Introduction

CD55-deficient protein-losing enteropathy (CHAPLE) disease is an ultra-rare, life-threatening genetic disorder caused by loss-of-function variants in the CD55 gene [1, 2]. CD55, also known as decay-accelerating factor, is a widely expressed glycosylphosphatidylinositol-linked membrane protein that regulates complement components 3 (C3) and 5 (C5) by destabilizing and inhibiting the formation of C3 and C5 convertases [3,4,5,6]. The absence of CD55 in patients with CHAPLE disease causes overactivation of the terminal complement system, specifically in gut lymphatic endothelial cells, leading to excess formation of the membrane attack complex (C5b-9) and resulting in cell membrane disruption [1, 7].

CHAPLE disease typically presents in infancy or early childhood [2], and although clinical and laboratory features of CHAPLE disease vary they include hypoproteinemia, low immunoglobulin concentrations, recurrent infections, hypoalbuminemia, peripheral and facial edema, hypogammaglobulinemia, and gastrointestinal symptoms (e.g., diarrhea, abdominal pain, nausea, vomiting, loss of appetite, and weight loss) [1]. Patients with CHAPLE disease frequently require hospitalization and medical interventions, and many progress to life-threatening conditions including hypoproteinemia (causing metabolic derangements, starvation, and infections); debilitating gastrointestinal inflammation, ulceration, and partial or complete obstruction; and thromboembolic events that frequently lead to premature mortality [1].

Pozelimab, a fully human monoclonal immunoglobulin G4 antibody directed against C5, has been shown to bind with high affinity to wild-type and variant (R885H/C) human C5 and block its activity [8]. In August 2023, the US Food and Drug Administration (FDA) approved pozelimab as the first and only treatment for adult and pediatric patients 1 year of age and older with CHAPLE disease [9]. This approval was based on a phase 2/3 clinical trial of pozelimab that comprised patients aged 3 to 19 years with CHAPLE disease (NCT04209634) [10]. Pozelimab was administered as a single intravenous (IV) loading dose of 30 mg/kg followed by weekly (QW) weight-tiered subcutaneous (SC) dosing. Following treatment, patients with CHAPLE disease showed complete inhibition of complement, and a rapid and sustained normalization of albumin levels, which was the primary endpoint of the clinical trial.

Paroxysmal nocturnal hemoglobinuria (PNH) is a chronic, progressive, life-threatening, and rare multisystem disease. It is characterized by uncontrolled complement activation on red blood cells (RBCs), resulting in intravascular hemolysis, and an increased risk of thrombosis [11]. PNH originates from a multipotent, hematopoietic stem cell (HSC) that acquires a mutation of the phosphatidylinositol glycan anchor biosynthesis class A (PIGA) gene, which results in lack of cell surface expression of CD55 and CD59 on blood cells. CD55 and CD59 are the terminal complement pathway negative regulators that prevent cell lysis. The absence of CD55 and CD59 renders PNH erythrocytes susceptible to complement-mediated intravascular hemolysis, which causes anemia (frequently requiring blood transfusion) and hemoglobinuria [12, 13]. Therapies targeting C5 for PNH, such as eculizumab and ravulizumab, have been demonstrated to be effective. However, in rare instances, eculizumab and ravulizumab are ineffective due to polymorphic variation in the gene encoding C5 such that the C5 protein is not bound by eculizumab or ravulizumab [14]. Pozelimab, as a monoclonal antibody targeting C5, is being investigated for the treatment of PNH. Additionally, pozelimab has different binding sites on C5 as compared to eculizumab and ravulizumab, thus is expected to be effective in patients with polymorphic variation who were not responsive to eculizumab or ravulizumab.

This current analysis aimed to develop a population pharmacokinetic (PopPK) model to characterize the pharmacokinetics (PK) of total pozelimab and total C5 in healthy adult volunteers, patients with paroxysmal nocturnal hemoglobinuria (PNH), and patients with CHAPLE disease, based on pooled data from phase 1–3 clinical studies; an additional aim was to simulate free C5 and free pozelimab concentrations to support the dose regimen in patients with CHAPLE disease.

