MO‐E‐BRB‐05: A Computational Tumor Modeling Framework for the Optimization of Molecular Targeted Therapies

Purpose : Current treatment schedules of molecular targeted therapies are established through costly clinical trials. Although several dosing schemes are used clinically, optimal dosing regimens remain unknown. We developed a computational tumor modeling framework to compare dosing schedules based on simulated therapeutic response. Method and Materials : A pharmacokinetic/pharmacodynamic model was developed to simulate changes in tumor cell proliferation and vascular function. The model was applied to data from a clinical trial in which patients received sunitinib, a molecular targeted agent with anti‐angiogenic and anti‐proliferative effects, on a 4/2 (4 weeks on, 2 week break) or 2/1 schedule. Using [ 18 F]FLT PET/CT imaging, levels of tumor proliferation and vascular function were assessed at baseline, peak drug exposure, and during treatment break. After testing the model on data from the 4/2 schedule, we compared simulated therapeutic responses for these dosing regimens: 4/2 cycle, two consecutive 2/1 cycles, and continuous dosing. Results : Trends in proliferative response were successfully simulated within one standard error of the population means. Two consecutive 2/1 cycles resulted in a 12% greater decrease in tumor proliferation as compared to one 4/2 cycle due to decreased drug washout during the off‐drug period. For iso‐response conditions, the dose for the 2/1 schedule could be reduced to 80% of the 4/2 schedule (from 50mg/kg/day to 40mg/kg/day). Continuous dosing using lower daily doses (32.5mg/kg/day) yielded the best growth inhibition after 6 weeks. Conclusion : The implemented model successfully reproduced trends in proliferative response observed in patients receiving sunitinib. Continuous dosing yielded the best growth inhibition, and outperformed two consecutive 2/1 cycles and the 4/2 regimen, indicating that this regimen might be favorable, especially for patients requiring lower daily doses to manage toxic side effects. Upon successful validation, the implemented model could serve as a cost‐effective tool to help identify improved drug regimens.