for paediatric patients ≤ 10 years, 0.015–0.353 for published paediatric patients (> 10 to < 17 years) and 0.317–0.350 for published adults. Both albumin and C-reactive protein (CRP) could explain the inter-individual variability obtained in CL. Stratifying for IFX assays in the residual error model showed significant differences in relative prediction errors (rPE) between Immundiagnostik and an assay developed by Shomron Ben-Horin (named “in-house” assay) (P < 0.05) and between Caltag and in-house assays (P < 0.05). Post hoc individual CL differed between the Immundiagnostik and Sanquin assays (P = 0.033), while estimated area under the curve for weeks 6–14 (AUCw6–14) remained similar (P > 0.05). With label-recommended dosing, IFX concentrations dropped below 5 mg/L for approximately 4 weeks during maintenance.
Conclusions
Paediatric patients ≤ 10 years, demonstrated a higher IFX CL than paediatric patients > 10 to < 17 years and adults, with albumin and CRP explaining the variability of CL. The differences obtained across the IFX assays did not affect overall drug exposure. To maintain trough concentrations above 5 mg/L in paediatric patients ≤ 10 years, a dosing interval of 4 weeks is required.
The original online version of this article was revised: The author’s name Victorien M. Wolters was incorrectly written without the mid-initial as Victorien Wolters.
This study addresses the first population pharmacokinetic (popPK) analysis of infliximab administered to paediatric patients with inflammatory bowel disease (IBD) under 10 years of age.
Paediatric patients with IBD ≤ 10 years exhibit higher infliximab clearance (CL) than older children > 10 to < 17 years old and adults.
Assessment of the measured infliximab (IFX) concentrations using different IFX assays simultaneously demonstrated that differences across assays had no significant effect on drug exposure.
A dosing interval of 4 weeks is needed to maintain IFX trough concentrations above 5 mg/L in paediatric patients with IBD ≤ 10 years.
1 Introduction
Since the introduction of infliximab (IFX), treatment of patients with inflammatory bowel disease (IBD) has changed revolutionarily [1]. Over the years, several studies have proven the effectiveness of IFX in paediatric IBD [2, 3]. As higher trough concentrations at the start of maintenance treatment (14 weeks) resulted in less immunogenicity [4] and higher response rates [5, 6], the clinical efficacy of IFX is strongly correlated with adequate IFX serum trough concentrations. However, multiple studies have suggested that paediatric patients with IBD have lower response rates on weight-based dosing than adults, partially owing to the differences found in the pharmacokinetics (PK) between children and adults [7]. Therefore, intensified IFX dosing regimens are required in paediatric patients to decrease the likelihood of poor response [8‐10].
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Consequentially, dosing strategies for young paediatric patients with IBD (up to 10 years old) may be more intense for older paediatric patients with IBD (> 10 to < 17 years old). One study demonstrated that paediatric patients who were 7 years or younger with IBD had reduced response rates to IFX and showed a lower likelihood of maintaining IFX therapy compared with older paediatric patients with IBD (> 10 to < 17 years old) [11]. Our previously published study [12] indicated that approximately 72% of patients with paediatric inflammatory bowel disease (PIBD) below 10 years old experience low trough concentrations, defined by a target concentration of 5.4 μg/mL at start of maintenance (week 14). Similarly, several studies have reported that patients with lower body weight, particularly those under 30 kg, exhibited increased IFX clearance (CL) when evaluated on a per kg basis (L/d/kg). Therefore, younger paediatric patients may require higher doses to attain similar target trough levels [13]. Since 2007, IFX has been approved for children between 6 and 17 years with a recommended dosing scheme of 5 mg/kg at week 0, 2 and 6 (induction) followed by maintenance infusions every 8 weeks. Nevertheless, IFX use in children under 6 years remains off-label, and evidence regarding the optimal dosing strategy in this age group is still lacking. Apart from body weight, the continuously changing body composition in childhood also affects the PK of IFX [12]. It is well-established that albumin, C-reactive protein (CRP) and anti-drug antibodies (ATI) can significantly influence IFX CL. Although population PK (popPK) analyses allow us to describe such physiological changes, no popPK analyses in paediatric patients with IBD ≤ 10 years have yet been published in literature.
For measuring IFX concentrations in serum, multiple enzyme-linked immunosorbent assay (ELISA) assays have been established and are applied for therapeutic drug monitoring (TDM). While a general target trough level of > 5 µg/mL is widely accepted across assays, inter-assay variability has been reported in research settings, suggesting that therapeutic interpretation may be influenced by the specific assay used [14]. Similarly, another study highlighted significant discrepancies between assays in classifying IFX concentration ranges, emphasizing caution when comparing results and recommending the use of the same assay for longitudinal monitoring of patients with IBD [15]. Moreover, pooled data may lead to varying PK parameter estimates.