Methods

Patients

A target-mediated drug disposition (TMDD) PopPK model was initially developed using data pooled from two phase 1 (NCT03115996; NCT04491838) and one phase 2 (NCT03946748) clinical trials of pozelimab that included healthy adult volunteers or patients with PNH. Subsequently, the TMDD PopPK model was updated to include data from a phase 2/3 clinical trial of pozelimab that included pediatric and adult patients with CHAPLE disease (NCT04209634). A summary of these studies is presented in Supplementary Table S1.

Bioanalytical assays

Total pozelimab concentrations (free pozelimab and pozelimab bound to one or two molecules of C5) in human serum were measured using a validated enzyme-linked immunosorbent assay. The lower limit of quantification (LLOQ) was 0.078 mg/L of pozelimab in neat human serum. Total C5 concentrations (free C5 and C5 bound to pozelimab) in human plasma were measured using a validated electrochemiluminescence immunoassay. The LLOQ was 7.8 mg/L of C5 in neat human plasma.

Modeling software

PopPK analysis was performed using a non-linear mixed-effects modeling approach with NONMEM® version 7.4.1 or higher (Icon Development Solutions, Ellicott City, Maryland, USA). The first-order conditional estimation method with the interaction option was used for all model runs. Perl-speaks-NONMEM version 4.8.1 or higher (Uppsala University, Uppsala, Sweden) was utilized as supportive software for NONMEM®. Pooled NONMEM-ready datasets were constructed using SAS version 9.4 or higher (SAS Institute Inc, Cary, North Carolina, USA) and R statistical software version 4.0.2 or higher (R Development Core Team, Vienna, Austria). R statistical software version 4.0.2 or higher was used for post-processing of NONMEM outputs, generation of statistical outputs, and preparation of figures. Simulations were performed using R package mrgsolve (0.10.4 or later, Metrum Research Group).

Data exclusion and below-limit-of-quantification records handling

Pozelimab concentration observations prior to the first dose of pozelimab were excluded from the PopPK analysis. Concentration records below the limit of quantification (BLQ) were flagged in the data set. If the proportion of post-dose BLQ observations accounted for < 5% of the overall number of sampling observations, and visual assessment of data distribution showed no systematic trends, Beal’s M1 method was applied and BLQ observations were excluded from the PopPK analysis [15]. Outliers were identified using the population conditional weighted residuals (CWRES) and individual weighted residuals (IWRES). Observations with |CWRES| >6 or |IWRES| ˃6 were considered potential outliers. A sensitivity analysis was conducted to evaluate the potential influence of these outliers by comparing estimates of the key PK parameters such as linear clearance (CL) and distribution volume of the central compartment (Vc or V2) from model fits of data with and without the outliers. Outliers were considered influential if a change of ≥ 20% was observed in ≥ 1 key PK parameter estimates.

Missing data and imputations

The baseline covariate value was defined as the value collected at the time closest to, but prior to, the first dose of study drug. If the covariate value was missing at the baseline visit, it was taken from another pre-treatment visit. If covariate values were missing at all visits, median (for continuous covariates) or mode (for categorical covariates) values across all participants were used for imputations. For time-varying covariates, partially missing data were imputed using the last observation carried forward. For the two phase 1 trials in healthy adult volunteers in which time-varying body weight was not collected, time-varying body weight was imputed based on body weight at baseline assuming no body weight change throughout the study duration.

