A continued learning approach for model-informed precision dosing
- Model-informed precision dosing (MIPD) is a quantitative dosing framework that combines prior knowledge on the drug-disease-patient system with patient data from therapeutic drug/ biomarker monitoring (TDM) to support individualized dosing in ongoing treatment. Structural models and prior parameter distributions used in MIPD approaches typically build on prior clinical trials that involve only a limited number of patients selected according to some exclusion/inclusion criteria. Compared to the prior clinical trial population, the patient population in clinical practice can be expected to also include altered behavior and/or increased interindividual variability, the extent of which, however, is typically unknown. Here, we address the question of how to adapt and refine models on the level of the model parameters to better reflect this real-world diversity. We propose an approach for continued learning across patients during MIPD using a sequential hierarchical Bayesian framework. The approach builds on two stages to separate theModel-informed precision dosing (MIPD) is a quantitative dosing framework that combines prior knowledge on the drug-disease-patient system with patient data from therapeutic drug/ biomarker monitoring (TDM) to support individualized dosing in ongoing treatment. Structural models and prior parameter distributions used in MIPD approaches typically build on prior clinical trials that involve only a limited number of patients selected according to some exclusion/inclusion criteria. Compared to the prior clinical trial population, the patient population in clinical practice can be expected to also include altered behavior and/or increased interindividual variability, the extent of which, however, is typically unknown. Here, we address the question of how to adapt and refine models on the level of the model parameters to better reflect this real-world diversity. We propose an approach for continued learning across patients during MIPD using a sequential hierarchical Bayesian framework. The approach builds on two stages to separate the update of the individual patient parameters from updating the population parameters. Consequently, it enables continued learning across hospitals or study centers, because only summary patient data (on the level of model parameters) need to be shared, but no individual TDM data. We illustrate this continued learning approach with neutrophil-guided dosing of paclitaxel. The present study constitutes an important step toward building confidence in MIPD and eventually establishing MIPD increasingly in everyday therapeutic use.…
Author details: | Corinna Sabrina MaierORCiDGND, Jana de WiljesORCiDGND, Niklas HartungORCiD, Charlotte KloftORCiDGND, Wilhelm HuisingaORCiDGND |
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DOI: | https://doi.org/10.1002/psp4.12745 |
ISSN: | 2163-8306 |
Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/34779144 |
Title of parent work (English): | CPT: pharmacometrics & systems pharmacology |
Subtitle (English): | Updating models in clinical practice |
Publisher: | London |
Place of publishing: | Nature Publ. Group |
Publication type: | Article |
Language: | English |
Date of first publication: | 2021/11/15 |
Publication year: | 2022 |
Release date: | 2023/06/23 |
Volume: | 11 |
Issue: | 2 |
Number of pages: | 14 |
First page: | 185 |
Last Page: | 198 |
Funding institution: | Graduate Research Training Program PharMetrX: Pharmacometrics & Computational Disease Modelling, Berlin/Potsdam, Germany; Deutsche Forschungsgemeinschaft (DFG)German Research Foundation (DFG) [SFB1294 - 318763901] |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Mathematik |
DDC classification: | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
Peer review: | Referiert |
Publishing method: | Open Access / Gold Open-Access |
License (German): | CC-BY-NC - Namensnennung, nicht kommerziell 4.0 International |