Emapalumab in Patients With Macrophage Activation Syndrome Associated With Still's Disease: A Population Pharmacokinetic/Pharmacodynamic Analysis

Patrick Brossard

doi:10.1111/cts.70163

. 2025 Feb 13;18(2):e70163. doi: 10.1111/cts.70163

Emapalumab in Patients With Macrophage Activation Syndrome Associated With Still's Disease: A Population Pharmacokinetic/Pharmacodynamic Analysis

Patrick Brossard ^1,^✉

PMCID: PMC11822261 PMID: 39943917

ABSTRACT

Macrophage activation syndrome (MAS) is a life‐threatening form of secondary haemophagocytic lymphohistiocytosis (HLH) associated with rheumatic diseases, most commonly Still's disease. This study aimed to develop a population pharmacokinetic (PK)/pharmacodynamic (PD) model for emapalumab, a fully human monoclonal antibody that targets interferon‐gamma (IFNγ), in patients with MAS associated with Still's disease. A two‐compartment disposition model based on data from patients with primary HLH administered emapalumab (1 mg/kg every 3 days, with possible increases to 3, 6 or 10 mg/kg) was re‐estimated for patients with MAS administered emapalumab (6 mg/kg, then 3 mg/kg every 3 days until day 15 and twice weekly until day 28). An exploratory population PK/PD analysis comprising patients' PD data for total IFNγ, chemokine C‐X‐C motif ligand 9 (CXCL9) and ferritin was performed. Emapalumab clearance was generally linear and independent of total IFNγ levels in patients with MAS (n = 14). Estimated baseline levels of CXCL9 (a marker of IFNγ activity), soluble interleukin‐2 receptor α (sIL‐2Rα; a marker of hyperinflammation) and ferritin (a clinical marker of MAS disease activity) were 8400, 6550 and 15,300 μg/L, respectively. All three PD markers responded rapidly to changes in emapalumab concentration. Emapalumab almost completely suppressed CXCL9, sIL2‐Rα, and ferritin production (estimated reduction in synthesis rate: 98.3%, 87%, and 99.6%, respectively). Population PK/PD modeling indicated that emapalumab rapidly suppresses markers of hyperinflammation in patients with MAS associated with Still's disease. Emapalumab dosing regimen used in clinical trials in patients with MAS is unlikely to need adjustment.

Keywords: Emapalumab, interferon‐γ, macrophage activation syndrome, pharmacodynamics, pharmacokinetics, systemic juvenile idiopathic arthritis

Summary.

What is the current knowledge on the topic?
- ○
  Emapalumab, an anti‐interferon γ (IFNγ) monoclonal antibody, has demonstrated efficacy in patients with primary haemophagocytic lymphohistiocytosis (HLH) and macrophage activation syndrome (MAS) in patients with Still's disease. High IFNγ levels result in target‐mediated drug disposition for emapalumab, but IFNγ levels are usually orders of magnitude lower in patients with MAS in Still's disease compared with patients with primary HLH.
What question does this study address?
- ○
  This study aimed to investigate the pharmacokinetic (PK) and pharmacodynamic (PD) properties of emapalumab in patients with MAS associated with Still's disease, which is associated with the lower IFNγ levels than in patients with primary HLH.
What does this study add to our knowledge?
- ○
  The PK profile of emapalumab is similar in patients with MAS in Still's disease and primary HLH, but lower levels of IFNγ in patients with MAS in Still's disease result in the half‐life of emapalumab being close to that observed in healthy subjects. Emapalumab (6 mg/kg, then 3 mg/kg every 3 days until Day 15 and twice weekly until Day 28) rapidly suppresses markers of IFNγ activity and hyperinflammation in patients with MAS in Still's disease.
How might this change clinical pharmacology or translational science?
- ○
  Studies of emapalumab in patients with primary HLH used a dosing regimen starting at 1 mg/kg, followed by dose escalation (to 3, 6 or 10 mg/kg) and/or increased dose frequency, if necessary to manage hyperinflammation, based on clinical and laboratory criteria. This study demonstrates the PK and PD of using higher loading and maintenance doses of emapalumab treatment to manage hyperinflammation in patients with MAS in Still's disease, which may avoid the need for dose adjustment to maintain efficacy.

1. Introduction

Haemophagocytic lymphohistiocytosis (HLH) is a rare, aggressive, and potentially fatal syndrome characterized by excessive, uncontrolled immune activation and systemic hyperinflammation [1, 2, 3]. Primary HLH, which is associated with genetic defects, can only be cured following hematopoietic stem cell transplant, whereas secondary HLH is typically triggered by an acute event, such as an infection, in patients with a predisposing underlying condition, such as an autoimmune/autoinflammatory disease, malignancy, or immunochemotherapy [1, 4, 5, 6, 7].

