A robust optimal control framework for controlling aberrant RTK signaling pathways in esophageal cancer

Area of Science:

• Oncology
• Pharmacology
• Computational Biology

Background:

• Esophageal cancer treatment faces challenges due to aberrant signaling pathways like epidermal growth factor (EGF) and vascular endothelial growth factor (VEGF).
• Personalized treatment strategies are needed to effectively target these specific signaling mechanisms and account for patient heterogeneities.

Purpose of the Study:

• To develop a novel framework for personalized optimal treatment strategies in esophageal cancer.
• To create a pharmacokinetic model that incorporates patient-specific heterogeneities in EGF and VEGF signaling.
• To design and validate optimal therapies using a multi-step computational approach.

Main Methods:

• Developed a pharmacokinetic model accounting for signaling pathway heterogeneities.
• Employed a three-step process involving constrained optimization, sensitivity analysis, and optimal control.
• Utilized discrete patient data for model parameter estimation.
• Incorporated Trastuzumab (anti-EGF) and Bevacizumab (anti-VEGF) into the model for therapy simulation.

Main Results:

• Successfully identified key sensitive parameters in EGF and VEGF receptor evolution through sensitivity analysis.
• Demonstrated the framework's ability to derive optimal treatment strategies.
• Numerical results showed the efficiency of combining Trastuzumab and Bevacizumab for targeted therapy.

Conclusions:

• The proposed framework offers a promising approach for personalized treatment of esophageal cancer.
• The novel pharmacokinetic model and optimization strategy effectively target aberrant EGF and VEGF signaling pathways.
• Combination therapy with Trastuzumab and Bevacizumab shows significant potential based on computational modeling.