dc.contributor.author
Maier, Corinna
dc.contributor.author
Hartung, Niklas
dc.contributor.author
Kloft, Charlotte
dc.contributor.author
Huisinga, Wilhelm
dc.contributor.author
de Wiljes, Jana
dc.date.accessioned
2021-04-30T13:00:28Z
dc.date.available
2021-04-30T13:00:28Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/30605
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-30344
dc.description.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.
en
dc.format.extent
14 Seiten
dc.rights.uri
https://creativecommons.org/licenses/by-nc/4.0/
dc.subject
precision dosing
en
dc.subject
therapeutic drug
en
dc.subject.ddc
500 Naturwissenschaften und Mathematik::570 Biowissenschaften; Biologie::570 Biowissenschaften; Biologie
dc.title
Reinforcement learning and Bayesian data assimilation for model‐informed precision dosing in oncology
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.doi
10.1002/psp4.12588
dcterms.bibliographicCitation.journaltitle
CPT: Pharmacometrics & Systems Pharmacology
dcterms.bibliographicCitation.number
3
dcterms.bibliographicCitation.pagestart
241
dcterms.bibliographicCitation.pageend
254
dcterms.bibliographicCitation.volume
10
dcterms.bibliographicCitation.url
https://doi.org/10.1002/psp4.12588
refubium.affiliation
Biologie, Chemie, Pharmazie
refubium.affiliation.other
Institut für Pharmazie
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.isPartOf.eissn
2163-8306
refubium.resourceType.provider
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