Development of the TMDD PopPK model

An initial TMDD PopPK model was developed for pozelimab using total pozelimab and total C5 concentration–time data from 106 participants (healthy volunteers [n = 82]; patients with PNH [n = 24]) across three clinical trials. The PopPK model was developed using the stepwise approach shown in Supplementary Figure S1. The initial step was development of a base model describing total pozelimab and total C5 concentration–time profiles and included a structural model, a residual error model, and an inter-individual variability (IIV) model. A TMDD PopPK model with two binding sites based on the quasi-equilibrium (QE) approximation [16] was the initial structural model evaluated to describe total pozelimab and total C5 concentration–time profiles (Fig. 1 and Supplemental Text 1). Baseline body weight was evaluated at the base model stage to stabilize the model and facilitate easier identification of the full model in subsequent steps. Standard goodness-of-fit diagnostic plots were generated to enable assessment of the model fit, including concordance (i.e., population predicted value [PRED] versus observed value [DV]; individual predicted value [IPRED] versus DV), residual (i.e., CWRES versus PRED; CWRES versus time; IWRES versus PRED; IWRES versus time), and overlay plots (i.e., DV, PRED, and IPRED versus time). Moreover, diagnostic plots of the individual random effect values versus covariate values were generated to identify any covariate effects to be accounted for in covariate analysis.

Fig. 1
figure 1

Base TMDD PopPK model for total pozelimab and total C5. Total pozelimab and total C5 data was used for model building. Free pozelimab and free C5 concentrations can be simulated based on the TMDD PopPK model. Y-shaped and round-shaped forms represent free pozelimab and free C5, respectively. C5, complement component 5; F1, bioavailability; IV, intravenous; k23, k32, inter-compartmental rate constants; ka, absorption rate constant; kD, equilibrium dissociation constant; ke, linear elimination rate constant; ksyn, synthesis rate of free C5; kint1, internalization rate constant for pozelimab-C5 complexes; kint2, internalization rate constant for pozelimab-C5-C5 complexes; kdeg, degradation rate of free C5; PopPK, population pharmacokinetic; R0, baseline free C5, defined as the ratio of ksyn and kdeg; SC, subcutaneous; TMDD, target-mediated disposition model; V2, distribution volume of the central compartment; V3, distribution volume of the peripheral compartment

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IIV of the PK parameters was described using the following lognormal random effects model: \(\:{\theta}_{\text{i}}\text{=}{\theta}_{\text{TV}}\text{}.\:{e}^{\left({n}_{i}\right)}\), where \(\:{\theta\:}_{i}\) denotes the parameter estimate for the ith individual, \(\:{\theta\:}_{TV}\) denotes the parameter estimate for the population, and \(\:{\eta\:}_{i}\) denotes the inter-individual random effect, representing the deviation of the individual parameter estimate from the population parameter estimate. The \(\:\eta\:\) values were assumed to have a normal distribution with a zero mean and variance \(\:{\omega\:}^{2}\).

Residual variability, a composite measure of assay error, dose and sample time collection errors, model misspecification, and any other unexplained variability within a participant, was described using the log-transformed error model \(\:{ln}\left({\widehat{Y}}_{ij}\right)=ln\left({C}_{tot,ij}\right)\:+\:{\epsilon\:}_{ij}\) for total pozelimab, and the proportional error model \(\:{\widehat{Y}}_{ij}={R}_{tot,ij}\dot\:(1+{\epsilon\:}_{ij})\:\)for total C5, where \(\:{\widehat{Y}}_{ij}\) denotes the observed concentration for the ith individual at time tj, \(\:{C}_{tot,ij}\) and \(\:{R}_{tot,ij}\) denote the corresponding predicted total pozelimab and total C5 concentrations based on the PopPK model, and εij denotes the residual random variable, which was assumed to have a normal distribution with a zero mean and variance σ2. Separate residual variability terms were estimated for total pozelimab and total C5.

After base model development, pre-specified intrinsic and extrinsic covariates that were not included as structural covariates in the base model were evaluated to identify potential sources of pozelimab PK variability. Clinical judgment and mechanistic plausibility were used to determine which covariates should be tested and on which model parameters they should be evaluated. Pre-specified covariates evaluated as baseline parameters included age, sex, Asian race, baseline body weight, baseline albumin, baseline aspartate aminotransferase, baseline alanine aminotransferase, disease state (patients with PNH versus healthy volunteers), baseline renal function, and baseline total C5 concentration. A full covariate model was constructed using a stepwise forward selection procedure, which involved a series of processes of univariate covariate evaluation in which the effect of each covariate on the pre-specified model parameter were tested by adding one covariate at a time, with the most significant covariate to the model, i.e. the one that met the significance level of P < 0.05 ([change in objective function value [OFV] of > 3.84 units) and resulted in the most reduction in OFV being retained to form a new base model; the same process was repeated with the remaining candidate covariates until none of the remaining candidate covariates provided significant improvement to the model. The full covariate model was then subjected to a stepwise backward elimination process, where each covariate was removed from the full model separately. If removal of a covariate from the model contributed to a change in OFV of > 10.83 units, the covariate was deemed significant (P < 0.001) and retained in the model. The backward elimination procedure was repeated until all covariate-parameters met the inclusion criteria.

