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Published on: April 30, 2021
Employing dynamical computational models for personalizing cancer immunotherapy
Zvia Agur1, Karin Halevi-Tobias1, Yuri Kogan1
1a Institute for Medical BioMathematics (IMBM) , Bene Ataroth , Israel.
Introduction:
Recently, cancer immunotherapy has shown considerable success, but due to the complexity of the immune-cancer interactions, clinical outcomes vary largely between patients. A possible approach to overcome this difficulty may be to develop new methodologies for personal predictions of therapy outcomes, by the integration of patient data with dynamical mathematical models of the drug-affected pathophysiological processes.
Areas Covered:
This review unfolds the story of mathematical modeling in cancer immunotherapy, and examines the feasibility of using these models for immunotherapy personalization. The reviewed studies suggest that response to immunotherapy can be improved by patient-specific regimens, which can be worked out by personalized mathematical models. The studies further indicate that personalized models can be constructed and validated relatively early in treatment.
Expert Opinion:
The suggested methodology has the potential to raise the overall efficacy of the developed immunotherapy. If implemented already during drug development it may increase the prospects of the technology being approved for clinical use. However, schedule personalization, per se, does not comply with the current, 'one size fits all,' paradigm of clinical trials. It is worthwhile considering adjustment of the current paradigm to involve personally tailored immunotherapy regimens.
Insights
Personalized mathematical models can predict cancer immunotherapy outcomes, improving treatment efficacy. Tailoring regimens to individual patients offers a promising approach beyond current clinical trial designs.
Area of Science:
- Oncology
- Immunology
- Mathematical Biology
Background:
- Cancer immunotherapy has shown success, but patient outcomes vary due to complex immune-cancer interactions.
- Personalized prediction of therapy outcomes is needed to overcome variability.
- Integrating patient data with dynamical mathematical models offers a potential solution.
Purpose of the Study:
- To review the role of mathematical modeling in cancer immunotherapy.
- To examine the feasibility of using these models for immunotherapy personalization.
- To explore the potential of personalized mathematical models in improving treatment outcomes.
Main Methods:
- Review of existing studies on mathematical modeling in cancer immunotherapy.
- Analysis of the application of personalized mathematical models for treatment regimen development.
- Assessment of the early construction and validation of personalized models.
Main Results:
- Mathematical models can be used to develop patient-specific immunotherapy regimens.
- Personalized models can be constructed and validated early in the treatment process.
- These models show potential for improving immunotherapy response.
Conclusions:
- Personalized mathematical modeling can enhance overall immunotherapy efficacy.
- Early implementation during drug development may improve clinical approval prospects.
- Adjusting clinical trial paradigms to include personalized regimens is recommended.
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