Drug dosing for cancer therapy: A stochastic model predictive control perspective
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.
Authors
- Ascensión Zafra‐Cabeza (ORCID: https://orcid.org/0000-0001-9505-8050)
- J. M. Maestre (ORCID: https://orcid.org/0000-0002-4968-6811)
- Pablo Velarde (ORCID: https://orcid.org/0000-0002-0503-829X)
- Andrés Hernández-Rivera (ORCID: https://orcid.org/0000-0002-8418-1333)
Institutions
- Universidad Loyola Andalucía (ES)
- Universidad Loyola (BO)
- Universidad de Sevilla (ES)
Publication Details
- Journal
- Journal of Theoretical Biology
- Published
- 2025-08-31
- DOI
- https://doi.org/10.1016/j.jtbi.2025.112255
- Primary Topic
- Mathematical Biology Tumor Growth
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ministerio de Ciencia e Innovación
- Agencia Estatal de Investigación