As the currently published popPK analyses for IFX only included data obtained from a single ELISA assay and paediatric patients above 10 years old, this study aimed to investigate the popPK of IFX in paediatric patients with IBD ≤ 10 years and to assess the effect of different IFX assays on the accuracy of PK parameter estimation.
2 Methods
2.1 Clinical Data and Patient Population
Data from paediatric patients with IBD were retrospectively collected in 14 European and Canadian centres. Paediatric patients treated with IFX between 2004 and 2016 were included, and data were collected between 2015 and 2019. All children included in the study were less than 10 years of age at the initiation of IFX therapy. IFX concentrations were measured with different commercial ELISA kits. Supplementary Table 1 presents a description of the associated research centres and the applied IFX assays.
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2.2 Evaluating the Predictive Performance of Published popPK Models
Multiple popPK models describing IFX concentrations in paediatric patients with IBD, primarily focusing on patients > 10 years, have been published [16, 17]. Therefore, a systematic search in PubMed was performed using the following keywords: ‘infliximab’[tiab] AND (‘population PK’[tiab] OR ‘population pharmacokinetic*’[tiab]) AND (‘children’[tiab] OR ‘pedia*’[tiab] OR ‘paedi*’[tiab]) AND (‘inflammatory bowel disease’[tiab] OR ‘Crohn*’[tiab] OR ‘Colitis’[tiab]). This literature search was performed on 23 May 2024 and updated on 30 April 2025. On the basis of the keywords, 14 publications were identified. The inclusion criteria required that the publication comprised a popPK model able to predict IFX concentrations in the paediatric population.
To assess the predictive performance of these published models, an external validation was performed for each model. Goodness-of-fit (GOF) plots and visual predictive checks (VPCs) were obtained. To determine the bias and precision for the published models, the relative mean prediction error (rMPE) and relative root mean square error (rRMSE) were calculated using the following equations, respectively:
in which Cobs,i is the individual observed concentration, and Cpred,i is the individual predicted concentration.
2.3 PopPK Model Construction
2.3.1 Model Construction
Model construction was conducted in NONMEM version 7.4 (ICON Development Solutions, Ellicott City, MD, USA) using Perl-speaks-NONMEM (PsN) version 4.4.0 (Uppsala University, Sweden). Model development was performed using Pirana version 2.9.9 (Certara L.P., USA). Processing and visualization of data was performed in R 4.2.1 (R Foundation for Statistical Computing, Vienna, Austria).
Modelling was initiated using the most parsimonious model with parameter initials taken from the best-performing published model. Each addition to a model was assessed using the difference in objective function value (dOFV), the precision of the parameter estimates and diagnostic plots. Statistical comparison of nested models was based on the χ2 test based on the dOFV, for which a decrease of 3.84 units was considered significant (P < 0.05) for 1 degree of freedom. For establishing the residual error model, an additive, proportional and a combined error model was evaluated for each of the six different FIX assays. To take IFX measurements below the limit of quantification (BLQ) into account, the M3 method was implemented [18].
2.3.2 Covariate Analysis
Potential continuous covariates affecting clearance were age, body weight, height, erythrocyte sedimentation rate (ESR), albumin (ALB), C-reactive protein (CRP) and body surface area (BSA). Categorical covariate relationships included gender, Physician’s Global Assessment (PGA), diagnosis type (Crohn’s disease or ulcerative colitis), immunomodulatory use (with or without) and IFX assays. For the continuous covariates, values were normalized to the population median. A power relationship was applied to evaluate the continuous covariates:
in which Pi is the individual parameter estimate, θpop is the population PK parameter estimate, COVi is the individual covariate value, COVmedian is the median covariate value, θcov is the power estimate of the covariate relationship and ηi is the between-subject random effect for the parameter. If the parameter is clearance, the exponential for body weight/65 kg is 0.75, and if the parameter is volume of distribution, the exponential for body weight/65 kg is 1.
For all categorical covariates, the following equation was applied:
in which Pi is the individual PK parameter estimate, θpop is the population PK parameter estimate, ηi is the between-subject random effect for the parameter, θcov is the proportional change in θpop for the corresponding covariate, which was set to 1 for the reference group (θcov = 1) and 0 otherwise. If the parameter is clearance, the exponential for body weight/65 kg is 0.75, and if the parameter is volume of distribution, the exponential for body weight/65 kg is 1. For assessing all covariate relationships, a univariate analysis was conducted. Subsequently, a multivariate analysis using a stepwise forward inclusion and backward elimination method was applied to establish the final popPK model. During forward inclusion and backward elimination, P < 0.05 and P < 0.01 based on the dOFV were considered statistically significant. Besides the dOFV, the change in between-subject variability (BSV), residual unexplained variability (RUV), the relationship of covariates, GOF plots, VPCs and estimate precision were taken into account during the covariate selection.
