Evolution of resistance to targeted anti-cancer therapies during continuous and pulsed administration strategies

Area of Science:

• Oncology
• Mathematical Biology
• Cancer Therapeutics

Background:

• Targeted small molecule therapies have transformed cancer treatment by inhibiting specific oncogenic pathways.
• Therapeutic efficacy is often limited by the development of acquired drug resistance in tumor cells.
• Optimizing drug administration strategies is crucial for minimizing resistance and improving long-term patient outcomes.

Purpose of the Study:

• To investigate optimal drug dosing schedules for preventing or delaying the emergence of anti-cancer drug resistance.
• To develop a mathematical framework for analyzing tumor cell population dynamics under therapeutic pressure.
• To identify treatment protocols that minimize resistance risk while managing drug toxicity.

Main Methods:

• Design and analysis of a stochastic mathematical model for tumor cell evolution during therapy.
• Calculation of resistance emergence probability under various dosing strategies.
• Optimization of treatment protocols using mathematical modeling, incorporating toxicity constraints.

Main Results:

• Identified specific drug dosing schedules that significantly reduce the probability of resistance.
• Demonstrated the model's ability to predict resistance dynamics based on single (epi)genetic alterations.
• Quantified the trade-offs between minimizing resistance and managing drug toxicity.

Conclusions:

• Optimal drug administration schedules can effectively delay or prevent acquired resistance in cancer therapy.
• Mathematical modeling provides a powerful tool for designing personalized and effective cancer treatment strategies.
• The developed methodology is applicable across various cancer types and therapeutic interventions.