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dc.contributor.author
Houy, Nicolas
dc.contributor.author
Le Grand, François Louis
dc.date.accessioned
2019-01-21T09:36:11Z
dc.date.available
2019-01-21T09:36:11Z
dc.date.issued
2018-06-26
dc.identifier.issn
1932-6203
dc.identifier.other
10.1371/journal.pone.0199076
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/317757
dc.identifier.doi
10.3929/ethz-b-000274405
dc.description.abstract
We determine an optimal protocol for temozolomide using population variability and dynamic optimization techniques inspired by artificial intelligence. We use a Pharmacokinetics/Pharmacodynamics (PK/PD) model based on Faivre and coauthors (Faivre, et al., 2013) for the pharmacokinetics of temozolomide, as well as the pharmacodynamics of its efficacy. For toxicity, which is measured by the nadir of the normalized absolute neutrophil count, we formalize the myelosuppression effect of temozolomide with the physiological model of Panetta and coauthors (Panetta, et al., 2003). We apply the model to a population with variability as given in Panetta and coauthors (Panetta, et al., 2003). Our optimization algorithm is a variant in the class of Monte-Carlo tree search algorithms. We do not impose periodicity constraint on our solution. We set the objective of tumor size minimization while not allowing more severe toxicity levels than the standard Maximum Tolerated Dose (MTD) regimen. The protocol we propose achieves higher efficacy in the sense that –compared to the usual MTD regimen– it divides the tumor size by approximately 7.66 after 336 days –the 95% confidence interval being [7.36–7.97]. The toxicity is similar to MTD. Overall, our protocol, obtained with a very flexible method, gives significant results for the present case of temozolomide and calls for further research mixing operational research or artificial intelligence and clinical research in oncology.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
PLOS
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Optimal dynamic regimens with artificial intelligence: The case of temozolomide
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2018-06-26
ethz.journal.title
PLoS ONE
ethz.journal.volume
13
en_US
ethz.journal.issue
6
en_US
ethz.journal.abbreviated
PLoS ONE
ethz.pages.start
0199076
en_US
ethz.size
15 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
San Francisco, CA
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::03877 - Bommier, Antoine / Bommier, Antoine
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::03877 - Bommier, Antoine / Bommier, Antoine
en_US
ethz.date.deposited
2018-07-06T16:13:14Z
ethz.source
SCOPUS
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2019-01-21T09:36:20Z
ethz.rosetta.lastUpdated
2024-02-02T07:00:27Z
ethz.rosetta.versionExported
true
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/274405
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/314276
ethz.COinS
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