Reinforcement learning and Bayesian data assimilation for model-informed precision dosing in oncology

TL;DR

This paper introduces three innovative Bayesian data assimilation and reinforcement learning methods for model-informed precision dosing in oncology, aiming to improve safety and efficacy by better managing neutropenia risks.

Contribution

It presents novel combined DA-RL approaches that incorporate uncertainty quantification and patient-specific data, enhancing personalized chemotherapy dosing strategies.

Findings

Potential to significantly reduce severe neutropenia cases

RL identifies key patient factors influencing dose decisions

Flexible framework adaptable to multiple endpoints

Abstract

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.