Predictive Modeling of <i>In Vivo</i> Response to Gemcitabine in Pancreatic Cancer

<div><p>A clear contradiction exists between cytotoxic <i>in-vitro</i> studies demonstrating effectiveness of Gemcitabine to curtail pancreatic cancer and <i>in-vivo</i> studies failing to show Gemcitabine as an effective treatment. The outcome of chemotherapy in metastatic stages, where surgery is no longer viable, shows a 5-year survival <5%. It is apparent that <i>in-vitro</i> experiments, no matter how well designed, may fail to adequately represent the complex <i>in-vivo</i> microenvironmental and phenotypic characteristics of the cancer, including cell proliferation and apoptosis. We evaluate <i>in-vitro</i> cytotoxic data as an indicator of <i>in-vivo</i> treatment success using a mathematical model of tumor growth based on a dimensionless formulation describing tumor biology. Inputs to the model are obtained under optimal drug exposure conditions <i>in-vitro</i>. The model incorporates heterogeneous cell proliferation and death caused by spatial diffusion gradients of oxygen/nutrients due to inefficient vascularization and abundant stroma, and thus is able to simulate the effect of the microenvironment as a barrier to effective nutrient and drug delivery. Analysis of the mathematical model indicates the pancreatic tumors to be mostly resistant to Gemcitabine treatment <i>in-vivo</i>. The model results are confirmed with experiments in live mice, which indicate uninhibited tumor proliferation and metastasis with Gemcitabine treatment. By extracting mathematical model parameter values for proliferation and death from monolayer <i>in-vitro</i> cytotoxicity experiments with pancreatic cancer cells, and simulating the effects of spatial diffusion, we use the model to predict the drug response <i>in-vivo</i>, beyond what would have been expected from sole consideration of the cancer intrinsic resistance. We conclude that this integrated experimental/computational approach may enhance understanding of pancreatic cancer behavior and its response to various chemotherapies, and, further, that such an approach could predict resistance based on pharmacokinetic measurements with the goal to maximize effective treatment strategies.</p>