A continued learning approach for model‐informed precision dosing: Updating models in clinical practice
Abstract 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 p...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-02-01
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| Series: | CPT: Pharmacometrics & Systems Pharmacology |
| Online Access: | https://doi.org/10.1002/psp4.12745 |
| _version_ | 1819278773815607296 |
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| author | Corinna Maier Jana deWiljes Niklas Hartung Charlotte Kloft Wilhelm Huisinga |
| author_facet | Corinna Maier Jana deWiljes Niklas Hartung Charlotte Kloft Wilhelm Huisinga |
| author_sort | Corinna Maier |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-24T00:17:20Z |
| format | Article |
| id | doaj.art-e104c0356fa34eb5bd47df96020dae8b |
| institution | Directory Open Access Journal |
| issn | 2163-8306 |
| language | English |
| last_indexed | 2024-12-24T00:17:20Z |
| publishDate | 2022-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | CPT: Pharmacometrics & Systems Pharmacology |
| spelling | doaj.art-e104c0356fa34eb5bd47df96020dae8b2022-12-21T17:24:42ZengWileyCPT: Pharmacometrics & Systems Pharmacology2163-83062022-02-0111218519810.1002/psp4.12745A continued learning approach for model‐informed precision dosing: Updating models in clinical practiceCorinna Maier0Jana deWiljes1Niklas Hartung2Charlotte Kloft3Wilhelm Huisinga4Institute of Mathematics University of Potsdam Potsdam GermanyInstitute of Mathematics University of Potsdam Potsdam GermanyInstitute of Mathematics University of Potsdam Potsdam GermanyDepartment of Clinical Pharmacy and Biochemistry Institute of Pharmacy Freie Universität Berlin Berlin GermanyInstitute of Mathematics University of Potsdam Potsdam GermanyAbstract 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.https://doi.org/10.1002/psp4.12745 |
| spellingShingle | Corinna Maier Jana deWiljes Niklas Hartung Charlotte Kloft Wilhelm Huisinga A continued learning approach for model‐informed precision dosing: Updating models in clinical practice CPT: Pharmacometrics & Systems Pharmacology |
| 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 |
| url | https://doi.org/10.1002/psp4.12745 |
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