2.3.3 Internal Model Validation
To assess the robustness of the parameter estimates from the final model, a bootstrap analysis was applied. For each parameter from the final model, the corresponding uncertainty was obtained using 1000 datasets generated by random sampling with replacement from the original dataset using PsN [19].
2.3.4 Evaluating the Impact of Multiple IFX Assays
For evaluating the impact of applying different IFX assays on IFX concentration prediction accuracy, the rMPE was obtained for each assay. Subsequently, post hoc individual CL was compared across different ELISA assays to examine whether assay-related differences in IFX concentration influenced individual CL estimates.
2.3.5 Evaluating IFX Assay Impact and Alternative Dosing Strategies
Monte Carlo simulations were performed with the developed popPK model to explore the impact of different IFX assays on drug exposure and multiple dosing strategies to optimize dosing regimen. For each simulation scenario, IFX concentration versus time curves from 1000 virtual patients were obtained.
For exploring the impact of multiple IFX assays, estimated area under the curve for weeks 6–14 (AUCw6–14) was compared across different ELISA assays. When evaluating alternative dosing strategies, IFX concentrations were simulated using label-recommended regimen (5, 7.5 and 10 mg/kg with induction at 0, 2 and 6 weeks, followed by maintenance every 8 weeks) to assess their adequacy. Next, IFX concentration–time curves with two typical paediatric patients with IBD ≤ 10 years were simulated: a 5-year-old (18 kg) and an 8-year-old (35 kg). Both received the same induction doses as per the label, while maintenance dosing was evaluated under three scenarios: (1) 5 mg/kg every 8, 6 and 4 weeks; (2) 7.5 mg/kg every 8, 6 and 4 weeks; and (3) 10 mg/kg every 8, 6 and 4 weeks. The target IFX concentration was set at 5 mg/L. All of the simulation scenarios were based on clinical practice to ensure their applicability, and we considered the concentration > 5 mg/L as adequate according to the ECCO/ESPGHAN guideline concerning therapeutic drug monitoring in paediatric inflammatory bowel disease, with Van Rheenen et al. recommending that a minimal maintenance threshold of 5 mg/L for IFX should be target for endoscopic healing [20].
3 Results
3.1 Study Population and Samples
In total, 2150 IFX concentrations from 104 paediatric patients with IBD ≤ 10 years were obtained. Most IFX concentrations were trough levels, with only four peak samples collected, primarily between weeks 2 and 12 post-administration. The median (min–max) age and body weight of the population was 8.2 years of age (1.2–10.0) and 25 kg (9.5–40.9) at start of IFX treatment (Table 1), respectively.
Table 1
Demographic data for pediatric patients with IBD (≤ 10 years old) who received infliximab
Total cohort
Patients
< 6 years old
Patients
≥ 6 and < 10 years old
Number of patients in PK analysis
104
16
88
Female, n (%)
54 (52%)
4 (25%)
46 (52%)
Female, n (%)
50 (48%)
12 (75%)
42 (48%)
Age at first infusion, years
Mean, SD
7.6 ± 2.0
3.8 ± 1.4
8.3 ± 1.0
Median, range, IQR
8.2 (1.2–10.0)
4.0 (1.2–5.9)
8.4 (6.1–10.0)
Bodyweight at the first infusion, kg
Mean, SD
25.3 ± 7.1
15.8 ± 3.6
27.0 ± 6.2
Median, range
25 (9.5–40.9)
16.0 (9.5–20.2)
25.6 (18.0–40.9)
Height at the first infusion, cm
Mean, SD
123.5 ± 14.0
99.3 ± 11.4
127.9 ± 9.0
Median, range
124.5 (74–150)
102.5 (74–113)
129 (112–150)
Body surface area, m2
Mean, SD
1.0 ± 0.2
0.7 ± 0.1
1.1 ± 0.2
Median, range
1.0 (0.4–1.9)
0.7 (0.4–0.9)
1.0 (0.7–1.9)
Diagnosis
UC, n
45 (43%)
11 (69%)
34 (39%)
CD, n
59 (57%)
5 (31%)
54 (61%)
IFX assay
Sanquin
14 (13.5%)
3 (19%)
11 (13%)
Immundiagnostik
31 (30%)
7 (44%)
24 (27%)
Caltag
34 (32.7%)
4 (25%)
30 (34%)
Matrix biotek
6 (5.8%)
1 (1%)
5 (6%)
Shomron Ben-Horin
12 (11.5%)
1 (1%)
11 (14%)
Promonitor