Macrophage activation syndrome (MAS) is a life‐threatening form of secondary HLH observed in patients with rheumatic diseases, most commonly associated with Still's disease (systemic juvenile idiopathic arthritis [sJIA]/adult‐onset Still's disease [AOSD]) [8]. MAS presents clinically in approximately 10% and sub‐clinically in approximately 30% of patients with sJIA [9]. While patients with primary HLH have traditionally been treated using immunochemotherapy with glucocorticoids and etoposide (with or without ciclosporin A), recommended treatments for patients with MAS generally target the underlying condition using glucocorticoids (with or without an interleukin (IL)‐1 inhibitor and/or IL‐6 inhibitor) [10, 11].

Interferon‐γ (IFNγ) contributes to macrophage activation and primes the production of pro‐inflammatory cytokines in HLH, resulting in a hyperinflammatory positive feedback loop, wherein uncontrolled IFNγ production by T cells results in macrophage overactivation and cytokine production, promoting even more IFNγ production by T cells [12, 13]. Emapalumab is a fully human monoclonal antibody that targets IFNγ, neutralizing its biological activity and suppressing IFNγ‐driven hyperinflammation [14, 15, 16]. Plasma concentrations of chemokine C‐X‐C motif ligand 9 (CXCL9), a chemokine induced specifically by IFNγ, can be used as a biomarker of IFNγ activity and lower levels of CXCL9 compared with baseline have been associated with treatment response in patients with primary HLH [15].

Pharmacometric modeling using reductions in CXCL9 levels as a marker for IFNγ neutralization following emapalumab administration in patients with primary HLH, along with clinical and laboratory observations, have been used to investigate emapalumab dosing and dose adaptation [14]. In particular, emapalumab has been found to be subject to a target‐mediated drug disposition (TMDD)‐like mechanism in patients with primary HLH, wherein the rate at which emapalumab is cleared appears to be influenced by the presence of excessive IFNγ levels under certain circumstances [14].

Emapalumab has also demonstrated efficacy and safety in patients with MAS associated with Still's disease who failed high‐dose glucocorticoids [16]. However, the differing underlying pathologies for primary versus secondary HLH [3] mean that IFNγ levels in patients with primary HLH are orders of magnitude higher than in patients with MAS [17]. Accordingly, further investigation of the pharmacokinetic (PK) and pharmacodynamic (PD) properties of emapalumab in patients with MAS associated with Still's disease was required [14, 16, 17]. This study aimed to develop a population PK/PD model for emapalumab in patients with MAS associated with Still's disease.

2. Methods

2.1. Population PK Analysis

PK data were pooled (n = 58; 2709 samples) from three studies: (i) an open‐label, single‐arm, phase 2/3 clinical trial of 45 patients who received emapalumab for primary HLH (NCT01818492); (ii) a pilot, open‐label, single‐arm, phase 2 study of 14 patients who received emapalumab for MAS in Still's disease (NCT03311854); and (iii) a 1‐year, long‐term, follow‐up study of patients from both studies (NCT02069899). Patients with primary HLH were administered emapalumab 1 mg/kg via intravenous infusion over 1 h, while subsequent doses could be increased to 3, 6, and up to 10 mg/kg every 3 days on the basis of clinical response [15], whereas patients with MAS in Still's disease were administered an initial dose of emapalumab 6 mg/kg, followed by 3 mg/kg every 3 days until Day 15 and twice weekly until Day 28 [16]. Emapalumab could continue to be administered during the long‐term follow‐up study to patients with primary HLH [15]. Only one patient with MAS in Still's disease received emapalumab treatment beyond Day 28 (to Day 39) [16]. Data from one patient with primary HLH who participated in NCT01818492 was not included in the analysis after being identified as an outlier because population predictions were much higher than observed concentrations. The lower limit of quantification for emapalumab was 62.5 ng/mL.

The base model comprised a two‐compartment disposition model with allometric scaling for body weight (exponents for allometry were fixed to +0.75 and +1, for clearances and volumes of distribution) based on data from patients with primary HLH administered emapalumab and from patients administered emapalumab as part of a compassionate use programme [17]. A clearance parameter that was dependent on total IFNγ concentration using a power function was also added [17].

Goodness‐of‐fit (GOF) plots were used to judge the appropriateness of the model fit and interpreted in the context of shrinkage, where shrinkage < 20% was considered to represent a reliable plot. Predictive performance of the final model was assessed using a visual predictive check (VPC). Plasma concentrations of emapalumab were simulated 1000 times using dose and covariate data, as well as the same sampling schedule, from the study participants who provided the data for the model development dataset. The 5th, 50th, and 95th percentiles of the predictions and observations were derived and plotted against time.

2.2. Population PK/PD Analysis

An analysis data set was created for exploratory population PK/PD analysis comprising patients' PD data for total IFNγ, CXCL9, and ferritin. Joint PK/PD models for CXCL9, soluble interleukin‐2 receptor α (sIL‐2Rα) and ferritin were investigated in parallel and sequentially; the sequential model was developed for CXCL9 first, before adding sIL‐2Rα, followed by ferritin.

