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

Abstract : The 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'.
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https://hal.inria.fr/hal-03147084
Contributor : Sebastien Benzekry Connect in order to contact the contributor
Submitted on : Friday, February 19, 2021 - 3:34:11 PM
Last modification on : Saturday, January 29, 2022 - 3:08:02 AM
Long-term archiving on: : Thursday, May 20, 2021 - 7:44:22 PM

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Joseph Ciccolini, Dominique Barbolosi, Nicolas André, Fabrice Barlesi, Sébastien Benzekry. Mechanistic Learning for Combinatorial Strategies With Immuno-oncology Drugs: Can Model-Informed Designs Help Investigators?. JCO precision oncology, American Society of Clinical Oncology, 2020, 108 (4), pp.486-491. ⟨10.1200/PO.19.00381⟩. ⟨hal-03147084⟩

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