7 (6.7%)
0
7 (8%)
Immunomodulatory therapy
Yes, n
46 (59.0%)
9 (56%)
37 (42%)
No, n
58 (41.0%)
7 (44%)
51 (58)
Erythrocyte sedimentation rate mm/h
Mean, SD
19.6 ± 13.6
16.2 ± 6.6
20.1 ± 14.3
Median, range
18.8 (0.1–124)
18.0 (2.0–35.0)
19 (0.1–124)
Albumin, g/L
Mean, SD
39.6 ± 4.8
40.6 ± 4.1
39.5 ± 4.9
Median, range
40.5 (19.6–50)
42.3 (25–45.8)
40.3 (19.6–50)
Physician’s Global Assessment
Remission
2 (2%)
10 (63%)
2 (2%)
Mild
12 (11.5%)
1 (6%)
11 (13%)
Moderate
54 (52.0%)
0
44 (50%)
Severe
36 (34.6%)
5 (31%)
31 (35%)
C-reactive protein, mg/L
Mean, SD
10.4 ± 22.3
8.2 ± 20.0
10.8 ± 22.6
Median, range
3 (0.02–302)
1.2 (0.02–118.1)
3.3 (0.03–302)
IFX dose, mg/kg
Mean, SD
7.0 ± 2.2
7.3 ± 2.5
7.0 ± 2.1
Median, range
6 (3.5–15)
6.8 (4–15)
6 (3.5–15)
IBD inflammatory bowel disease, PK pharmacokinetics, SD standard deviation, IQR interquartile range, IFX infliximab, UC ulcerative colitis, CD Crohn’s disease
3.2 Performance of Published popPK Models
From the PubMed search, six published popPK models developed for paediatric patients with IBD (> 10 to < 17 years old) were obtained [13, 21‐25]. For the six selected models, the values for rMPE and rRMSE are described in Supplementary Table 2. Moreover, the GOF plots demonstrated large deviations from the line of identity (Supplementary Fig. 1), which was also demonstrated by all VPCs (Supplementary Fig. 2). The poor agreement between observed and predicted IFX concentrations indicated that the predictive performance from all published models was inadequate. Therefore, it was decided to construct a refined popPK model.
3.3 Model Development for Paediatric Patients with IBD ≤ 10 Years
The dataset used for model development consisted of 104 paediatric patients, contributing 640 infliximab concentration measurements, of which 95 measurements (14.4%) were BLQ. A two-compartmental popPK model best described the IFX disposition in this population. The allometric exponent was fixed at 0.75 for all CL terms and 1 for all volume of distribution terms a priori. Owing to the low number of peak concentrations, the volume of distribution of peripheral compartment (V2) and inter-compartment clearance (Q) could not be estimated and, therefore, fixed to values from the published models [21, 25]. Estimating the exponents for allometric scaling using body weight did not improve the model fit. The final parameter estimates (%RSE) for CL, V1, V2 and Q were 0.779 (7%), 17.2(16%), 1.21 (FIX) and 0.0697 (FIX). Between-subject variability was included on IFX CL (12%). A proportional error model provided the best fit of the model on the IFX measurements.
Albumin and CRP were selected as covariate relationships associated with CL. CL decreased by approximately 17% for every 10 g/L increase in albumin from the reference value of 40.5 g/L. Conversely, CL increased by approximately 1.6% for every 1 mg/L increase in CRP from the reference value of 3 mg/L. With all the covariate relationships associated with the model, between-subject variability for CL decreased with 5.2% (from 46.3% to 41.1%).
Parameter estimates for the final model are presented in Table 2. The GOF plots of the final model demonstrated an adequate fit, as the population and individual predictions were mostly symmetrically distributed around the line of identity (y = x) when compared with the measured IFX concentrations (Supplementary Fig. 3). The VPC plots revealed strong concordance between observed blood concentrations over time following dose administration and the model-based simulations (Supplementary Fig. 4). The interval validation demonstrated the robustness of the model parameter estimates, as the median bootstrap estimates were close to the parameter estimates from the final model, and the 95% confidence intervals demonstrated reasonably good precision for all estimated parameters (Table 2).
