The aim of this study is to optimize target trough attainment while minimizing high exposures that impose increased risk of side effects such as hyperammonemia. Clinical pharmacology is based on the principle that plasma drug concentrations are linked to therapeutic effects. Therefore, understanding plasma drug concentrations is critical to balancing efficacy and toxicity in oncology treatment, as shown with Erwinase where elevated activity increases the risk of hyperammonemia. TDM can guide dose adjustments to reduce PK variability and improve outcomes. However, harnessing all information from TDM data can be challenging due to sparse and uneven sampling, and multiple sources of PK variability. Advanced analytical tools, such as pharmacometrics and population PK modelling, can address these complexities by quantifying exposure variability in patients and linking it to dosage, patient characteristics, and biomarkers. Methods: This is a NOPHO study. Within the NOPHO, Erwinase was administered intramuscularly (IM) at a dose of 20,000 IU/m² on a Mon-Wed-Fri schedule for two weeks. All non-high-risk (non-HR) patients received the same dose. HR-patients received additional Erwinase, with three doses at 2-day intervals in each treatment block. Activity levels are available from \~150 patients; among these, 20 patients received more than six doses (range: 7-42). In the A2G-1, Erwinase was administered intravenously (IV) at 20,000 IU/m², substituting one PEG-asparaginase dose with seven Erwinase doses every other day. The number of doses received varied depending on the timing of hypersensitivity reactions. Activity levels are already available from \>90 patients. Measurement of Erwinase enzyme activity levels is performed at the Asparaginase Lab in Aarhus. All samples have been analysed. The data will undergo further cleaning, validation, followed by integration with patient characteristics in a population PK model. Data Management and Security in Uppsala: All PK data will be pseudo-anonymized before being securely transferred to Uppsala University through the Allvis data portal. Data handling and formatting will be scripted in R to ensure transparency and reproducibility. Modelling will be performed on the UPPMAX computing cluster via the Swedish National Academic Infrastructure for Supercomputing. Workflow, statistics and perspectives: The modelling workflow will be performed as follows: 1. A PK model for Erwinase will be established based on asparaginase enzyme activity TDM data obtained from Nordic/Baltic ALL patients treated under the NOPHO and A2G-1. Drug clearance, volume of distribution, absorption, and bioavailability will be derived to describe the PK of both routes of Erwinase administration (IV vs IM). Nonlinear mixed-effects modelling will be applied to obtain both typical population PK parameters as well as patient variability parameters. Potential changes in parameters within patients over time will also be explored. Model development and evaluation will be conducted using NONMEM17. Model selection will be based on statistical fit and biological plausibility. Goodness of fit plots and visual predictive checks will ensure the PK model adequately describes the observed data. 2. The Erwinase PK model will be applied to explore alternative dosing strategies, as simultaneously achieving aimed target attainment (≥100 IU/L).
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Pharmacokinetic Model
Timeframe: 12-31-2027