TY  - JOUR
AU  - Li, Hanyin
AU  - Wu, Wanqin
AU  - Tan, Xuewen
PY  - 2026
DA  - 2026/03/07
TI  - Establishment and Simulation of Adaptive Strategy for Cancer Therapy Under Multi-Drug Conditions
JO  - Journal of Numerical Simulations in Physics and Mathematics
T2  - Journal of Numerical Simulations in Physics and Mathematics
JF  - Journal of Numerical Simulations in Physics and Mathematics
VL  - 2
IS  - 1
SP  - 1
EP  - 8
DO  - 10.62762/JNSPM.2025.161987
UR  - https://www.icck.org/article/abs/JNSPM.2025.161987
KW  - computer simulation
KW  - multi-drug cancer therapy
KW  - adaptive strategy
AB  - Previous research has focused on formulating cancer treatment strategies within a single-drug framework, which clearly fails to effectively address the practical needs of combination drug therapy in clinical settings. Therefore, this study develops a multi-drug cancer treatment model and conducts strategy design and simulation investigations under hypothetical patient-specific parameters for dual-drug regimens. Based on the previously proposed adaptive threshold strategy and several novel strategies integrating threshold-based and sequential administration patterns, and conducted a parameter search and optimization of upper/lower thresholds was performed to explore the performance improvement of strategies resulting from threshold adjustments in the multi-drug framework. Furthermore, a threshold decay coefficient was introduced to facilitate further optimization and enhance strategy performance. Experimental results demonstrate that the newly proposed multi-drug cancer treatment strategies outperform the extended traditional strategy. Parameter optimization of both thresholds and the threshold decay coefficient improved the survival benefit of the strategies to varying degrees. This indicates that, compared with the traditional parallel multi-drug treatment model, strategies incorporating sequential drug administration characteristics significantly exploit the synergistic effects among drugs, yielding superior therapeutic outcomes.
SN  - 3068-9082
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Li2026Establishm,
  author = {Hanyin Li and Wanqin Wu and Xuewen Tan},
  title = {Establishment and Simulation of Adaptive Strategy for Cancer Therapy Under Multi-Drug Conditions},
  journal = {Journal of Numerical Simulations in Physics and Mathematics},
  year = {2026},
  volume = {2},
  number = {1},
  pages = {1-8},
  doi = {10.62762/JNSPM.2025.161987},
  url = {https://www.icck.org/article/abs/JNSPM.2025.161987},
  abstract = {Previous research has focused on formulating cancer treatment strategies within a single-drug framework, which clearly fails to effectively address the practical needs of combination drug therapy in clinical settings. Therefore, this study develops a multi-drug cancer treatment model and conducts strategy design and simulation investigations under hypothetical patient-specific parameters for dual-drug regimens. Based on the previously proposed adaptive threshold strategy and several novel strategies integrating threshold-based and sequential administration patterns, and conducted a parameter search and optimization of upper/lower thresholds was performed to explore the performance improvement of strategies resulting from threshold adjustments in the multi-drug framework. Furthermore, a threshold decay coefficient was introduced to facilitate further optimization and enhance strategy performance. Experimental results demonstrate that the newly proposed multi-drug cancer treatment strategies outperform the extended traditional strategy. Parameter optimization of both thresholds and the threshold decay coefficient improved the survival benefit of the strategies to varying degrees. This indicates that, compared with the traditional parallel multi-drug treatment model, strategies incorporating sequential drug administration characteristics significantly exploit the synergistic effects among drugs, yielding superior therapeutic outcomes.},
  keywords = {computer simulation, multi-drug cancer therapy, adaptive strategy},
  issn = {3068-9082},
  publisher = {Institute of Central Computation and Knowledge}
}