Table 2
Parameter estimates for the base model, final model and bootstrap analysis of IFX in pediatric patients with IBD ≤ 10 years old
Parameters
Base model estimates
Final model estimates
Bootstrap of the final model
Estimate
RSE (%)
Shr. (%)
Estimate
RSE (%)
Shr. (%)
Median
95% CI
Population PK parameter
CL (L/day/65 kg)
0.804
11
0.779
7
0.775
0.615–0.943
V1 (L/65 kg)
17.6
17
17.2
16
17.1
10.831–23.629
V2 (L/65 kg)
1.21 (FIX)
1.21 (FIX)
1.21 (FIX)
Q (L/day/65 kg)
0.0697 (FIX)
0.0697 (FIX)
0.0697 (FIX)
Inter-individual variability
CL (%CV)
46.3%
10
14
41.1%
12
15
39.8%
26.88%–52.35%
Covariate relationships
ALB on CL
−0.758
32
−0.754
−1.270 to −0.246
CRP on CL
0.0545
36
0.0541
0.016–0.093
Residual unexplained variability
Prop. RUV for Sanquin
0.927
33
42
0.951
27
33
0.941
0.387–1.516
Prop. RUV for Immundiagnostik
0.805
11
16
0.781
9
14
0.777
0.603–0.959
Prop. RUV for Caltag
0.665
8
13
0.617
9
10
0.617
0.509–0.724
Prop. RUV for Matrix biotek
0.525
18
6
0.591
20
6
0.584
0.320–0.862
Prop. RUV for Shomron Ben-Horin
0.902
25
27
0.889
25
25
0.848
0.088–1.690
Prop. RUV for Promonitor
1.11
35
12
0.914
31
11
0.861
−0.021–1.848
IFX infliximab, IBD inflammatory bowel disease, RSE relative standard error, Shr. shrinkage, CI confidence interval, CL clearance, V1 volume distribution for central compartment, V2 volume distribution for peripheral compartment, Q inter-compartmental clearance, ALB albumin, CRP C-reactive protein, RUV residual unexplained variability, CV coefficient of variation, Prop. proportional error
3.4 Comparison of PK Parameters Among Age Groups
Typical values (min–max) of CL for paediatric patients with IBD ≤ 10 years, paediatric patients with IBD > 10 and < 17 years, and adult patients were 0.615–0.943 L/day/65 kg, 0.0151–0.353 L/day/65 kg and 0.317–0.350 L/day/65 kg (Tables 3 and 4), respectively. Typical value (min–max) of the volume of distribution of central compartment (V1) for paediatric patients with IBD ≤ 10 years, paediatric patients with IBD > 10 and < 17 years and adult patients were 10.8–23.6 L/65 kg, 2.97–4.44 L/65 kg and 1.24–1.27 L/65 kg (Tables 3 and 4), respectively.
Table 3
Infliximab PK parameter comparison between pediatric patients with IBD (≤ 10 years old) and pediatric patients with IBD (> 10 and < 17 years old)
PK pharmacokinetics, IBD inflammatory bowel disease, CL clearance, V1 volume of distribution for central compartment, V2 volume of distribution for peripheral compartment, Q inter-compartment clearance, ALB albumin, CRP C-reactive protein, ATI antibody to infliximab, IMM immunomodulatory, ESR erythrocyte sedimentation rate, CD64 Fc gamma receptor I, FC fecal
Table 4
Infliximab PK parameter comparison between pediatric patients with IBD (≤ 10 years old) and adult patients with IBD
PK pharmacokinetics, IBD inflammatory bowel disease, CL clearance, V1 volume of distribution for central compartment, V2 volume of distribution for peripheral compartment, Q inter-compartmental clearance, ALB albumin, CRP C-reactive protein, ADA anti-drug antibody, BW bodyweight, FC fecal, FFM fat-free mass, ATI antibody to infliximab, IMM immunomodulatory
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3.5 Evaluation of Individual CL Estimates Between Age Groups
The individual estimates for CL revealed that children aged between 6 and 10 years exhibited higher CL (0.41 L/day/65 kg) as compared with those under 6 years of age (0.28 L/day/65 kg). However, this observed difference was primarily attributable to differences in body weight between the two groups, as the individual estimates for CL were obtained using the patient’s body weight and is, therefore, strongly correlated with body size (Fig. 1).
Fig. 1
Comparison of individual clearance (CL) between different age groups within pediatric patients with inflammatory bowel disease (IBD) ≤ 10 years old
The comparison of the rPE for predicting the measured IFX concentrations across the assays revealed that the Shomron Ben-Horin (in-house) assay exhibited significant differences compared with the assay from Immundiagnostik (P = 0.02) and Caltag assays (P = 0.01), whereas no differences were observed between the assays from Immundiagnostik and Caltag (P > 0.05) (Table 5). Of note, the in-house assay was not widely or commercially available. Furthermore, no published studies have evaluated or established the comparability of the in-house assay with other commonly used assays.
Table 5
Comparison for IFX prediction error, individual CL and calculated AUCw6–14 by different IFX assays
Assay A
Assay B
P-value (pairwise Wilcoxon test)
PE
Individual CL
AUCw6–14
Immundiagnostik
Sanquin
0.26
0.03
0.80
Immundiagnostik
Promonitor
0.06
0.08
0.70
Immundiagnostik
Caltag
0.84
0.35
0.85
Immundiagnostik
Shomron Ben-Horin
< 0.05
0.05
0.87
Immundiagnostik
Matrix biotek
0.89
0.16
0.39
Sanquin
Promonitor
0.64
0.94
0.89
Sanquin
Caltag
0.22
0.13
0.66
Sanquin
Shomron Ben-Horin
0.86
0.98
0.93
Sanquin
Matrix biotek
0.53
0.97
0.55
Promonitor
Caltag
0.06
0.26
0.56
Promonitor
Shomron Ben-Horin
0.91
0.97
0.83
Promonitor
Matrix biotek
0.18
0.84
0.64
Caltag
Shomron Ben-Horin
< 0.05
0.23
0.72
Caltag
Matrix biotek
0.86
0.37
0.30
Shomron Ben-Horin
Matrix biotek
0.08
0.82
0.49
IFX infliximab, PE prediction error, CL clearance, AUCw6–14 area under the curve between week 6–14
For the comparison of the individual CL estimates, the in-house assay no longer showed differences as compared with other assays. Nevertheless, significant differences emerged between individual CL estimates obtained using the Sanquin and Immundiagnostik assay (P = 0.033). It should be noted that only a limited number of IFX measurements (n = 8) were obtained using the Sanquin assay in this study, suggesting that the observed difference may lack robustness and clinical significance (Table 5).
