Combination Chemotherapy Optimization with Discrete Dosing

Area of Science:

• Mathematical modeling
• Computational oncology
• Pharmacodynamics

Background:

• Chemotherapy regimen evaluation is costly and complex.
• Reliable models can inform future clinical trials.
• Tumor heterogeneity introduces uncertainty in treatment outcomes.

Purpose of the Study:

• To develop a mixed-integer programming model for optimizing combination chemotherapy.
• To incorporate operational constraints, including toxicity and tumor response.
• To address uncertainty in tumor heterogeneity using chance constraints.

Main Methods:

• Formulated a mixed-integer program for chemotherapy optimization.
• Included dose, concentration, and white blood cell count constraints for toxicity.
• Implemented chance constraints to ensure tumor size operability.
• Validated the model's accuracy in representing biological processes.

Main Results:

• The model accurately represents chemotherapy's biological effects.
• Numerical studies demonstrate potential for clinical application in breast cancer.
• The optimization framework effectively controls treatment toxicity.
• Chance constraints enhance the probability of achieving operable tumor size.

Conclusions:

• The proposed mixed-integer program offers a reliable approach to optimize combination chemotherapy.
• This model can reduce the burden of clinical trials and improve treatment efficacy.
• The framework shows promise for personalized neoadjuvant therapy in breast cancer.