Exposure-response modeling improves selection of radiation and radiosensitizer combinations

Area of Science:

• Pharmacology
• Oncology
• Biostatistics

Background:

• Drug discovery necessitates efficient methods for selecting promising drug candidates from vast compound libraries.
• Optimizing combinations of radiation therapy and radiosensitizing agents is crucial for improving cancer treatment efficacy.

Purpose of the Study:

• To present a model-based quantitative approach for comparing and ranking combinations of radiation and radiosensitizers.
• To evaluate drug candidates based on their potential for tumor regression relative to toxicity and other costs using the Tumor Static Exposure (TSE) concept.

Main Methods:

• A case study involving three radiosensitizing agents was conducted.
• Nonlinear mixed-effects modeling was employed to analyze xenograft study data.
• A previously published tumor model for radiation and radiosensitizing agents was utilized.

Main Results:

• The most promising drug candidate was identified assuming equal toxicity among all agents.
• The impact of differential toxicity on candidate selection was demonstrated, altering the optimal choice.

Conclusions:

• The developed model provides a quantitative framework for selecting optimal radiation and radiosensitizer combinations in drug discovery.
• Toxicity is a critical factor that must be incorporated into the evaluation process for effective drug candidate selection.