Volume 2, Issue 1, Journal of Numerical Simulations in Physics and Mathematics
Volume 2, Issue 1, 2026
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Journal of Numerical Simulations in Physics and Mathematics, Volume 2, Issue 1, 2026: 1-8

Open Access | Research Article | 07 March 2026
Establishment and Simulation of Adaptive Strategy for Cancer Therapy Under Multi-Drug Conditions
1 School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650031, China
* Corresponding Author: Wanqin Wu, [email protected]
ARK: ark:/57805/jnspm.2025.161987
Received: 05 December 2025, Accepted: 06 January 2026, Published: 07 March 2026  
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.

Graphical Abstract
Establishment and Simulation of Adaptive Strategy for Cancer Therapy Under Multi-Drug Conditions

Keywords
computer simulation
multi-drug cancer therapy
adaptive strategy

Data Availability Statement
Data will be made available on request.

Funding
This work was supported in part by the National Natural Science Foundation of China under Grant 11361104 and Grant 12261104; in part by the Youth Talent Program of Xingdian Talent Support Plan under Grant XDYC-QNRC 2022-0514; in part by the Yunnan Provincial Basic Research Program Project under Grant 202301AT070016 and Grant 202401AT070036; in part by the Yunnan Province International Joint Laboratory for Intelligent Integration and Application of Ethnic Multilingualism under Grant 202403AP140014.

Conflicts of Interest
The authors declare no conflicts of interest.

AI Use Statement
The authors declare that Doubao 1.8 was used for cross-lingual translation and grammatical correction during the preparation of this manuscript. The authors reviewed and edited the output as necessary and take full responsibility for the final content of the manuscript.

Ethical Approval and Consent to Participate
Not applicable. This is a purely computational and mathematical modeling study with no involvement of human or animal subjects; therefore, ethical approval is not required.

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Cite This Article
APA Style
Li, H., Wu, W., & Tan, X. (2026). Establishment and Simulation of Adaptive Strategy for Cancer Therapy Under Multi-Drug Conditions. Journal of Numerical Simulations in Physics and Mathematics, 2(1), 1–8. https://doi.org/10.62762/JNSPM.2025.161987
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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  - 
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@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}
}

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CC BY Copyright © 2026 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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