Modeling tumor dynamics and predicting response to therapies in a murine pancreatic cancer model

We seek to establish a parsimonious mathematical framework for understanding the interaction and dynamics of the response of pancreatic cancer to the NGC triple chemotherapy regimen (mNab-paclitaxel, gemcitabine, and cisplatin), stromal-targeting drugs (calcipotriol and losartan), and an immune checkpoint inhibitor (anti-PD-L1). We developed a set of ordinary differential equations describing changes in tumor size under the influence of cocktails of treatments. Parameter estimation relies on three tumor volume measurements obtained over a 14-day period in a genetically engineered pancreatic cancer model ( ${Kras}^{LSL-G12D};{Trp53}^{LSL-R172H};Pdx1-Cre$ ). Our model reproduces tumor growth in all scenarios with an average concordance correlation coefficient (CCC) of 0.99 ± 0.01. We conduct leave-one-out predictions (average CCC = 0.74 ± 0.06), mouse-specific predictions (average CCC = 0.75 ± 0.02), and hybrid, group-informed, mouse-specific predictions (average CCC = 0.85 ± 0.04). The developed mathematical model demonstrates high accuracy in fitting the experimental tumor data and a robust ability to predict tumor response to treatment. This approach has important implications for optimizing combination NGC treatment strategies.