The population PK model prepared using data from patients with both primary and secondary HLH was then re‐estimated by fitting the model to the Study (ii) population (patients with MAS associated with Still's disease) PK NONMEM analysis dataset to obtain estimates for the fixed and individual random effect parameters. Estimated individual PK parameters from the population PK model were fixed for the population PK/PD model and only the PD and PK/PD parameters were estimated in the population PK/PD model.

Individual PK parameters, recorded dosing events and covariates (IFNγ, age, weight and total bilirubin) were used to predict individual PK–time profiles, which were subsequently summarized as time‐matched exposure variables for each PD record. The area under the PK–time curve was calculated for 1 week prior to each PD record and changes in PD parameters reported as relative change from baseline.

Turnover models were investigated assuming that the PD markers were produced by a zero‐order process (i.e., a constant rate of production) and eliminated by a first‐order process (i.e., concentration‐dependent elimination). PD marker concentrations were then assumed to be in steady‐state when emapalumab treatment was initiated, while emapalumab concentrations were assumed to affect (inhibit) PD marker production rates. Indirect drug effects were investigated for sIL2‐Rα and ferritin, to determine if changes were directly related to emapalumab concentrations or the concentration of another PD marker (CXCL9 for sIL‐2Rα or sIL‐2Rα for ferritin).

Inter‐individual variability was tested on at least the baseline parameter and one PD marker. If required during model development, an additive or proportional component of residual unexplained variability was fixed or removed. Covariates were only tested for parameters with associated inter‐individual variability.

The predictive performance of the final population PK/PD model was assessed using GOF plots and VPCs after estimated PD parameters were simulated 500 times using the model development dataset. The 5th, 50th, and 95th percentiles of the predictions and observations were derived and plotted against time and overlaid against the respective summary statistics of the observed data to visually assess the alignment between the model‐simulated and the observed data. In addition, a bootstrap procedure was performed to obtain bootstrap confidence intervals (CIs) for key models using at least 500 re‐sampled data sets (stratified at the patient level) when it was determined that the NONMEM‐reported PD marker precision was not considered to be reliable.

2.3. Statistics

The population PK analysis data set was prepared with SAS software (version 9.4) and population PK analysis performed using NONMEM (version 7.5.0) with first order conditional estimation with interaction. PsN (version 5.0.0) was used to execute NONMEM runs and to perform VPCs and R software (version 4.1.1) was used for exploratory analysis, evaluation of GOF plots and summary statistics. The population PK/PD analysis was performed using NONMEM (version 7.4.1) compiled with GNU Fortran (Homebrew GCC 12.2.0) and data management, as well as plotting and tabulating of results performed using R software (version 4.2.2).

3. Results

Samples for PK analysis were collected from 58 patients, comprising 44 patients with primary HLH and 14 with MAS associated with Still's disease. Patients with MAS associated with Still's disease were older than patients with primary HLH, had higher baseline alanine aminotransferase levels and lower baseline total IFNγ (see Table S1).

3.1. Population PK Modeling

The parameter estimates for the final population PK model for patients with MAS in Still's disease are presented in Table S2. The precision of the fixed‐effects estimates was good with only non‐linear clearance and effects of age on PK parameters having a residual standard error > 20%. Age impacted clearance, inter‐compartmental clearance and the volume of the central compartment, which may be expected as part of the maturation process as young children age. Total bilirubin also had a significant impact on emapalumab clearance and both the central and peripheral compartments.

Emapalumab clearance was generally linear and independent of total IFNγ levels because of the lower levels of IFNγ in patients with MAS versus primary HLH. Therefore, making clearance dependent on the observed total IFNγ concentration (i.e., accounting for potential TMDD) was not necessary for patients with MAS associated with Still's disease.

GOF plots indicated good agreement between observations and prediction in both studies of patients with MAS in Still's disease. Prediction‐corrected VPCs as a function of IFNγ level demonstrated no bias in simulations across total IFNγ levels (Figure S1).

3.2. Population PK/PD Modeling

The (re‐)estimated parameters for the PK model to be used in the final population PK/PD model for patients with MAS associated with Still's disease are presented in Table 1. All PD markers showed a clear reduction during the treatment period, except for an increase in total IFNγ, which increased substantially during the first 3 days of emapalumab treatment before stabilizing, then gradually reducing with ongoing treatment. This pattern was consistent with treatment initiation resulting in emapalumab‐bound IFNγ being observed, in addition to free IFNγ, when assessing total IFNγ levels [14].

TABLE 1.

Parameter (re‐)estimates for the population PK model to be used in a population PK/PD model using data from patients with MAS associated with sJIA.

