A General Network Pharmacodynamic Model-Based Design Pipeline for Customized Cancer Therapy Applied to the VEGFR...

Area of Science:

• Computational Biology and Bioinformatics
• Pharmacology and Pharmaceutical Sciences
• Oncology and Cancer Research

Background:

• Optimizing multidrug chemotherapy requires understanding complex drug interactions within signaling pathways.
• The vascular endothelial growth factor receptor (VEGFR) signaling system is a key target in cancer therapy.
• Existing models often lack the integration of pharmacokinetic and pharmacodynamic properties for comprehensive drug optimization.

Purpose of the Study:

• To develop a unified pharmacokinetic/pharmacodynamic (PK/PD) model for optimizing multidrug chemotherapy targeting the VEGFR signaling system.
• To identify potential drug combinations and schedules that maximize target inhibition while minimizing dosage.
• To explore the impact of oncogenic mutations on optimal therapeutic strategies.

Main Methods:

• Developed a detailed VEGFR signaling network model incorporating ligand-receptor interactions and downstream enzyme cascades.
• Integrated a sunitinib (VEGFR inhibitor) PK model with the VEGFR network model.
• Performed Sobol sensitivity analysis to identify key drug targets and employed optimization-based control analyses for regimen design.

Main Results:

• Sensitivity analysis revealed potential mechanisms to enhance sunitinib efficacy.
• Designed multidrug regimens (including VEGF, PI3K, PLCγ, and MAPK inhibitors) that maintained 80% pERK and pAkt inhibition for 28 days.
• Optimal regimens often involved low doses and varied continuous/discontinuous schedules, influenced by specific oncogenic mutations.

Conclusions:

• Model-based approaches can effectively capture the complexity of drug actions in cancer chemotherapy.
• This computational pipeline demonstrates a powerful tool for tailoring cancer treatment strategies.
• The findings support the advancement of personalized medicine through in silico drug regimen optimization.