Mechanistic Multiscale Pharmacokinetic Model for the Anticancer Drug 2’,2’-difluorodeoxycytidine (Gemcitabine) in Pancreatic Cancer
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García-Cremades, M. (María); Melillo, N. (Nicola); Troconiz, I.F. (Iñaki F.); et al. "Mechanistic Multiscale Pharmacokinetic Model for the Anticancer Drug 2’,2’-difluorodeoxycytidine (Gemcitabine) in Pancreatic Cancer". CTS - Clinical and Translational Science. 13 (3), 2020, 608 - 617
The aim of this work is to build a mechanistic multiscale pharmacokinetic model for the anticancer drug 2’, 2’-difluorodeoxycytidine (gemcitabine, dFdC), able to describe the concentrations of dFdC metabolites in the pancreatic tumor tissue in dependence of physiological and genetic patient characteristics, and, more in general, to explore the capabilities and limitations of this kind of modeling strategy. A mechanistic model characterizing dFdC metabolic pathway (metabolic network) was developed using in vitro literature data from two pancreatic cancer cell lines. The network was able to describe the time course of extracellular and intracellular dFdC metabolites concentrations. Moreover, a physiologically-based pharmacokinetic model was developed to describe clinical dFdC profiles by using enzymatic and physiological information available in the literature. This model was then coupled with the metabolic network to describe the dFdC active metabolite profile in the pancreatic tumor tissue. Finally, global sensitivity analysis was performed to identify the parameters that mainly drive the interindividual variability for the area under the curve (AUC) of dFdC in plasma and of its active metabolite (dFdCTP) in tumor tissue. From this analysis, cytidine deaminase (CDA) concentration was identified as the main driver of plasma dFdC AUC interindividual variability, whereas CDA and deoxycytidine kinase concentration mainly explained the tumor dFdCTP AUC variability. However, the lack of in vitro and in vivo information needed to characterize key model parameters hampers the development of this kind of mechanistic approach. Further studies to better characterize pancreatic cell lines and patient enzymes polymorphisms are encouraged to refine and validate the current model.

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