Parameter	Role	Estimate (95% CI)	RSE, %
CLL	Typical value, L/h	0.0143 (0.0118–0.0169)	9.2
	CL, Q age effect, exponent	0.188 (0.124–0.251)	17.4
	CL, V1, V2 bilirubin effect, exponent	0.162 (0.137–0.187)	8.0
CLNL	Intercept for IFNγ = 1 × 10⁶	0.121 (0.0926–0.149)	11.9
CLNL	IFNγ effect, exponent	0.542 (0.503–0.580)	3.6
V1	Typical value, L	3.08 (2.61–3.55)	7.8
	Age effect, exponent	−0.104 (−0.156–0.0529)	25.2
	Inter‐individual variability	0.207 (0.143–0.271)	15.8
	Correlation (V1, V2)	0.569 (0.297–0.841)	24.4
Q	Typical value, L/h	0.105 (0.0815–0.128)	11.4
V2	Typical value, L	4.28 (3.43–5.12)	10.1
V2	Inter‐individual variability	0.711 (0.555–0.867)	11.2
RUV	Additive component, log	0.301 (0.293–0.309)	1.4
CL	Inter‐individual variability	0.361 (0.291–0.431)	9.9

Open in a new tab

Abbreviations: CI, confidence interval; CL, clearance; CLL, linear clearance independent of total IFNγ; CLNL, non‐linear clearance dependent of total IFNγ; IFNγ, interferon γ; MAS, macrophage activation syndrome; PD, pharmacodynamic; PK, pharmacokinetic; Q, inter‐compartmental clearance; RSE, relative standard error; RUV, residual unexplained variability; sJIA, systemic juvenile idiopathic arthritis; V1, volume of the central compartment; V2, volume of the peripheral compartment.

A parallel model wherein emapalumab affected all parameters at once, rather than the effects on sIL‐2Rα and ferritin occurring sequentially, resulted in a better fit with the observed data and was used in the final PK/PD model consisting of a two‐compartment PK model linked with three turnover models for CXCL9, sIL‐2Rα, and ferritin concentrations (Figure 1).

Schematic of the PK/PD model for CXCL9, sIL‐2Rα, and ferritin in patients with MAS associated with sJIA treated with emapalumab.

Estimated baseline levels of CXCL9, sIL‐2Rα and ferritin were 8400 ng/L, 6550 ng/L and 15,300 μg/L, respectively and all three PD markers showed a rapid response to changes in emapalumab concentration. Emapalumab almost completely suppressed CXCL9, sIL2‐Rα, and ferritin production (estimated reduction in synthesis rate: 98.3%, 87%, and 99.6%, respectively).

Standard errors for all parameters estimated using a bootstrap procedure were < 40% of their respective bootstrap means, indicating good model precision (Table 2) and shrinkage was below 10% in all cases. In addition, the means of the random effects parameters were not significantly different from 0. Good alignment was also observed between individual predictions and observed PD concentrations for all three parameters (Figure 2) and VPCs indicated good alignment between model predictions and observed data (Figure 3). In almost all cases, the observed data fell within the predicted 90% CI.

TABLE 2.

Final population PK/PD model for emapalumab in patients with MAS associated with sJIA.

			Original		Bootstrap (N = 500)
PD marker	Parameter	Role	Estimate	RSE, %	Mean (95% CI)	RSE, %
CXCL9	Baseline	Typical value, ng/L	8400	165	8010 (3420–12,600)	29.3
		Interindividual variability, exponent	1.28	0.942	1.25 (0.949–1.55)	12.3
		Correlation with baseline ferritin	0.675	0.288	0.665 (0.414–0.915)	19.2
	Degradation rate	Typical value, per day	0.414	37.5	0.401 (0.242–0.559)	20.2
	Degradation rate	Interindividual variability, exponent	0.694	43.5	0.682 (0.43–0.934)	18.9
	I_max	Typical value, %	98.3	5.94	98.2 (97.2–99.2)	0.5
	I_max	Interindividual variability, logit	1.07	212	1 (0.606–1.4)	20.2
	RUV	Proportion, %	46.4	66.3	46.2 (38.7–53.6)	8.2
sIL‐2Rα	Baseline	Typical value, ng/L	6630	617	6700 (4140–9260)	19.5
		Interindividual variability, exponent	0.696	14.9	0.64 (0.29–0.989)	27.9
		Correlation with I_max	0.805	4.95	0.723 (0.162–1.28)	39.6
	Degradation rate	Typical value, per day	0.112	56.9	0.115 (0.0682–0.162)	20.8
	Degradation rate	Interindividual variability, exponent	0.746	18.3	0.693 (0.456–0.93)	17.5
	I_max	Typical value, %	87.3	12.9	86.9 (81.7–92.2)	3.1
	I_max	Interindividual variability, logit	0.891	4.34	0.833 (0.466–1.2)	22.5
	RUV	Proportion, %	29.8	164	29.1 (21–37.2)	14.2
Ferritin	Baseline	Typical value, μg/L	15,300	58.3	15,500 (6600–24,400)	29.3
		Interindividual variability, exponent	1.5	0.127	1.47 (1.13–1.81)	11.9
		Correlation with I_max	0.684	0.238	0.68 (0.421–0.94)	19.5
	Degradation rate	Typical value, per day	0.207	5.89	0.208 (0.165–0.252)	10.7
	Degradation rate	Interindividual variability, exponent	0.37	121	0.349 (0.216–0.482)	19.4
	I_max	Typical value, %	99.6	0.433	99.6 (99.2–99.9)	0.2
	I_max	Interindividual variability, logit	1.65	0.0664	1.61 (1.15–2.08)	14.8
	RUV	Proportion, %	38.9	49	39 (29.4–48.5)	12.6