Interestingly, the simulations showed no significant difference in calculated AUCw6–14, which indicated that differences observed between the in-house and Sanquin assay did not influence drug exposure. This result suggests that despite minor assay-related discrepancies in concentration measurements, the PK profiles remain comparable, supporting the robustness of the established model in estimating drug exposure across different assays (Table 5).
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3.7 Dosing Recommendation
Monte Carlo simulations showed that median IFX trough concentrations exceeded 5 mg/L in the induction period (weeks 2–6) across all regimens. Nonetheless, with the standard 8-week maintenance interval, concentrations fell below 5 mg/L for 63% of the time interval at 5 mg/kg, 50% at 7.5 mg/kg and 38% at 10 mg/kg (Fig. 2).
Fig. 2
Simulation for the label-recommended dosing regimen
Simulations about dosing regimen demonstrated that both dose and maintenance dosing interval could impact the percentage of time the drug concentration falls below the target of 5 mg/L. At the standard 5 mg/kg dose, the percentage of subtherapeutic time increased with longer intervals, reaching 53% in 5-year-old patients with IBD and 47% in 8-year-old patients with IBD at an 8-week interval. Increasing the dose from 5 mg/kg to 7.5 mg/kg reduced the time below target, particularly with a 4-week interval (reduced from 27% to 9% in 5-year-olds, and from 27% to 0% in 8-year-olds). The 10 mg/kg dose consistently minimized subtherapeutic exposure compared with 5 mg/kg (reduce from 27% to 0% both for 5 year-old and 8-year-old patients). These findings suggest that while the standard 8-week maintenance interval may not sustain adequate drug exposure, dose escalation or shorter intervals could help maintain target concentrations, particularly in younger patients (Figs. 3, 4 and Table 6).
Fig. 3
Simulation for the 5-year-old pediatric patients with IBD receiving different dosing strategies. a The dosage is 5 mg/kg; b the dosage is 7.5 mg/kg; c the dosage is 10 mg/kg
Simulation for the 8-year-old pediatric patients with IBD receiving different dosing strategies. a The dosage is 5 mg/kg; b the dosage is 7.5 mg/kg; c the dosage is 10 mg/kg
Percentage of time interval below target under different dosing regimen
Age/weight
Dosage (mg/kg)
Every 4 weeks (%)
Every 6 weeks (%)
Every 8 weeks (%)
5 years old (18 kg)
5
27
42
53
7.5
9
33
46
10
0
25
40
8 years old (35 kg)
5
27
33
47
7.5
0
25
47
10
0
25
40
4 Discussion
In this study, a popPK model was developed exclusively using data from paediatric patients with IBD ≤ 10 years old while incorporating IFX concentrations measured using multiple assays. Although six popPK models describing IFX in paediatric patients with IBD have been developed [13, 21‐25], an external validation demonstrated that these models inadequately estimated the IFX concentrations from paediatric patients with IBD < 10 years of age. This unsatisfactory predictive ability may be attributed to the external models being more tailored to older paediatric populations and lacking stratification of residual unexplained variability by different IFX assays. Therefore, a refined popPK model was developed.
