Reinforcement learning and Bayesian data assimilation for model-informed precision dosing in oncology
This work addresses personalized dosing for cancer patients to improve therapy efficacy and safety, representing a domain-specific incremental advance.
The authors tackled the problem of model-informed precision dosing in oncology by proposing three novel approaches using Bayesian data assimilation and reinforcement learning to control neutropenia, resulting in potential substantial reductions in life-threatening grade 4 and subtherapeutic grade 0 neutropenia compared to existing methods.
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.