Open in a new tab

Abbreviations: CI, confidence interval; CXCL9, chemokine C‐X‐C motif ligand 9; I_max, maximum inhibition; MAS, macrophage activation syndrome; PD, pharmacodynamic; PK, pharmacokinetic; RSE, residual standard error; RUV, residual unexplained variation; sIL‐2Rα, soluble interleukin‐2 receptor alpha; sJIA, systemic juvenile idiopathic arthritis.

Observed versus predicted concentrations for CXCL9 (yellow dots), sIL‐2Rα, (red dots) and ferritin (blue dots) in patients with MAS associated with sJIA. CXCL9, chemokine C‐X‐C motif ligand 9; DV, data value (ng/L for CXCL9 and sIL‐2Rα and μg/L for ferritin); MAS, macrophage activation syndrome; sIL‐2Rα, soluble interleukin‐2 receptor alpha; sJIA, systemic juvenile idiopathic arthritis.

VPC of the final population PK/PD model for CXCL9 (a), ferritin (b), and sIL‐2Rα (c) in patients with MAS associated with sJIA. CXCL9, chemokine C‐X‐C motif ligand 9; MAS, macrophage activation syndrome; PD, pharmacodynamic; PK, pharmacokinetic; sIL‐2Rα, soluble interleukin‐2 receptor alpha; sJIA, systemic juvenile idiopathic arthritis; VPC, visual predictive check.

4. Discussion

Emapalumab has a predictable PK/PD profile in patients with MAS associated with Still's disease with modeling demonstrating that emapalumab rapidly suppresses CXCL9, sIL2‐Rα, and ferritin production, indicating effective IFNγ neutralization and control of hyperinflammation. In particular, neutralizing IFNγ may be a key element of controlling MAS, given that monocytes in patients with MAS are hyperresponsive to IFNγ‐mediated signaling [18].

Increases in total IFNγ levels after administering emapalumab to patients with MAS associated with Still's disease are consistent with observations in blood samples from patients with primary HLH [14]. Only a fraction of the free IFNγ that is believed to be present in inflamed tissues may be observed in blood samples before emapalumab treatment [14]. Once emapalumab is administered, and free IFNγ in the blood is bound and neutralized, the equilibrium between blood and tissue IFNγ concentrations may be altered, drawing greater amounts of free IFNγ out of the tissues and into the blood where it becomes emapalumab‐bound and measurable [14]. In particular, the reduced CXCL9 concentrations that are observed alongside increases in total IFNγ indicate that any increase in total IFNγ concentrations is driven by increased emapalumab‐bound (i.e., neutralized) IFNγ.

In contrast to patients with primary HLH, where underlying genetics drive continuous IFNγ production, in patients with MAS administered emapalumab, observed total IFNγ levels decreased with time. This suggests that emapalumab inhibits the IFNγ‐driven positive feedback loop that can occur in patients with MAS, potentially addressing the underlying pathophysiology of MAS in addition to controlling hyperinflammation [13, 14]. Likewise, the ability of the treating physicians to rapidly taper immunosuppressive glucocorticoid treatment in patients with MAS administered emapalumab provides additional evidence of reduced hyperinflammation in patients administered emapalumab [16].

Despite the differences in the underlying pathology of primary and secondary forms of HLH, the PK/PD model for emapalumab in patients with MAS secondary to Still's disease was generally consistent with an earlier model developed in patients with primary HLH [14]. Mean observed total IFNγ levels in patients with MAS associated with Still's disease were substantially lower than those observed in patients with primary HLH, and generally remained below the threshold for TMDD of 10,000 pg/mL, meaning that the half‐life of emapalumab in patients with MAS was close to that of healthy subjects, rather than being shortened, as has been observed in some patients with primary HLH [14, 16]. The stability of the PK/PD effect of emapalumab in patients with MAS also suggests that dose adjustment is less likely to be required to control hyperinflammation in patients with MAS compared with patients with primary HLH. In addition, while the long half‐life means that emapalumab levels can remain detectable in patient serum for many months after control of MAS is achieved, this does not appear to result in an increased risk of adverse events, particularly infection events that may be expected to be associated with suppressed IFNγ signaling [16, 19, 20].

This study is limited by the small sample size of patients with MAS associated with sJIA, which prevented attempts to extend the model to include occasions where PD markers transiently increased in some patients. The age range of patients with Still's disease also required age to be factored into the model, using allometric scaling to account for differences in age groups between the MAS and primary HLH patient populations.

