Improving cancer treatments via dynamical biophysical models
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Published on: December 1, 2023
M Kuznetsov1, J Clairambault2, V Volpert3
1P.N. Lebedev Physical Institute of the Russian Academy of Sciences, 53 Leninskiy Prospekt, Moscow, 119991, Russian Federation; Peoples' Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya St, Moscow, 117198, Russian Federation.
Abstract:
Despite significant advances in oncological research, cancer nowadays remains one of the main causes of mortality and morbidity worldwide. New treatment techniques, as a rule, have limited efficacy, target only a narrow range of oncological diseases, and have limited availability to the general public due their high cost. An important goal in oncology is thus the modification of the types of antitumor therapy and their combinations, that are already introduced into clinical practice, with the goal of increasing the overall treatment efficacy. One option to achieve this goal is optimization of the schedules of drugs administration or performing other medical actions. Several factors complicate such tasks: the adverse effects of treatments on healthy cell populations, which must be kept tolerable; the emergence of drug resistance due to the intrinsic plasticity of heterogeneous cancer cell populations; the interplay between different types of therapies administered simultaneously. Mathematical modeling, in which a tumor and its microenvironment are considered as a single complex system, can address this complexity and can indicate potentially effective protocols, that would require experimental verification. In this review, we consider classical methods, current trends and future prospects in the field of mathematical modeling of tumor growth and treatment. In particular, methods of treatment optimization are discussed with several examples of specific problems related to different types of treatment.
Insights
Mathematical modeling optimizes cancer treatment by analyzing tumor growth and drug resistance. This approach aims to improve therapy efficacy and reduce side effects for better patient outcomes.
Area of Science:
- Oncology
- Mathematical Biology
- Computational Science
Background:
- Cancer remains a leading cause of death globally, with current treatments often limited in efficacy, scope, and accessibility.
- Developing novel antitumor therapies and optimizing existing ones is crucial for improving patient outcomes.
- Challenges include treatment side effects, drug resistance, and the complexity of combining therapies.
Purpose of the Study:
- To review classical and current methods in mathematical modeling for tumor growth and treatment.
- To explore future prospects of mathematical modeling in oncology.
- To discuss treatment optimization strategies using mathematical models.
Main Methods:
- Review of existing literature on mathematical modeling in cancer research.
- Analysis of classical and contemporary modeling techniques.
- Discussion of treatment optimization through schedule adjustments and therapy combinations.
Main Results:
- Mathematical modeling offers a framework to address the complexity of tumor growth and treatment interactions.
- Modeling can identify potentially effective treatment protocols for experimental validation.
- Optimization of drug administration schedules and therapy combinations is a key application.
Conclusions:
- Mathematical modeling is a powerful tool for understanding and optimizing cancer treatment strategies.
- Integrating mathematical models into clinical practice can enhance therapeutic efficacy and patient survival.
- Further research in mathematical oncology holds promise for personalized and more effective cancer therapies.
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