Reinforcement learning and Bayesian data assimilation for model-informed precision dosing in oncology.
CPT Pharmacometrics Syst Pharmacol
; 10(3): 241-254, 2021 03.
Article
en En
| MEDLINE
| ID: mdl-33470053
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Paclitaxel
/
Efectos Colaterales y Reacciones Adversas Relacionados con Medicamentos
/
Biomarcadores Farmacológicos
/
Aprendizaje
/
Neutropenia
Tipo de estudio:
Prognostic_studies
Aspecto:
Patient_preference
Límite:
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Idioma:
En
Revista:
CPT Pharmacometrics Syst Pharmacol
Año:
2021
Tipo del documento:
Article
País de afiliación:
Alemania
Pais de publicación:
Estados Unidos