CICCOLINI, Joseph; BARBOLOSI, Dominique; ANDRÉ, Nicolas; BARLESI, Fabrice; BENZEKRY, Sébastien

doi:10.1200/PO.19.00381

Métadonnées

Afficher la notice complète

Licence d’utilisation du document

CICCOLINI, Joseph
Simulation and Modeling of Adaptive Response for Therapeutics in Cancer [SMARTc]

BARBOLOSI, Dominique
Simulation and Modeling of Adaptive Response for Therapeutics in Cancer [SMARTc]

ANDRÉ, Nicolas
Simulation and Modeling of Adaptive Response for Therapeutics in Cancer [SMARTc]
Hôpital de la Timone [CHU - APHM] [TIMONE]

Langue

Article de revue

Ce document a été publié dans

JCO precision oncology. 2020-09, vol. 108, n° 4, p. 486-491

American Society of Clinical Oncology

Résumé en anglais

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'.< Réduire

Métadonnées

Partager cette publication !

Licence d’utilisation du document

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

Langue

Ce document a été publié dans

Résumé en anglais

URI

DOI

Origine

Unités de recherche