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A new mathematical model could help evaluate different approaches to treating metastatic cancer
A Rochester Institute of Technology scientist helped develop a new mathematical model that could help doctors and patients evaluate different approaches to treating metastatic cancer. Assistant Professor Nourridine Siewe of RIT's School of Mathematical Sciences is the lead author of a paper published in the Journal of Theoretical Biology describing the new method. In recent years, so-called immune checkpoint inhibitors have helped advance the treatment of many metastatic cancers, but the treatment only benefits a limited percentage of patients due to immunosuppression in the tumor microenvironment. Combining immune checkpoint inhibitors with other medications such as...
A new mathematical model could help evaluate different approaches to treating metastatic cancer
A Rochester Institute of Technology scientist helped develop a new mathematical model that could help doctors and patients evaluate different approaches to treating metastatic cancer. Assistant Professor Nourridine Siewe of RIT's School of Mathematical Sciences is the lead author of a paper published in the Journal of Theoretical Biology describing the new method.
In recent years, so-called immune checkpoint inhibitors have helped advance the treatment of many metastatic cancers, but the treatment only benefits a limited percentage of patients due to immunosuppression in the tumor microenvironment. Combining immune checkpoint inhibitors with other drugs such as anti-PD-1 and anti-CSF-1 could help extend the treatment's benefits to more patients, but factors such as toxicity also need to be taken into account.
Siewe and his colleague at Ohio State University developed a model that shows the interactions between immune cells and cancer. The model shows how the use of different drugs in different amounts and at different times affects the tumor volume and patient toxicity. Siewe said he hopes this can be used by the medical field to find the best opportunities for clinical trials.
Conducting clinical trials is very expensive, and this model can help clinicians decide which approaches offer the most favorable conditions. Our simulations examined the trade-offs between reducing tumor volume and maintaining toxicity at acceptable levels, and we found that the best strategy is to administer anti-CSF-1 in larger quantities as early as possible.”
Nourridine Siewe, assistant professor, School of Mathematical Sciences at RIT
Siewe said he hopes to work more with researchers in the medical field in the future to develop similar models to evaluate other types of treatment. He also envisions the mathematical model being presented in the form of an app that allows patients and physicians to visualize how changing treatment methods can affect tumor volume and toxicity levels.
Source:
Rochester Institute of Technology
Reference:
Siewe, N. & Friedman, A., (2022) Cancer therapy with immune checkpoint inhibitor and CSF-1 blockade: A mathematical model. Journal of Theoretical Biology. doi.org/10.1016/j.jtbi.2022.111297.
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