Refinement of the PopPK model

After development of the TMDD PopPK model as described thus far, an update was made to include PK data from 10 patients (nine children and one adult) with CHAPLE disease. Model refinements were undertaken to improve fitting of the data, including an evaluation of baseline body weight and IIV random effects on PK parameters in addition to CL and Vc, and separate residual variability terms for adults and children with CHAPLE disease. A sensitivity analysis was performed to explore the relationships of pozelimab PK parameters with body weight using model-estimated exponents in contrast to fixed allometric exponent values of 0.75 for CL and 1 for Vc or volume of the peripheral compartment (Vp or V3).

Predictive performance of the final PopPK model

The predictive performance of the final model was assessed by simulating data using the parameter estimates from the final model and conducting internal visual predictive checks (VPCs) [17]. For the VPCs, 500 datasets were generated using the final model that replicated the design, participant population, dose regimens, sample sizes, and covariate distributions from the combined observed dataset. The 90% confidence intervals of the 5th, 50th, and 95th percentiles of the simulated total pozelimab and total C5 concentrations were plotted as a function of time and overlaid with the corresponding percentiles of the observed data. The concordance between observed and simulated concentrations of total pozelimab and total C5 was evaluated to provide a visual assessment of model fit.

Simulations of pozelimab exposure in patients with CHAPLE disease

Stochastic simulations were performed using the final PopPK model to predict concentration–time profiles for total pozelimab, free pozelimab, and free C5, and obtained pozelimab exposure metrics for adults and children with CHAPLE disease in each of the weight groups receiving a single IV loading dose of 30 mg/kg on day 1, followed by the 10 mg/kg SC QW maintenance dose, with a maximum dose of 800 mg SC QW, or the following weight-tiered QW maintenance doses of SC pozelimab: 125 mg SC QW for body weight < 10 kg, 200 mg SC QW for ≥ 10–<20 kg, 350 mg SC QW for ≥ 20–<40 kg, 500 mg SC QW for ≥ 40–60 kg, and 800 mg SC QW for ≥ 60 kg. The simulations were conducted under the assumption that pozelimab PK was similar between healthy volunteers and patients with CHAPLE disease after accounting for the body weight effect. For each 5 kg weight bracket, 1000 virtual patients were generated. Time-varying body weight for each virtual patient was generated by fitting nonlinear mixed effect models to longitudinal body weight in observed patients with CHAPLE disease. The model with the lowest Akaike information criterion that described the body weight data and generated longitudinal simulation body weight beginning from the simulated baseline body weight was selected. It has been reported that by the age of 6 months mean concentrations of C5 in children are not significantly different from those in adults [18, 19]. Therefore, given that earliest onset of CHAPLE disease does not occur before 6 months of age, baseline C5 for the virtual pediatric patients with CHAPLE disease was assumed to be comparable to that observed in healthy adult volunteers. The simulation results were used to demonstrate comparable total pozelimab exposures between 10 mg/kg and weight-tiered QW SC maintenance dose in patients with CHAPLE disease.

Results

Analysis set

The final PopPK dataset comprised 116 participants (82 healthy volunteers, 24 patients with PNH, and 10 patients with CHAPLE disease) contributing a total of 2795 concentration samples (total pozelimab n = 1640; total C5 n = 1155) (Supplementary Table S2). Of the 2795 concentration samples, 2695 (96.4%) were quantifiable; 100 (3.6%) post-dose samples were BLQ and were thus excluded from the PopPK analysis given the low proportion (< 5%).

