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Publicaciones

Drug dosing for cancer therapy: A stochastic model predictive control perspective.

Authors

Hernández-Rivera, Andrés , VELARDE RUEDA, PABLO ANIBAL, Zafra-Cabeza, Ascensión , Maestre, José M

External publication

No

Means

J. Theor. Biol.

Scope

Article

Nature

Científica

JCR Quartile

✗

SJR Quartile

✗

Web

https://www.scopus.com/inward/record.uri?eid=2-s2.0-105014971417&doi=10.1016%2Fj.jtbi.2025.112255&partnerID=40&md5=59fc45304f5fcd21f9c5fb6c924eb9d1

Publication date

07/12/2025

ISI

001567397500003

Scopus Id

2-s2.0-105014971417

DOI

10.1016/j.jtbi.2025.112255

Abstract

Stochastic Model Predictive Control (SMPC) is an effective decision-making method in applications where uncertainties play a significant role. This work introduces a non-linear formulation of SMPC specifically designed for cancer therapy. The proposed method considers the stochastic nature of tumor growth, non-linear dynamics, and a potential side effect of the treatment. Through one-year simulations, the results showcase the effectiveness of this strategy in controlling drug dosing.

Keywords

Cancer; Chemotherapy; Model predictive control; Non-linear control systems; Stochastic processes

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