[Mathematical and computational methods to model chemotherapy- induced resistance].

Area of Science:

• Oncology
• Mathematical Biology
• Computational Medicine

Background:

• Chemotherapy is a cornerstone of cancer treatment, but tumor progression despite treatment remains a significant challenge.
• Anticancer therapy resistance limits the efficacy of conventional chemotherapy protocols.
• Understanding tumor growth dynamics and treatment response is crucial for improving patient outcomes.

Purpose of the Study:

• To summarize mathematical approaches for modeling tumor growth and response to chemotherapy.
• To focus on the critical issue of anticancer therapy resistance.
• To introduce a novel therapy optimization concept aimed at enhancing traditional chemotherapy.

Main Methods:

• Review and synthesis of existing mathematical modeling techniques for cancer treatment.
• Application of computational methods to analyze tumor growth and drug resistance.
• Development of a new conceptual framework for chemotherapy optimization.

Main Results:

• Identification of key mathematical strategies for modeling tumor-chemotherapy interactions.
• Highlighting the importance of incorporating therapy resistance into predictive models.
• Preliminary results from the Cybermedical Competence Center on a new optimization approach.

Conclusions:

• Mathematical modeling offers powerful tools to understand and combat chemotherapy resistance.
• A new therapy optimization concept shows promise for improving traditional chemotherapy efficacy.
• Further research and development at institutions like Óbuda University are vital for advancing cancer treatment.