ArticleCPT: pharmacometrics & systems pharmacology2020
Bayesian Data Assimilation to Support Informed Decision Making in Individualized Chemotherapy.
Article in CPT: pharmacometrics & systems pharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed, 37 citations in OpenAlex.
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- Uncertainty undermines the validity of antimicrobial pharmacodynamics.Journal of pharmacokinetics and pharmacodynamics · 2026Article
- Model-informed identification of optimised dosing strategies for meropenem in critically ill patients receiving SLEDD: an observational study.Infection · 2025Observational
- Recommended approaches for integration of population pharmacokinetic modelling with precision dosing in clinical practice.British journal of clinical pharmacology · 2025Review
- State of the art of real-life concentration monitoring of rifampicin and its implementation contextualized in resource-limited settings: the Tanzanian case.JAC-antimicrobial resistance · 2024Review
- Article
- Generation and application of avatars in pharmacometric modelling.Journal of pharmacokinetics and pharmacodynamics · 2023Article
- 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
- Simulating clinical trials for model-informed precision dosing: using warfarin treatment as a use case.Frontiers in pharmacology · 2023Article
- Model Informed Precision Dosing Tool Forecasts Trough Infliximab and Associates with Disease Status and Tumor Necrosis Factor-Alpha Levels of Inflammatory Bowel Diseases.Journal of clinical medicine · 2022Article
- A Pharmacometric Model to Predict Chemotherapy-Induced Myelosuppression and Associated Risk Factors in Non-Small Cell Lung Cancer.Pharmaceutics · 2022Article
- Free and Open-Source Posologyr Software for Bayesian Dose Individualization: An Extensive Validation on Simulated Data.Pharmaceutics · 2022Article
- A continued learning approach for model-informed precision dosing: Updating models in clinical practice.CPT: pharmacometrics & systems pharmacology · 2022Article
- Development and Validation of Open-Source R Package HMCtdm for Therapeutic Drug Monitoring.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Easy and reliable maximum a posteriori Bayesian estimation of pharmacokinetic parameters with the open-source R package mapbayr.CPT: pharmacometrics & systems pharmacology · 2021Article
- The Use of Translational Modelling and Simulation to Develop Immunomodulatory Therapy as an Adjunct to Antibiotic Treatment in the Context of Pneumonia.Pharmaceutics · 2021Article
- Reinforcement learning and Bayesian data assimilation for model-informed precision dosing in oncology.CPT: pharmacometrics & systems pharmacology · 2021Article
- Optimizing Predictive Performance of Bayesian Forecasting for Vancomycin Concentration in Intensive Care Patients.Pharmaceutical research · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An essential component of therapeutic drug/biomarker monitoring (TDM) is to combine patient data with prior knowledge for model-based predictions of therapy outcomes. Current Bayesian forecasting tools typically rely only on the most probable model parameters (maximum a posteriori (MAP) estimate). This MAP-based approach, however, does neither necessarily predict the most probable outcome nor does it quantify the risks of treatment inefficacy or toxicity. Bayesian data assimilation (DA) methods overcome these limitations by providing a comprehensive uncertainty quantification. We compare DA methods with MAP-based approaches and show how probabilistic statements about key markers related to chemotherapy-induced neutropenia can be leveraged for more informative decision support in individualized chemotherapy. Sequential Bayesian DA proved to be most computationally efficient for handling interoccasion variability and integrating TDM data. For new digital monitoring devices enabling more frequent data collection, these features will be of critical importance to improve patient care decisions in various therapeutic areas.
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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.