  Model-informed precision dosing (MIPD) using therapeutic drug/biomarker
monitoring offers the opportunity to significantly improve the efficacy and
safety of drug therapies. Current strategies comprise model-informed dosing
tables or are based on maximum a-posteriori estimates. These approaches,
however, lack a quantification of uncertainty and/or consider only part of the
available patient-specific information. We propose three novel approaches for
MIPD employing Bayesian data assimilation (DA) and/or reinforcement learning
(RL) to control neutropenia, the major dose-limiting side effect in anticancer
chemotherapy. These approaches have the potential to substantially reduce the
incidence of life-threatening grade 4 and subtherapeutic grade 0 neutropenia
compared to existing approaches. We further show that RL allows to gain further
insights by identifying patient factors that drive dose decisions. Due to its
flexibility, the proposed combined DA-RL approach can easily be extended to
integrate multiple endpoints or patient-reported outcomes, thereby promising
important benefits for future personalized therapies.
