From Prospective Evaluation to Practice: Model-Informed Dose Optimization in Oncology.

Abstract

One dose does not fit all, especially in oncolytic drugs, where side effects and therapy failures highlight the need for personalized dosing approaches. In recent years, the quest to apply model-informed precision dosing to oncology drugs has gained significant momentum, reflecting its potential to revolutionize patient care by tailoring treatments to individual pharmacokinetic profiles. Despite this progress, model-informed precision dosing has not (yet) become widely integrated into routine clinical care. We aimed to explain model-informed precision dosing from a clinical viewpoint while addressing all prospective model-informed precision dosing implementation and validation studies in the field of oncology. We identified 16 different drugs for which prospective model-informed precision dosing validation/implementation has been performed. Although these studies are mostly focused on attaining adequate drug exposures and reducing inter-individual variability, improved clinical outcomes after performing model-informed precision dosing were shown for busulfan, and high-dose methotrexate. Toxicities were significantly reduced for busulfan and cyclophosphamide treatment. In contrast, for carboplatin, for which model-informed precision dosing has been used in the Calvert formula, no prospective validation on outcomes was deemed necessary as the therapeutic window had been extensively validated. Model-informed precision dosing has shown to be of added value in oncology and is expected to significantly change dosing regimens in the future.