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 ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup><mml:mrow><mml:mi>K</mml:mi> <mml:mi>r</mml:mi> <mml:mi>a</mml:mi> <mml:mi>s</mml:mi></mml:mrow> <mml:mrow><mml:mstyle><mml:mi>L</mml:mi> <mml:mi>S</mml:mi> <mml:mi>L</mml:mi></mml:mstyle> <mml:mo>-</mml:mo> <mml:mstyle><mml:mi>G</mml:mi> <mml:mn>12</mml:mn> <mml:mi>D</mml:mi></mml:mstyle> </mml:mrow> </mml:msup> <mml:mspace></mml:mspace> <mml:mo>;</mml:mo> <mml:mspace></mml:mspace> <mml:msup><mml:mrow><mml:mi>T</mml:mi> <mml:mi>r</mml:mi> <mml:mi>p</mml:mi> <mml:mn>53</mml:mn></mml:mrow> <mml:mrow><mml:mstyle><mml:mi>L</mml:mi> <mml:mi>S</mml:mi> <mml:mi>L</mml:mi></mml:mstyle> <mml:mo>-</mml:mo> <mml:mstyle><mml:mi>R</mml:mi> <mml:mn>172</mml:mn> <mml:mi>H</mml:mi></mml:mstyle> </mml:mrow> </mml:msup> <mml:mspace></mml:mspace> <mml:mo>;</mml:mo> <mml:mspace></mml:mspace> <mml:mi>P</mml:mi> <mml:mi>d</mml:mi> <mml:mi>x</mml:mi> <mml:mn>1</mml:mn> <mml:mo>-</mml:mo> <mml:mstyle><mml:mi>C</mml:mi> <mml:mi>r</mml:mi> <mml:mi>e</mml:mi></mml:mstyle> </mml:mrow> </mml:math> ). 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.