ArticleCPT: pharmacometrics & systems pharmacology2021
Reinforcement learning and Bayesian data assimilation for model-informed precision dosing in oncology.
Article in CPT: pharmacometrics & systems pharmacology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 4 citations in OpenAlex.
- Multi-model averaging improves the performance of model-guided infliximab dosing in patients with inflammatory bowel diseases.CPT: pharmacometrics & systems pharmacology · 2022Trial
- Artificial Intelligence in Particle Therapy Treatment Planning: Current Advances and Future Directions.International journal of particle therapy · 2026Article
- Nature meets machine: the AI renaissance in natural product drug discovery.Natural products and bioprospecting · 2026Review
- Uncertainty undermines the validity of antimicrobial pharmacodynamics.Journal of pharmacokinetics and pharmacodynamics · 2026Article
- Model-Informed Deep Q-Networks to Guide Infliximab Dosing in Pediatric Crohn's Disease.Clinical pharmacology and therapeutics · 2026Article
- Delay-aware chemotherapy dosing via online critic learning.Scientific reports · 2025Article
- The Three Musketeers in Cancer Therapy: Pharmacokinetics, Pharmacodynamics and Personalised Approach.Journal of personalized medicine · 2025Review
- Precision Dosing in Presence of Multiobjective Therapies by Integrating Reinforcement Learning and PK-PD Models: Application to Givinostat Treatment of Polycythemia Vera.CPT: pharmacometrics & systems pharmacology · 2025Article
- Review
- Machine Learning Methods for Precision Dosing in Anticancer Drug Therapy: A Scoping Review.Clinical pharmacokinetics · 2024Article
- Personalized Dosing of Medicines for Children: A Primer on Pediatric Pharmacometrics for Clinicians.Paediatric drugs · 2024Review
- Mathematical Model-Driven Deep Learning Enables Personalized Adaptive Therapy.Cancer research · 2024Article
- Bridging the Worlds of Pharmacometrics and Machine Learning.Clinical pharmacokinetics · 2023Review
- Towards Model-Informed Precision Dosing of Voriconazole: Challenging Published Voriconazole Nonlinear Mixed-Effects Models with Real-World Clinical Data.Clinical pharmacokinetics · 2023Article
- Precision Oncology by Point-of-Care Therapeutic Drug Monitoring and Dosage Adjustment of Conventional Cytotoxic Chemotherapies: A Perspective.Pharmaceutics · 2023Review
- Model-informed precision dosing of teicoplanin for the rapid achievement of the target area under the concentration-time curve: A simulation study.Clinical and translational science · 2023Article
- Translating Data Science Results into Precision Oncology Decisions: A Mini Review.Journal of clinical medicine · 2023Review
- Simulating clinical trials for model-informed precision dosing: using warfarin treatment as a use case.Frontiers in pharmacology · 2023Article
- Model enhanced reinforcement learning to enable precision dosing: A theoretical case study with dosing of propofol.CPT: pharmacometrics & systems pharmacology · 2022Article
- Model-Based Prediction of Irinotecan-Induced Grade 4 Neutropenia in Advanced Cancer Patients: Influence of Demographic and Clinical Factors.Clinical pharmacology and therapeutics · 2022Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.