In this study, we found that paediatric patients with IBD under 10 years of age have an augmented CL compared with older patients. This is aligned with published findings showing that lower body weight was associated with higher CL. One argument is the difference in the distribution of monoclonal antibody (mAbs) between paediatric patients with IBD ≤ 10 years old and paediatric patients > 10 years old and < 17 years old. In a study by Tian et al., it was demonstrated that neonatal Fc receptor (FcRn) expression, which contributes to IFX recycling and thereby extending its elimination half-life and decreasing clearance, increases with age until the end of puberty [26]. Therefore, immature FcRn activity in young patients could affect the IFX CL, potentially leading to higher IFX CL values and resulting in lower IFX trough concentrations. In addition, paediatric patients with IBD (≤ 10 years old) in our study had higher baseline CRP (10.4 ± 22.3 mg/L) and ESR (19.6 ± 13.6 mm/h) compared with previously reported values in paediatric patients over 10 years old (CRP: 2.9–4.6 mg/L; ESR: 9.0 [6.75–12.25] mm/h), suggesting a higher inflammatory burden and possibly elevated TNF-α levels [3, 23]. This heightened inflammatory state may contribute to increased IFX CL, consistent with previous studies linking higher IFX CL to protein-losing enteropathy, immunogenicity and increased TNF-α load [27, 28]. Notably, TNF-α is well-known to be closely linked to the target-mediated drug disposition (TMDD) of IFX. TMDD describes a process in which a significant portion of a drug binds with high affinity to its target, leading to nonlinear, concentration-dependent elimination. Consequently, the strong binding of IFX to TNF-α can trigger receptor-mediated internalization and accelerated degradation, especially when drug concentrations are low and the target is unsaturated [29]. Another publication mentioned that at lower IFX doses or concentrations, TMDD can substantially accelerate clearance, whereas at higher concentrations the pathway may become saturated, thus limiting its impact on overall drug elimination [30]. Although the developed model did not incorporate TMDD, the higher TNF-α load often seen in paediatric patients may enhance TMDD, thereby contributing to the increased clearance observed. The absence of a formal TMDD model does not preclude its underlying influence on IFX PK. Future studies with more comprehensive data on TNF-α levels and receptor dynamics could further elucidate the contribution of TMDD to IFX clearance in this population.
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In addition to increased CL, the higher V1 observed in paediatric patients with IBD (≤ 10 years old) may be explained by developmental differences in body composition. Younger children exhibit a higher proportion of total body water and extracellular fluid relative to body weight compared with older cohorts and adults, expanding the distribution space for hydrophilic macromolecules such as IFX [31]. The larger V1 observed in paediatric patients with IBD (≤ 10 years old) may reflect developmental differences in body composition and disease-driven fluid redistribution. Furthermore, immature vascular barriers and lower plasma protein binding in this age group may allow for greater drug permeation into the interstitial space [32]. These physiological characteristics may result in a larger apparent V1, ultimately influencing the overall PK profile of IFX in young patients with IBD. Nonetheless, it could not be ignored that the covariate distribution impacts scaling of CL and V1, ultimately impacting the estimation of the typical values (Eq. 3). This highlights the need to consider population-specific factors when interpreting model results.
When using different ELISA assays, obtaining similar PK parameter estimates is essential for ensuring that dosing decisions based on therapeutic drug monitoring (TDM) remain accurate and consistent. In recent years, model-informed precision dosing (MIPD) has been applied increasingly to perform personalized dosing of IFX owing to its higher accuracy as compared with TDM [33‐35]. Within the MIPD framework, accurate IFX assays are essential not only for quantifying drug concentrations but also for estimating PK parameters, particularly for calculating CL and predicting drug exposure. Different commercially available IFX assays for measuring IFX concentrations are designed using different detection antibodies, which can exhibit variable responses to interfering factors such as anti-drug antibodies (ADAs), potentially leading to less accurate results [36, 37]. This may explain why, despite reports of correlations among assays in multiple studies, systematic differences persist owing to a lack of standardization [8‐10]. One study provided the suggestion that we should be aware when interpreting IFX concentrations and recognize that therapeutic ranges are not interchangeable between different ELISA kits [14]. Moreover, in the external validation of the published models, the data from different IFX assays were lumped into one residual error model, as all the published models only mentioned one IFX assay for the model-building. Not surprisingly, the published models did not predict sufficiently. For the established model, stratification of the residual error model was performed, leading to significantly improved predictive performance. Another improvement was adding a method for including BLQ IFX measurements using the M3 method. Therefore, it can be emphasized that for pooled data generated by multiple IFX assays, the residual error model should be stratified and allowed to describe BLQ IFX measurements using the M3 method.
In this study, first the rMPE of observed and predicted concentration was compared among different assays resulting in significant differences between the in-house and Immundiagnostik/Caltag assays, potentially due to differences in assay design, detection sensitivity or drug tolerance. In contrast, no significant differences were found between the commercially developed Immundiagnostik and Caltag assays, likely reflecting greater methodological similarity and harmonization in their commercial manufacturing processes. Secondly, individual IFX CL estimates were compared across assays. As a result, the rMPE differences between the in-house and the Immundiagnostik/Caltag assays disappeared. This finding suggested that although the in-house assay may affect rMPE, its impact on CL estimation is minimal. Furthermore, the simulations showed no significant difference in AUCw6–14 across different assays, suggesting that the observed variation in rMPE and CL values caused by different ELISA assays did not substantially impact overall drug exposure.
