Daniel Centanni (1), Mats O. Karlsson (1), Merete Dam (2,3), Anne Mols Krarup (2,3), Birgitte Klug Albertsen (2,3), Lena E. Friberg (1)
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Introduction: Acute lymphoblastic leukemia (ALL) is the most prevalent form of pediatric cancer, accounting for approximately 6540 new cases and over 1300 fatalities in the United States in 2023 [1,2]. Asparaginase treatment has become pivotal for ALL and has vastly improved survival time, with pegylated asparaginase (PEG-Asp) being the most frequently used preparation. Despite its importance, the use of PEG-Asp is not without challenges. Variability in Asparaginase enzyme activity (AEA) levels both between and within subject is considerable, and treatment with PEG-Asp is associated with a broad spectrum of toxicities as well as the potential to elicit a hypersensitivity reaction (HSR) with inactivation (IA) of AEA [1–3]. Such events can lead to potentially life-threatening symptoms and premature therapy cessation. Due to the high frequency of IA, therapeutic drug monitoring (TDM) has been introduced to confirm suspected cases (with HSR) of PEG-Asp IA and detect silent IA (i.e. without clinical manifestation) [4,5]. TDM has been applied across several ALL NOPHO dosing protocols. Firstly, this was introduced during the NOPHO ALL2008 protocol and then in ALLTogether (A2G) pilot study [6,7].
Objectives: In this study, we aimed to characterize the pharmacokinetics (PK) of PEG-Asp and capture PK alterations due to IA after both intravenous (IV) and intramuscular (IM) dosing. The specific objectives were to use the combined data from the abovementioned protocols in order to:
- Expand limited popPK IM dosing knowledge and further delineate the PK of PEG-Asp following repeated IV and IM administrations in non-IA-patients, examining trends in PK over the course of treatment and variations attributable to protocol differences.
- Identify variations in PK parameters among patients exhibiting hypersensitivity, utilizing mixture modeling to differentiate these from typical PK profiles.
- Investigate the influence of patient characteristics, PK characteristics and administration routes on the development of hypersensitivity.
Methods: Data from 772 patients with 5336 samples were available from NOPHO ALL2008 (100% IM dosing) and A2G Pilot Study (89% IV, 11% IM dosing) protocols. PEG-Asp dosing occurred every 14 days, at 1000 IU/m2 for NOPHO ALL2008 and 1500 IU/m2 for patients <16 years and 1000 IU/m2 for ≥16 years in A2G Pilot Study. Patients were classified according to risk group (standard to high) depending on several protocol specific clinical factors (e.g., age, white blood cell count, cytogenic lesions, treatment response). Model construction was performed using NONMEM (version 7.5.1), with SAEM followed by IMP estimation method. Models were assessed through analysis of goodness-of-fit plots, visual predictive checks and changes in OFV. Development started with non-HSF patients to compare a previously established transit compartment model [8] to additional structures and subsequent covariate analysis to establish a base PK model. This was followed by HSF patient inclusion and mixture modeling for differentiation purposes.
Results: A one compartment model with a sigmoid Emax equation varying with time after dose (TAD) was best able to describe the increase in clearance (CL) of PEG-Asp over a dosing occasion, likely due to depegylation, as compared to previous transit compartment structure (dAIC = -227.29). To retain the increase in CL during a dosing interval, each dose entered different compartments with distinct TADs. Visualization of the pattern of inter occasion variability demonstrated a decrease in CL over treatment time in non-IA subjects, which was then captured by a sigmoid Emax function varying over treatment. A mixture model adequately allowed for capturing the increased CL occurring in due to IA. The PK after IM dosing was best described by combined first (48%) and zero order (52%) absorption processes.
Conclusions: Through a multi-protocol model building approach, we developed a novel model structure to capture the PK of PEG-Asp, including the periodic increase in CL from (I) depegylation over one dosing interval, (II) hypersensitivity over dosing intervals and (III) a reduction of CL over time in non-IA-patients. The simplified model structure may facilitate clinical implementation, for example to identify cases of hypersensitivity with IA as increased CL over time, via mixture modeling. The observed decline in CL among non-IA patients may warrant further investigation to assess its significance.
References:
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[6] Gottschalk Højfeldt S, Grell K, Abrahamsson J, Lund B, Vettenranta K, Jónsson ÓG, et al. Relapse risk following truncation of pegylated asparaginase in childhood acute lymphoblastic leukemia. Blood 2021;137:2373–82. https://doi.org/10.1182/blood.2020006583.
[7] Dam M, Centanni M, Friberg LE, Centanni D, Karlsson MO, Stensig Lynggaard L, et al. Increase in peg-asparaginase clearance as a predictor for inactivation in patients with acute lymphoblastic leukemia. Leukemia 2024. https://doi.org/10.1038/s41375-024-02153-6.
[8] Würthwein G, Lanvers-Kaminsky C, Hempel G, Gastine S, Möricke A, Schrappe M, et al. Population Pharmacokinetics to Model the Time-Varying Clearance of the PEGylated Asparaginase Oncaspar® in Children with Acute Lymphoblastic Leukemia. Eur J Drug Metab Pharmacokinet 2017;42:955–63. https://doi.org/10.1007/s13318-017-0410-5.
Reference: PAGE 32 (2024) Abstr 11147 [www.page-meeting.org/?abstract=11147]
Poster: Drug/Disease Modelling - Oncology