Optimizing Adaptive Therapy Based on the Reachability to Tumor Resistant Subpopulation.

Area of Science:

• Oncology
• Mathematical Biology
• Pharmacology

Background:

• Adaptive therapy aims to delay drug resistance by exploiting tumor cell self-organization.
• Standard adaptive therapy is often outperformed by maximum tolerated dose (MTD) therapy when aggressive resistant subpopulations are present.

Purpose of the Study:

• To develop and evaluate a method to improve adaptive therapy's efficacy against aggressive resistant tumor subpopulations.
• To introduce a 'restore index' for predicting treatment cycle duration and optimizing adaptive therapy administration.

Main Methods:

• A tumor system with osimertinib-sensitive and resistant cell lines was modeled using the Lotka-Volterra model.
• A 'restore index' was proposed to assess system reachability and predict optimal treatment interruption points.
• Reachability-based adaptive therapy was compared to classic adaptive therapy via simulations and animal experiments.

Main Results:

• The restore index effectively predicted treatment cycle duration and identified optimal switching points to high-frequency administration.
• Reachability-based adaptive therapy demonstrated superior outcomes compared to classic adaptive therapy in the presence of aggressive resistant subpopulations.
• The new approach leverages tumor intra-competition and sensitive cell killing for enhanced control.

Conclusions:

• Reachability-based adaptive therapy offers an improved strategy for managing tumors with aggressive resistant subpopulations.
• The proposed restore index provides a feasible method for dynamically adjusting adaptive therapy cycles.
• This approach enhances adaptive therapy's benefits by integrating tumor dynamics and population interactions.