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Mechanistic Learning for Combinatorial Strategies With Immuno-oncology Drugs: Can Model-Informed Designs Help Investigators?

hal.structure.identifierSimulation and Modeling of Adaptive Response for Therapeutics in Cancer [SMARTc]
dc.contributor.authorCICCOLINI, Joseph
hal.structure.identifierSimulation and Modeling of Adaptive Response for Therapeutics in Cancer [SMARTc]
dc.contributor.authorBARBOLOSI, Dominique
hal.structure.identifierSimulation and Modeling of Adaptive Response for Therapeutics in Cancer [SMARTc]
hal.structure.identifierHôpital de la Timone [CHU - APHM] [TIMONE]
dc.contributor.authorANDRÉ, Nicolas
hal.structure.identifierInstitut Gustave Roussy [IGR]
hal.structure.identifierDirection de la recherche clinique [Gustave Roussy]
dc.contributor.authorBARLESI, Fabrice
hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorBENZEKRY, Sébastien
dc.date.accessioned2024-04-04T02:47:12Z
dc.date.available2024-04-04T02:47:12Z
dc.date.issued2020-09
dc.identifier.issn2473-4284
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/191622
dc.description.abstractEnThe amount of 'big' data generated in clinical oncology, whether from molecular, imaging, pharmacological or biological origin, brings novel challenges. To mine efficiently this source of information, mathematical models able to produce predictive algorithms and simulations are required, with applications for diagnosis, prognosis, drug development or prediction of the response to therapy. Such mathematical and computational constructs can be subdivided into two broad classes: biologically agnostic, statistical models using artificial intelligence techniques, and physiologically-based, mechanistic models. In this review, recent advances in the applications of such methods in clinical oncology are outlined. These include machine learning applied to big data (omics, imaging or electronic health records), pharmacometrics, quantitative systems pharmacology, tumor size kinetics, and metastasis modeling. Focus is set on studies with high potential of clinical translation, as well as applied to cancer immunotherapy. Perspectives are given in terms of combinations of the two approaches: 'mechanistic learning'.
dc.language.isoen
dc.publisherAmerican Society of Clinical Oncology
dc.title.enMechanistic Learning for Combinatorial Strategies With Immuno-oncology Drugs: Can Model-Informed Designs Help Investigators?
dc.typeArticle de revue
dc.identifier.doi10.1200/PO.19.00381
dc.subject.halInformatique [cs]/Modélisation et simulation
dc.subject.halSciences du Vivant [q-bio]/Cancer
dc.subject.halPhysique [physics]/Physique [physics]/Analyse de données, Statistiques et Probabilités [physics.data-an]
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologie
dc.subject.halSciences du Vivant [q-bio]/Sciences pharmaceutiques/Pharmacologie
dc.subject.halStatistiques [stat]/Applications [stat.AP]
bordeaux.journalJCO precision oncology
bordeaux.page486-491
bordeaux.volume108
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.issue4
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-03147084
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03147084v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=JCO%20precision%20oncology&rft.date=2020-09&rft.volume=108&rft.issue=4&rft.spage=486-491&rft.epage=486-491&rft.eissn=2473-4284&rft.issn=2473-4284&rft.au=CICCOLINI,%20Joseph&BARBOLOSI,%20Dominique&ANDR%C3%89,%20Nicolas&BARLESI,%20Fabrice&BENZEKRY,%20S%C3%A9bastien&rft.genre=article


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