From User Experience to Data-Driven

My thesis explores the optimization of asparaginase treatment for children with acute lymphoblastic leukemia (ALL) by integrating clinical data and advanced modeling techniques. Asparaginase, an enzyme that depletes L-asparagine—essential for ALL cell survival but not for healthy cells—plays a critical role in therapy. Its efficacy, however, is influenced by factors like hypersensitivity reactions, inter and intra-individual differences in pharmacokinetics, and dosing schedules. My research highlights that initiating treatment with PEGasparaginase, a pegylated formulation, reduces hypersensitivity compared to non-pegylated versions. Continuous, break-free dosing schedules further lower hypersensitivity risks without compromising treatment outcomes. In contrast, interruptions increase antibody development and adverse reactions. Pharmacokinetic studies in infants confirmed that standard dosages effectively achieve therapeutic asparaginase activity without age-based adjustments. In the central nervous system (CNS), PEGasparaginase was less effective at depleting cerebrospinal fluid (CSF) L-asparagine, though CNS relapse rates remained unchanged, suggesting that complete CSF depletion may not be essential. Alternative strategies for patients with PEGasparaginase hypersensitivity were investigated, demonstrating that E. coli asparaginase could be effective if PEG-specific antibodies are the primary issue and therapeutic drug monitoring is available. A new dosing guideline, developed using pharmacokinetic modeling, achieved target asparaginase activity more consistently with fewer dose adjustments, improving safety and efficacy. Finally, a comprehensive review showed that while maintaining therapeutic asparaginase levels is crucial, extending exposure durations or increasing dosages beyond therapeutic thresholds does not improve outcomes. This thesis establishes actionable recommendations to refine asparaginase protocols, enhance safety, and optimize treatment for children with ALL.