bioRxivCancer BiologyPreprint
Preliminary in silico experiments: Towards new cancer treatments?
Abstract
We present some "in silico" experiments to design combined chemo- and immunotherapy treatment schedules. We introduce a new framework by combining flatness-based control along with model-free control. The flatness property of the used mathematical model yields straightforward reference trajectories. They provide us with the nominal open-loop control inputs. Closing the loop via model-free control allows to deal with the uncertainties on the injected drug doses. Several numerical simulations illustrating different case studies are displayed. We show in particular that the considered health indicators are driven to the safe region, even for critical initial conditions. Furthermore, in some specific cases there is no need to inject chemotherapeutic agents.
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- The Goldilocks Window of Personalized Chemotherapy: An Immune Perspective
D. S. Park, M. Robertson-Tessi, … A. R. Anderson·bioRxiv·Dec 13, 2018
- Gaming the cancer-immunity cycle by synchronizing the dose schedules
Saeedeh Mahmoodifar, K. Stuckey, Paul K. Newton·Proceedings of the National Academy of SciencesPeer-reviewed·Aug 8, 2025
- Reinforcement learning derived chemotherapeutic schedules for robust patient-specific therapy
B. Eastman, M. Przedborski, M. Kohandel·bioRxiv·Apr 23, 2021
- Dynamic optimization of chemo-immunotherapy sequencing reveals phenotype-dependent regimens across tumor immune microenvironments
Kai Gong, Tong Li Lu, … Xinggao Liu·bioRxiv·Nov 26, 2025
- What does not kill a tumour may make it stronger: in silico Insights into Chemotherapeutic Drug Resistance
S. Hamis, P. Nithiarasu, G. Powathil·bioRxiv·Apr 13, 2018
- A Mathematical Framework for Comparison of Intermittent and Continuous Adaptive Chemotherapy Dosing in Cancer
Cordelia D. McGehee, Yoichiro Mori A·npj Systems Biology and ApplicationsPeer-reviewed·Nov 29, 2024