Population PK/PD modeling indicated that emapalumab rapidly suppresses markers of hyperinflammation, such as CXCL9, sIL‐2Rα and ferritin, in patients with MAS associated with Still's disease and reduces total IFNγ over time. This demonstrates indicating effective IFNγ neutralization and control of hyperinflammation by emapalumab in this patient population. In contrast to patients with primary HLH, emapalumab is generally subject to linear clearance in patients with MAS because total IFNγ levels did not generally reach the threshold for TMDD, likely resulting in a reduced need for dose adjustment to control hyperinflammation when using the dosing regimen utilized in clinical trials enrolling patients with MAS associated with Still's disease.

Author Contributions

P.B. wrote the manuscript, designed the research, performed the research, and analyzed the data.

Conflicts of Interest

P.B. is an employee of Sobi.

Supporting information

Data S1:

CTS-18-e70163-s001.docx^{(558.8KB, docx)}

Acknowledgments

The author acknowledges the support provided by Axel Facius (thinkQ2 AG, Baar, Switzerland) in performing pharmacological analyses, developing the models and assisting in interpreting the outputs from the models and contributions of Professor Franco Locatelli, Professor Michael Jordan and Professor Fabrizio De Benedetti as the primary clinical investigators for the studies that contributed data to this analysis. The author also wishes to acknowledge Kathleen York, CMPP and Stefan Duscha, PhD from Sobi (Basel, Switzerland) for publication coordination and Blair Hesp PhD CMPP and Marissa Scandlyn PhD CMPP of Kainic Medical Communications Ltd. (Dunedin, New Zealand) for medical writing and editorial support, funded by Sobi, based on the author's input and direction, and in accordance with Good Publication Practice (GPP) 2022 guidelines (https://www.ismpp.org/gpp‐2022). Sobi reviewed and provided feedback on the manuscript. The author had full editorial control of the manuscript and provided their final approval of all content. Funding for this study was provided by Sobi.

Funding: This study was provided by Sobi.

Data Availability Statement

The datasets analyzed during the current study are available from the corresponding author on reasonable request. Sobi is committed to responsible and ethical sharing of data on the participant level and summary data for medicines and indications approved by the European Medicines Agency and/or Food and Drug Administration, while protecting individual participant integrity and compliance with applicable legislation. Data access will be granted in response to qualified research requests. All requests are evaluated by a cross‐functional panel of experts within Sobi and a decision on sharing will be based on the scientific merit and feasibility of the research proposal, maintenance of personal integrity and commitment to publication of the results. To request access to study data, a data sharing request form (available on www.sobi.com) should be sent to medical.info@sobi.com. Further information on Sobi's data sharing policy and process for requesting access can be found at: https://www.sobi.com/en/policies.