Demographic characteristics and baseline values for relevant categorical and continuous covariates in the PopPK model are presented in Table 1.

Table 1 Demographics and baseline characteristics of subjects included in the PopPK analysis
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For adult participants (excluding patients with CHAPLE disease), median age was 37.5 years (range: 19–76), median baseline body weight was 68 kg (range: 45–108), median baseline body mass index (BMI) was 24 kg/m2 (range: 17.6–34.1), and median baseline albumin level was 44 g/L (range: 37–51).

For patients with CHAPLE disease, the median age was 8.5 years (range: 3–19), median baseline body weight was 25.0 kg (range: 11.0–53.8), median baseline BMI was 15.5 kg/m2 (range: 12.2–24.5), and median baseline albumin level was 23.0 g/L (range:11.0–29.0 g/L).

Baseline C5 levels were comparable between the predominantly pediatric patients with CHAPLE disease, and adult patients with PNH or healthy adult volunteers (Supplementary Figure S2). Moreover, there was no trend observed between baseline C5 levels and baseline covariates of age, body weight, and albumin levels across the analysis population (Supplementary Figure S3).

Base PopPK model

The PopPK dataset used to develop the initial PopPK model was pooled from three clinical studies comprised of healthy adult volunteers or patients with PNH. Using this PopPK dataset, the PopPK model developed was a two-compartment TMDD model with two binding sites based on the QE approximation [16] and defined by the following parameters: CL, Vc, absorption rate constant (ka), intercompartmental clearance (Q), Vp, bioavailability, synthesis rate of free C5 (ksyn), degradation rate constant of free C5 (kdeg), equilibrium dissociation constant (kD), internalization rate constant for the pozelimab-C5 complexes (kint1), and an internalization rate constant for the pozelimab-C5-C5 complexes (kint2). The kD value was fixed to 0.189 nM (0.03591 mg/L), which was obtained from SPR-Biacore technology. IIV random effects were included on CL, Vc, ka and ksyn. Different error models were evaluated for total pozelimab and total C5. An additive error model with log-transformed data best described the total pozelimab data, while a proportional error model was used for the total C5 data. A correction factor was added to baseline free C5 as a linear function described by the equation below, to provide model flexibility and account for the initial drop in total C5 concentrations between baseline (time 0) and the sampling time at 1 h after drug administration. Inclusion of the correction factor resulted in a decrease in OFV of ~ 113 units, which was statistically significant. The impact of baseline body weight on CL and Vc was included in the base model.

$$\:{R}_{0}=\frac{{k}_{syn}}{{k}_{deg}}+{\theta\:}_{13}$$

R0 is the baseline free C5 and θ13 is the correction factor.

Final PopPK model

In addition to baseline body weight, which was included as a covariate in the base structural model, PNH patient status on Vc was the only statistically significant covariate identified following covariate analysis.

After the TMDD PopPK model was updated to include data from the phase 2/3 clinical study of pozelimab of patients with CHAPLE disease, model refinements were undertaken. These comprised of inclusion of the impact of baseline body weight on Vp, the inclusion of IIV on Vp and kint1, and the removal of IIV on ka, which led to improvements in model fitting, especially for patients with CHAPLE disease. The effect of baseline body weight on Q was evaluated, however, this resulted in unstable model performance and no visible improvement in model fitting. As a result, the TMDD model with the effect of IIV estimated on CL, Vc, Vp, ksyn, and kint1, with residual error separated by adults and children with CHAPLE disease for both pozelimab and total C5, baseline body weight effect on CL, Vc, and Vp, and PNH patient status on Vc, was declared the final TMDD PopPK model for pozelimab.

