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A continued learning approach for model‐informed precision dosing: Updating models in clinical practice

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...

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Autores principales: Maier, Corinna, de Wiljes, Jana, Hartung, Niklas, Kloft, Charlotte, Huisinga, Wilhelm
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8846635/
https://www.ncbi.nlm.nih.gov/pubmed/34779144
http://dx.doi.org/10.1002/psp4.12745
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author Maier, Corinna
de Wiljes, Jana
Hartung, Niklas
Kloft, Charlotte
Huisinga, Wilhelm
author_facet Maier, Corinna
de Wiljes, Jana
Hartung, Niklas
Kloft, Charlotte
Huisinga, Wilhelm
author_sort Maier, Corinna
collection PubMed
description 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 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.
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spelling pubmed-88466352022-02-25 A continued learning approach for model‐informed precision dosing: Updating models in clinical practice Maier, Corinna de Wiljes, Jana Hartung, Niklas Kloft, Charlotte Huisinga, Wilhelm CPT Pharmacometrics Syst Pharmacol Research 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 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. John Wiley and Sons Inc. 2021-12-27 2022-02 /pmc/articles/PMC8846635/ /pubmed/34779144 http://dx.doi.org/10.1002/psp4.12745 Text en © 2021 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Research
Maier, Corinna
de Wiljes, Jana
Hartung, Niklas
Kloft, Charlotte
Huisinga, Wilhelm
A continued learning approach for model‐informed precision dosing: Updating models in clinical practice
title A continued learning approach for model‐informed precision dosing: Updating models in clinical practice
title_full A continued learning approach for model‐informed precision dosing: Updating models in clinical practice
title_fullStr A continued learning approach for model‐informed precision dosing: Updating models in clinical practice
title_full_unstemmed A continued learning approach for model‐informed precision dosing: Updating models in clinical practice
title_short A continued learning approach for model‐informed precision dosing: Updating models in clinical practice
title_sort continued learning approach for model‐informed precision dosing: updating models in clinical practice
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8846635/
https://www.ncbi.nlm.nih.gov/pubmed/34779144
http://dx.doi.org/10.1002/psp4.12745
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