References

1. Allen C. E. and McClain K. L., “Pathophysiology and Epidemiology of Hemophagocytic Lymphohistiocytosis,” Hematology. American Society of Hematology. Education Program 2015 (2015): 177–182. [DOI] [PubMed] [Google Scholar]
2. Janka G. E. and Lehmberg K., “Hemophagocytic Lymphohistiocytosis: Pathogenesis and Treatment,” Hematology. American Society of Hematology. Education Program 2013 (2013): 605–611. [DOI] [PubMed] [Google Scholar]
3. Jordan M. B., Allen C. E., Weitzman S., Filipovich A. H., and McClain K. L., “How I Treat Hemophagocytic Lymphohistiocytosis,” Blood 118 (2011): 4041–4052. [DOI] [PMC free article] [PubMed] [Google Scholar]
4. La Rosee P., Horne A., Hines M., et al., “Recommendations for the Management of Hemophagocytic Lymphohistiocytosis in Adults,” Blood 133 (2019): 2465–2477. [DOI] [PubMed] [Google Scholar]
5. Schram A. M. and Berliner N., “How I Treat Hemophagocytic Lymphohistiocytosis in the Adult Patient,” Blood 125 (2015): 2908–2914. [DOI] [PubMed] [Google Scholar]
6. Hines M. R., von Bahr Greenwood T., Beutel G., et al., “Consensus‐Based Guidelines for the Recognition, Diagnosis, and Management of Hemophagocytic Lymphohistiocytosis in Critically Ill Children and Adults,” Critical Care Medicine 50 (2022): 860–872. [DOI] [PubMed] [Google Scholar]
7. Ramos‐Casals M., Brito‐Zerón P., López‐Guillermo A., Khamashta M. A., and Bosch X., “Adult Haemophagocytic Syndrome,” Lancet 383 (2014): 1503–1516. [DOI] [PubMed] [Google Scholar]
8. Grom A. A., Horne A., and De Benedetti F., “Macrophage Activation Syndrome in the Era of Biologic Therapy,” Nature Reviews Rheumatology 12 (2016): 259–268. [DOI] [PMC free article] [PubMed] [Google Scholar]
9. Crayne C. B., Albeituni S., Nichols K. E., and Cron R. Q., “The Immunology of Macrophage Activation Syndrome,” Frontiers in Immunology 10 (2019): 119. [DOI] [PMC free article] [PubMed] [Google Scholar]
10. Onel K. B., Horton D. B., Lovell D. J., et al., “2021 American College of Rheumatology Guideline for the Treatment of Juvenile Idiopathic Arthritis: Therapeutic Approaches for Oligoarthritis, Temporomandibular Joint Arthritis, and Systemic Juvenile Idiopathic Arthritis,” Arthritis and Rheumatology 74 (2022): 553–569. [DOI] [PMC free article] [PubMed] [Google Scholar]
11. Shakoory B., Geerlinks A., Wilejto M., et al., “The 2022 EULAR/ACR Points to Consider at the Early Stages of Diagnosis and Management of Suspected Haemophagocytic Lymphohistiocytosis/Macrophage Activation Syndrome (HLH/MAS),” Annals of the Rheumatic Diseases 82 (2023): 1271–1285. [DOI] [PMC free article] [PubMed] [Google Scholar]
12. Ivashkiv L. B., “IFNγ: Signalling, Epigenetics and Roles in Immunity, Metabolism, Disease and Cancer Immunotherapy,” Nature Reviews. Immunology 18 (2018): 545–558. [DOI] [PMC free article] [PubMed] [Google Scholar]
13. Schoenborn J. R. and Wilson C. B., “Regulation of Interferon‐Gamma During Innate and Adaptive Immune Responses,” Advances in Immunology 96 (2007): 41–101. [DOI] [PubMed] [Google Scholar]
14. Jacqmin P., Laveille C., Snoeck E., et al., “Emapalumab in Primary Haemophagocytic Lymphohistiocytosis and the Pathogenic Role of Interferon Gamma: A Pharmacometric Model‐Based Approach,” British Journal of Clinical Pharmacology 88 (2022): 2128–2139. [DOI] [PMC free article] [PubMed] [Google Scholar]
15. Locatelli F., Jordan M. B., Allen C., et al., “Emapalumab in Children With Primary Hemophagocytic Lymphohistiocytosis,” New England Journal of Medicine 382 (2020): 1811–1822. [DOI] [PubMed] [Google Scholar]
16. De Benedetti F., Grom A. A., Brogan P. A., et al., “Efficacy and Safety of Emapalumab in Macrophage Activation Syndrome,” Annals of the Rheumatic Diseases 82 (2023): 857–865. [DOI] [PMC free article] [PubMed] [Google Scholar]
17. Brossard P. and Laveille C., “Population Pharmacokinetics of the Anti–Interferon‐Gamma Monoclonal Antibody Emapalumab: An Updated Analysis,” Rheumatology and Therapy 11 (2024): 869–880. [DOI] [PMC free article] [PubMed] [Google Scholar]
18. Pascarella A., Bracaglia C., Caiello I., et al., “Monocytes From Patients With Macrophage Activation Syndrome and Secondary Hemophagocytic Lymphohistiocytosis Are Hyperresponsive to Interferon Gamma,” Frontiers in Immunology 12 (2021): 663329. [DOI] [PMC free article] [PubMed] [Google Scholar]
19. De Benedetti F., Prencipe G., Bracaglia C., Marasco E., and Grom A. A., “Targeting Interferon‐γ in Hyperinflammation: Opportunities and Challenges,” Nature Reviews Rheumatology 17 (2021): 678–691. [DOI] [PubMed] [Google Scholar]
20. Jordan M. B. and Locatelli F., “Exposure–Safety Relationship for Patients With Primary Hemophagocytic Lymphohistiocytosis Treated With Emapalumab,” Pediatric Blood & Cancer 71 (2024): e30778. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data S1:

CTS-18-e70163-s001.docx^{(558.8KB, docx)}

Data Availability Statement

[cts70163-bib-0001] 1. Allen C. E. and McClain K. L., “Pathophysiology and Epidemiology of Hemophagocytic Lymphohistiocytosis,” Hematology. American Society of Hematology. Education Program 2015 (2015): 177–182. [DOI] [PubMed] [Google Scholar]

[cts70163-bib-0002] 2. Janka G. E. and Lehmberg K., “Hemophagocytic Lymphohistiocytosis: Pathogenesis and Treatment,” Hematology. American Society of Hematology. Education Program 2013 (2013): 605–611. [DOI] [PubMed] [Google Scholar]

[cts70163-bib-0003] 3. Jordan M. B., Allen C. E., Weitzman S., Filipovich A. H., and McClain K. L., “How I Treat Hemophagocytic Lymphohistiocytosis,” Blood 118 (2011): 4041–4052. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cts70163-bib-0004] 4. La Rosee P., Horne A., Hines M., et al., “Recommendations for the Management of Hemophagocytic Lymphohistiocytosis in Adults,” Blood 133 (2019): 2465–2477. [DOI] [PubMed] [Google Scholar]