The PK parameter estimates for the final PopPK model are presented in Table 2. Structural PK parameters were well estimated in the final PopPK model, with percent relative standard error values ≤ 17% for structural parameters. The model was stable with a condition number of 138.3, well below the reference threshold value of 1000. The empirical Bayes estimate for IIV shrinkage was high for linear CL (53.3%) and kint1 (38.5%), and moderate for Vc (19.9%), ksyn (21.7%), and Vp (24.0%). Inspection of diagnostic plots demonstrated alignment between observed, individual predicted, and population predicted concentrations of total pozelimab and total C5 in all participants (Supplementary Figures S4 and S5). Similarly, inspection of diagnostic plots demonstrated alignment between observed data and model-predicted data for total pozelimab and total C5 in patients with CHAPLE disease (Supplementary Figures S6 and S7).

Table 2 PK parameter estimates using the final TMDD PopPK model
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The covariate effect of time-varying body weight was evaluated using the final TMDD PopPK model by replacing baseline body weight with time-varying body weight, due to rapid weight gain in patients with CHAPLE disease following pozelimab treatment. Time-varying body weight was not available in healthy volunteers, therefore this was imputed using baseline body weight for this group. A comparison of PK parameter estimates for the models using time-varying and baseline body weight is presented in Supplementary Table S3. Using time-varying body weight resulted in similar (< 5% difference) PK parameter estimates and OFV, except for the exponent relating body weight to CL, which increased from 0.9989 to 1.108 with time-varying body weight.

A sensitivity analysis of exponents relating time-varying body weight to CL, Vc, and Vp was conducted by fixing body weight exponents to classical allometric values (1 for Vc or Vp and 0.75 for CL). Fixing body weight exponents to allometric values resulted in an increase in OFV by 11.2 points (Supplementary Table S4) and diagnostic plots that indicated poorer fitting in patients with CHAPLE disease, compared to the model with estimated exponents (Supplementary Figures S8 and S9).

Predictive performance of the final TMDD PopPK model

Internal VPCs stratified by dose showed that most of the observed concentrations of pozelimab were within the range of the 5th to 95th predicted percentiles (Fig. 2). Similarly, the internal VPC for total C5 showed that the observed concentrations of total C5 were generally contained within the range of the 5th to 95th predicted percentiles (Fig. 3). The VPCs indicate that the final PopPK model allowed for reliable predictions for individual exposures.

Fig. 2
figure 2

Visual predictive check of the final TMDD PopPK model for total pozelimab stratified by dose. Red solid line: simulated 50th percentile; black solid line: observed 50th percentile; blue dashed lines: simulated 5th and 95th percentiles; symbols: observed data; shaded area: 90% simulation-based confidence interval around median (pink) and upper/lower percentiles (blue). PopPK, population pharmacokinetic; IV, intravenous; QW, once weekly; SC, subcutaneous; TMDD, target-mediated drug disposition

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Fig. 3
figure 3

Visual predictive check of the final TMDD PopPK model for total C5. Red solid line: simulated 50th percentile; black solid line: observed 50th percentile; blue dashed lines: simulated 5th and 95th percentiles; symbols: observed data; shaded area: 90% simulation-based confidence interval around median (pink) and upper/lower percentiles (blue). C5, complement component 5; PopPK, population pharmacokinetic; TMDD, target-mediated drug disposition

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Simulations of pozelimab exposure in patients with CHAPLE disease

Simulated concentration–time profiles for total pozelimab, free pozelimab (i.e., pozelimab not bound to any target), and free C5 following a single IV dose of 1, 3, 10 and 30 mg/kg of pozelimab are shown in Fig. 4, which demonstrated free pozelimab marked nonlinearity at lower concentrations. Following a single IV dose of 30 mg/kg pozelimab, free C5 was predicted to be suppressed for approximately 12 weeks before returning to baseline.

Fig. 4
figure 4

Simulated median concentration–time profiles of total pozelimab (green dashed lines), free pozelimab (blue dashed lines), and free C5 (solid red lines) following a single IV dose of 1, 3, 10, and 30 mg/kg. C5, complement component 5; IV, intravenous

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Simulated exposure metrics for total pozelimab based on the final TMDD PopPK model following 10 mg/kg weight-based dose or weight-tiered doses are shown in Fig. 5 and Supplementary Table S5. Similar simulation results based on the PopPK model with time-varying body weight effect are illustrated in Supplementary Figure S10. Simulation results based on either the final TMDD PopPK model or the model with time-varying body weight effect suggest that pozelimab PK exposure metrics at steady-state were generally comparable for patients with CHAPLE disease weighing > 20 kg following either a 10 mg/kg weight-based dose or weight-tiered doses. In addition, similar pozelimab PK exposures are expected across the different weight groups.

