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

## TL;DR

This study creates a mathematical model to predict how pancreatic tumors respond to chemotherapy and other drugs in a mouse model.

## Contribution

A new mathematical model using ordinary differential equations to predict tumor response to combination therapies in pancreatic cancer.

## Key findings

- The model accurately reproduces tumor growth with an average concordance correlation coefficient of 0.99.
- Leave-one-out predictions achieved an average CCC of 0.74, showing robust predictive ability.
- Hybrid predictions combining group and mouse-specific data reached an average CCC of 0.85.

## Abstract

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 (\documentclass[12pt]{minimal}
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\begin{document}$${{Kras}}^{{\rm{LSL}}-{\rm{G12D}}}\,;\,{{Trp53}}^{{\rm{LSL}}-{\rm{R172H}}}\,;\,{Pdx1}-{\rm{Cre}}$$\end{document}KrasLSL-G12D;Trp53LSL-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.