Optimizing cancer therapy for individuals based on tumor-immune-drug system interaction

Xin Chen; Tengda Li; Will Cao; Xin Chen; Tengda Li; Will Cao

doi:10.3934/mbe.2023781

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 10: 17589-17607. doi: 10.3934/mbe.2023781

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Research article Special Issues

Optimizing cancer therapy for individuals based on tumor-immune-drug system interaction

Xin Chen ¹,
Tengda Li ^{2
,
,},
Will Cao ^{3
,
,}

1.
University of California, Los Angeles College of Letters and Science, Los Angeles, California, United States
2.
Wenzhou Medical University School of Laboratory Medicine and Life Sciences, Wenzhou, Zhejiang, China
3.
Duke University Department of Biomedical Engineering, Durham, North Carolina, United States

Academic Editor: Gianpaolo Ruocco

Received: 15 May 2023 Revised: 24 July 2023 Accepted: 15 August 2023 Published: 14 September 2023

Background and aim
Chemotherapy is a crucial component of cancer therapy, albeit with significant side effects. Chemotherapy either damages or inhibits the immune system; therefore, its efficacy varies according to the patient's immune state. Currently, there is no efficient model that incorporates tumor-immune-drug (TID) interactions to guide clinical medication strategies. In this study, we compared five different types of existing TID models with the aim to integrate them into a single, comprehensive model; our goal was to accurately reflect the reality of TID interactions to guide personalized cancer therapy.
Methods
We studied four different drug treatment profiles: direct function, normal distribution function, sine function, and trapezoid function. We developed a platform capable of plotting all combinations of parameter sets and their corresponding treatment efficiency scores. Subsequently, we generated 10,000 random parameter combinations for an individual case and plotted two polygon graphs using a seismic colormap to depict efficacy of treatment. Then, we developed a platform providing treatment suggestions for all stages of tumors and varying levels of self-immunity. We created polygons demonstrating successful treatments according to parameters related to tumor and immune status.
Results
The trapezoid drug treatment function achieved the best inhibitory effect on the tumor cell density. The treatment can be optimized with a high score indicating that the drug delivery interval had exceeded a specific value. More efficient parameter combinations existed when the immunity was strong compared to when it was weak, thus indicating that increasing the patient's self-immunity can make treatment much more effective.
Conclusions
In summary, we created a comprehensive model that can provide quantitative recommendations for a gentle, yet efficient, treatment customized according to the individual's tumor and immune system characteristics.
- dynamic analysis,
- immune response,
- mathematical modeling,
- personalized cancer therapy,
- tumor-immune-drug system interaction
Citation: Xin Chen, Tengda Li, Will Cao. Optimizing cancer therapy for individuals based on tumor-immune-drug system interaction[J]. Mathematical Biosciences and Engineering, 2023, 20(10): 17589-17607. doi: 10.3934/mbe.2023781

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Abstract

Background and aim

Chemotherapy is a crucial component of cancer therapy, albeit with significant side effects. Chemotherapy either damages or inhibits the immune system; therefore, its efficacy varies according to the patient's immune state. Currently, there is no efficient model that incorporates tumor-immune-drug (TID) interactions to guide clinical medication strategies. In this study, we compared five different types of existing TID models with the aim to integrate them into a single, comprehensive model; our goal was to accurately reflect the reality of TID interactions to guide personalized cancer therapy.

Methods

We studied four different drug treatment profiles: direct function, normal distribution function, sine function, and trapezoid function. We developed a platform capable of plotting all combinations of parameter sets and their corresponding treatment efficiency scores. Subsequently, we generated 10,000 random parameter combinations for an individual case and plotted two polygon graphs using a seismic colormap to depict efficacy of treatment. Then, we developed a platform providing treatment suggestions for all stages of tumors and varying levels of self-immunity. We created polygons demonstrating successful treatments according to parameters related to tumor and immune status.

Results

The trapezoid drug treatment function achieved the best inhibitory effect on the tumor cell density. The treatment can be optimized with a high score indicating that the drug delivery interval had exceeded a specific value. More efficient parameter combinations existed when the immunity was strong compared to when it was weak, thus indicating that increasing the patient's self-immunity can make treatment much more effective.

Conclusions

In summary, we created a comprehensive model that can provide quantitative recommendations for a gentle, yet efficient, treatment customized according to the individual's tumor and immune system characteristics.

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