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

CPT Pharmacometrics Syst Pharmacol. 2021 Mar;10(3):241-254. doi: 10.1002/psp4.12588. Epub 2021 Mar 7.

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 using 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 with 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 end points or patient-reported outcomes, thereby promising important benefits for future personalized therapies.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Antineoplastic Agents, Phytogenic / adverse effects
  • Antineoplastic Agents, Phytogenic / pharmacokinetics
  • Bayes Theorem
  • Biomarkers, Pharmacological / analysis*
  • Clinical Decision Rules
  • Computer Simulation
  • Drug Development
  • Drug Discovery
  • Drug-Related Side Effects and Adverse Reactions / prevention & control*
  • Female
  • Humans
  • Learning / physiology*
  • Male
  • Maximum Tolerated Dose
  • Medical Oncology
  • Middle Aged
  • Neutropenia / chemically induced*
  • Neutropenia / prevention & control
  • Paclitaxel / adverse effects*
  • Paclitaxel / pharmacokinetics
  • Patient Reported Outcome Measures
  • Quality Improvement
  • Reinforcement, Psychology
  • Safety
  • Treatment Outcome
  • Uncertainty

Substances

  • Antineoplastic Agents, Phytogenic
  • Biomarkers, Pharmacological
  • Paclitaxel