Fig. 5
figure 5

Simulated total pozelimab Cmin, ss and AUCtau, ss for patients with CHAPLE disease following a 10 mg/kg or weight-tiered SC QW maintenance dose. Weight-based dose: 10 mg/kg, not exceeding 800 mg SC QW; weight-tiered dose: 125 mg for < 10 kg, 200 mg for 10–<20 kg, 350 mg for 20–<40 kg, 500 mg for 40–<60 kg, and 800 mg for ≥ 60 kg. AUCtau, ss, steady-state area under the concentration time curve for a dosing interval; CHAPLE, complement hyperactivation, angiopathic, thrombosis, protein-losing enteropathy; Cmin, ss, trough concentration at steady-state; QW, weekly; SC, subcutaneous

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Simulations for free C5 following the 10 mg/kg weight-based dose or weight-tiered doses are shown in Supplementary Figure S11. Free C5 concentrations are predicted to be extremely low (< 0.05 mg/L), indicating complete inhibition of complement activity.

Discussion

A robust TMDD PopPK model for pozelimab was developed using total pozelimab and total C5 concentration–time data pooled from four clinical studies. A two-compartment TMDD model with two binding sites based on the QE approximation adequately described the concentration–time profiles of total pozelimab and total C5 following IV or SC administration of pozelimab in healthy adult participants, adult patients with PNH, and pediatric and adult patients with CHAPLE disease. Covariate analysis resulted in identification of body weight as the most important source of pozelimab PK variability. The final TMDD PopPK model retained the positively correlated body weight effect on CL, Vc, and Vp.

Sensitivity analysis results comparing the effect of time-varying body weight and baseline body weight on PK parameters (CL, Vc, and Vp) suggested minimal differences in PK parameter estimates and model performance for the range of body weight differences. Comparison of model-estimated and fixed exponents relating time-varying body weight to PK parameters also showed that models with body weight exponents fixed to allometric values yielded poorer fitting compared to the models with estimated exponents, and that exponents for body weight on CL and Vc/Vp differed from the allometry-derived values assumed in this scaling approach.

Patients with CHAPLE disease experience gastrointestinal protein wasting which leads to hypoproteinemia, edema, and pleural and pericardial effusions [20]. Aside from the difficulty of the clinical symptoms of gastrointestinal pain, vomiting and diarrhea, the pathological process has potential implications for the pharmacokinetics of pozelimab. For instance, the protein loss characteristic of CHAPLE disease and manifest as hypogammaglobulinemia could result in faster clearance of this Immunoglobulin G (IgG) antibody. The administration of the large IV loading dose of pozelimab 30 mg/kg followed by the maintenance was sufficient to reverse the complement-mediated lymphangiectasia, chyle leaks and protein loss such that faster pozelimab clearance in patients with CHAPLE disease was not detected. Both total Ig and IgG, in particular, rapidly increased 7 days after initiating treatment, returning to within normal limits after 29 days. However, it should be noted that the PK sampling in this CHAPLE patient population was sparse and concentrations immediately following the first dose, where more rapid clearance may have been apparent, were not measured. The hypoalbuminemia manifest in patients with CHAPLE disease often results in edema which could have affected the volume of distribution of pozelimab in these patients. While the sample size for this study population was relatively small, there was no apparent evidence of time-dependent changes in volume parameters (i.e., Vc or Vp) identified in these patients.