[cts70163-bib-0005] 5. Schram A. M. and Berliner N., “How I Treat Hemophagocytic Lymphohistiocytosis in the Adult Patient,” Blood 125 (2015): 2908–2914. [DOI] [PubMed] [Google Scholar]

[cts70163-bib-0006] 6. Hines M. R., von Bahr Greenwood T., Beutel G., et al., “Consensus‐Based Guidelines for the Recognition, Diagnosis, and Management of Hemophagocytic Lymphohistiocytosis in Critically Ill Children and Adults,” Critical Care Medicine 50 (2022): 860–872. [DOI] [PubMed] [Google Scholar]

[cts70163-bib-0007] 7. Ramos‐Casals M., Brito‐Zerón P., López‐Guillermo A., Khamashta M. A., and Bosch X., “Adult Haemophagocytic Syndrome,” Lancet 383 (2014): 1503–1516. [DOI] [PubMed] [Google Scholar]

[cts70163-bib-0008] 8. Grom A. A., Horne A., and De Benedetti F., “Macrophage Activation Syndrome in the Era of Biologic Therapy,” Nature Reviews Rheumatology 12 (2016): 259–268. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cts70163-bib-0009] 9. Crayne C. B., Albeituni S., Nichols K. E., and Cron R. Q., “The Immunology of Macrophage Activation Syndrome,” Frontiers in Immunology 10 (2019): 119. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cts70163-bib-0010] 10. Onel K. B., Horton D. B., Lovell D. J., et al., “2021 American College of Rheumatology Guideline for the Treatment of Juvenile Idiopathic Arthritis: Therapeutic Approaches for Oligoarthritis, Temporomandibular Joint Arthritis, and Systemic Juvenile Idiopathic Arthritis,” Arthritis and Rheumatology 74 (2022): 553–569. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cts70163-bib-0011] 11. Shakoory B., Geerlinks A., Wilejto M., et al., “The 2022 EULAR/ACR Points to Consider at the Early Stages of Diagnosis and Management of Suspected Haemophagocytic Lymphohistiocytosis/Macrophage Activation Syndrome (HLH/MAS),” Annals of the Rheumatic Diseases 82 (2023): 1271–1285. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cts70163-bib-0012] 12. Ivashkiv L. B., “IFNγ: Signalling, Epigenetics and Roles in Immunity, Metabolism, Disease and Cancer Immunotherapy,” Nature Reviews. Immunology 18 (2018): 545–558. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cts70163-bib-0013] 13. Schoenborn J. R. and Wilson C. B., “Regulation of Interferon‐Gamma During Innate and Adaptive Immune Responses,” Advances in Immunology 96 (2007): 41–101. [DOI] [PubMed] [Google Scholar]

[cts70163-bib-0014] 14. Jacqmin P., Laveille C., Snoeck E., et al., “Emapalumab in Primary Haemophagocytic Lymphohistiocytosis and the Pathogenic Role of Interferon Gamma: A Pharmacometric Model‐Based Approach,” British Journal of Clinical Pharmacology 88 (2022): 2128–2139. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cts70163-bib-0015] 15. Locatelli F., Jordan M. B., Allen C., et al., “Emapalumab in Children With Primary Hemophagocytic Lymphohistiocytosis,” New England Journal of Medicine 382 (2020): 1811–1822. [DOI] [PubMed] [Google Scholar]

[cts70163-bib-0016] 16. De Benedetti F., Grom A. A., Brogan P. A., et al., “Efficacy and Safety of Emapalumab in Macrophage Activation Syndrome,” Annals of the Rheumatic Diseases 82 (2023): 857–865. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cts70163-bib-0017] 17. Brossard P. and Laveille C., “Population Pharmacokinetics of the Anti–Interferon‐Gamma Monoclonal Antibody Emapalumab: An Updated Analysis,” Rheumatology and Therapy 11 (2024): 869–880. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cts70163-bib-0018] 18. Pascarella A., Bracaglia C., Caiello I., et al., “Monocytes From Patients With Macrophage Activation Syndrome and Secondary Hemophagocytic Lymphohistiocytosis Are Hyperresponsive to Interferon Gamma,” Frontiers in Immunology 12 (2021): 663329. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cts70163-bib-0019] 19. De Benedetti F., Prencipe G., Bracaglia C., Marasco E., and Grom A. A., “Targeting Interferon‐γ in Hyperinflammation: Opportunities and Challenges,” Nature Reviews Rheumatology 17 (2021): 678–691. [DOI] [PubMed] [Google Scholar]

[cts70163-bib-0020] 20. Jordan M. B. and Locatelli F., “Exposure–Safety Relationship for Patients With Primary Hemophagocytic Lymphohistiocytosis Treated With Emapalumab,” Pediatric Blood & Cancer 71 (2024): e30778. [DOI] [PubMed] [Google Scholar]

PERMALINK

Emapalumab in Patients With Macrophage Activation Syndrome Associated With Still's Disease: A Population Pharmacokinetic/Pharmacodynamic Analysis

Patrick Brossard

ABSTRACT

Summary.

1. Introduction