Radiation and PD-(L)1 treatment combinations: immune response and dose optimization via a predictive systems model

Area of Science:

• Computational oncology
• Quantitative systems pharmacology
• Cancer immunotherapy modeling

Background:

• Lack of quantitative simulation models for oncology combination therapies.
• Need for predictive frameworks for radiation (RT) and anti PD-(L)1 therapies.
• Integrating experimental data for biomarker and response rate prediction.

Purpose of the Study:

• To develop a validated simulation framework for combined RT and anti PD-(L)1 therapies.
• To predict tumor size dynamics, immune markers, and anti-tumor response rates.
• To evaluate untested dosing sequences and schedules.

Main Methods:

• Developed a quantitative systems pharmacology model of the cancer immunity cycle.
• Incorporated tumor microenvironment, growth, and dose-exposure-target modulation.
• Used a mixed-effects model to account for variability in tumor-infiltrating T cells.

Main Results:

• Model quantitatively explains synergistic kinetic effects of immune cell interactions on tumor size.
• T cell infiltration is a primary determinant of tumor response variability.
• Anti-PD-L1 before or concurrently with RT shows synergistic effects due to favorable immuno-modulation dynamics.

Conclusions:

• Provides quantitative mechanistic links between RT and anti-tumor immune responses.
• Optimized combinations and schedules of immunomodulation and radiation can favor host immunity.
• Potential for tumor shrinkage or rejection through improved immune balance.