According to a review of Bensalem et al. regarding the variability of PK parameters describing therapeutic antibodies [38], the mean estimated power coefficient of albumin on CL for all monoclonal antibodies (mAb) was − 0.99 (with a standard deviation of 0.44), which corresponded with our findings. Furthermore, multiple publications [39‐41] have explained that the inverse correlation between mAb and albumin concentrations could be attributed to the recycling of immunoglobulin G (IgG) by FcRn. Since both albumin and IgG are recycled by FcRn in a non-competitive manner, albumin levels are thought to reflect the abundance and efficiency of FcRn in individuals. Therefore, higher FcRn levels reflected by higher albumin levels may be associated with prolonged elimination half-life and reduced CL. CRP is a biomarker of intestinal mucosal inflammation, and higher CRP levels are typically associated with increased protein-losing enteropathy and extracellular catabolism, which can lead to enhanced drug CL [21‐23]. In our paediatric cohort, CRP levels were higher compared with those reported in published data for children older than 10 years; conversely, albumin concentrations were not significantly different. The positive relationship between CRP and IFX CL was also observed in this study, which aligns with previous popPK analyses [21, 28].
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In our simulation results, we demonstrated that a typical 5-year-old patient was more likely to experience therapeutic failure than a typical 8-year-old patient when given the same IFX dosing amount and dosing interval. Moreover, IFX trough concentrations and overall drug exposure were influenced more by interval reduction than by increasing the dose. To maintain trough concentrations above 5 mg/L in paediatric patients with IBD (≤ 10 years old), the dosing interval during the maintenance phase should be reduced to 4 weeks. Nevertheless, clinical symptoms and biomarker levels should be considered as well for establishing a dose recommendation.
A strength of this study is the inclusion of a diverse patient population across multiple centres and the integration of data from various IFX assays. Nevertheless, this study has limitations. The data were collected retrospectively, and at the time, proactive TDM was not yet part of routine clinical practice. As a result, the timing of sample collection was not standardized, unlike in prospective studies. This may have led to insufficient measurements of peak concentrations of IFX. Consequently, the values for the population PK parameters from the second elimination compartment had to be fixed on the basis of previously published values. In addition, ATI data were sparse. This limited the assessment for the potential impact of ATI on the PK parameters. Furthermore, the number of IFX concentrations among different IFX assays was not balanced, with the Sanquin assay being particularly underrepresented. The latter was likely to introduce bias for the rMPE and individual CL estimate comparisons across different assays.
5 Conclusions
In this study, a popPK model was developed exclusively using data from paediatric patients with IBD ≤ 10 years of age while incorporating IFX concentrations measured using multiple ELISA assays. The developed popPK model demonstrated that paediatric patients with IBD (≤ 10 years old) have a higher IFX CL than paediatric patients with IBD (> 10 and < 17 years old). Albumin and CRP were significantly associated with IFX CL. By stratifying the residual error model for the different IFX assays, the observed PE and CL differences across assays were unlikely to significantly alter overall drug exposure in clinical practice. For young paediatric patients (≤ 10 years old) to maintain IFX trough concentrations above 5 mg/L, the dosing interval during the maintenance phase should be reduced to 4 weeks. TDM is recommended at the end of induction and at regular intervals during maintenance to optimize dosing and ensure adequate drug exposure.
Declarations
Funding
Funding was received from Porto IBD group of ESPGHAN.
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Conflict of Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data Availability Statement
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
Ethics Approval
Ethics committee approval was either waived or obtained in all participating centres.
Consent to Participate
The study was not subject to the Dutch Medical Research Involving Human Subjects Act (WMO), and therefore written informed consent was not required for all subjects.
Consent for Publication
Not applicable.
Code Availability
The code used during the current study is available from the corresponding author on reasonable request.
Author Contributions:
Q. Zhao conducted model analysis and manuscript preparation. M.M.E Jongsma and S.A. Vuijk collected the data. T. Preijers and B.C.M. de Winter supervised Q. Zhao on the population PK analysis and model construction. L. de Ridder designed this study. C. Martinez-Vinson, K.L. Kolho, Lorenzo Norsa, S. Hussey, Eytan Wine, Shlomi Cohen, Dror S. Shouval, Amit Assa, R. Lev-Tzion, Tim de Meij, Victorien M. Wolters and Hien Q. Huynh participated in the clinical trial conduction. All authors reviewed and approved the manuscript before submission.
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.
Population Pharmacokinetics Analysis of Infliximab in up to 10-Year-Old Patients with Paediatric Inflammatory Bowel Disease: Label-Recommended Dose Fails to Achieve Therapeutic Target Concentration
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Q. Zhao
M. M. E Jongsma
S. A. Vuijk
B. C. M. de Winter
C. Martinez-Vinson
K. L. Kolho
Lorenzo Norsa
S. Hussey
Eytan Wine
Shlomi Cohen
Dror S. Shouval
Amit Assa
R. Lev-Tzion
Tim de Meij
Victorien M. Wolters
Hien Q. Huynh
T. Preijers
L. de Ridder
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