Consistent with most monoclonal antibodies [21], we found that the elimination of pozelimab is characterized by both linear and nonlinear pathways in the PopPK model; the relationship between the CL components of the PopPK model was also dependent on pozelimab concentrations. At low pozelimab concentrations, target-mediated elimination contributed a significant portion to CL. However, with increasing pozelimab concentrations, the target-mediated elimination pathway became saturated and CL approached a first-order linear process while the contribution from the nonlinear pathway became negligible. At the FDA approved dosing regimen for patients with CHAPLE disease, total pozelimab concentrations are expected to be maintained above 200 mg/L at steady-state, where the elimination pathway is mainly via the linear pathway. Clear nonlinearity due to a target-mediated process was demonstrated in the simulated concentration–time profiles for free pozelimab (Fig. 4). Because the pozelimab-C5 complex was cleared at a similar rate as free pozelimab, the concentration–time profiles for total pozelimab appear linear. Despite the apparent linearity of total pozelimab, the ratio of free pozelimab to the complex was constantly changing with the decreasing portion of free pozelimab to total pozelimab.

As body weight was identified as the most important source of pozelimab PK variability affecting CL, Vc, and Vp, the dose was adjusted based on body weight for the predominantly pediatric patients with CHAPLE disease. Weekly SC weight-tiered dosing regimens of pozelimab were studied, and model-predicted exposures following weight-tiered dosing regimens of pozelimab were consistent with the observed data, indicating that the final PopPK model adequately described pozelimab PK in healthy volunteers, patients with PNH, and patients with CHAPLE disease. While the weight-tiered SC dosing regimen offers several advantages over the weight-based dosing regimen, including convenience, better compliance, and less risk of medical error or medical waste, it could result in unnecessary fluctuation of drug exposures for patients with a body weight range across the tiers (e.g., from 200 mg for a patient weighing 19 kg to 350 mg for a patient weighing 20 kg, a dose increase of 75%). A weight-based dose eliminates such fluctuation and offers more consistent exposure across all body weight groups. Therefore, the final PopPK model was used to simulate total pozelimab and free C5 concentrations followed a weight-based dosing regimen of 10 mg/kg SC QW, which showed comparable total pozelimab exposure in patients with CHAPLE disease weighing > 20 kg between weight-tiered doses and the 10 mg/kg weight-based dose. In addition, simulations for free C5 suggest that the 10 mg/kg SC QW dose was expected to adequately suppress free C5 to pharmacologically inactive levels. Simulations based on the PopPK model with time-varying body weight effect also demonstrated consistent results. Thus, the simulation results supported the approval of a 10 mg/kg SC QW weight-based dosing regimen (following an initial 30 mg/kg IV loading dose on day 1) by the US FDA (pozelimab-bbfg) [9]. If after at least 3 weekly doses clinical response is determined to be inadequate, the dose may be increased to 12 mg/kg SC QW, with a maximum dose of 800 mg SC QW. Both C5 production and response to pozelimab are variable, so the dose increase ensures that patients attain normalization of albumin and resolution of their symptoms. The current dose regimen, starting with a 10 mg/kg SC QW with the possibility of escalation to 12 mg/kg SC QW, follows a minimum effective dose approach with individualization of dose per patient response. Of particular concern if the disease is not well controlled and complement activity is not adequately reduced is the risk of thrombotic events, reported in some patients with CHAPLE disease due to the putative cross-regulation of the complement and coagulation cascades [20].

Complete inhibition of complement activity by pozelimab was also evident by the rapid and sustained normalization of albumin in all patients with CHAPLE disease enrolled in the clinical trial (Supplementary Figure S12); this demonstrates the efficacy of pozelimab in treating patients with this condition.

Pozelimab is also currently being investigated for the treatment of PNH. Initial results from a phase 2 trial (NCT03946748) in which adult patients with PNH received a single IV loading dose of pozelimab 30 mg/kg followed by a weekly SC dose of 800 mg show that pozelimab demonstrated control of intravascular hemolysis.

Conclusions

Our results show that the final TMDD PopPK model adequately describes the concentration–time profiles of total pozelimab and total C5 following the administration of pozelimab in patients with CHAPLE disease, and provides reliable predictions for individual exposures which supported the use of a 10 mg/kg weight-based maintenance dose regimen in patients with CHAPLE disease.