Data-driven modeling and characterization of anti-angiogenic molecule effects on tumoral vascular density

International audienceAngiogenesis is the phenomenon by which new blood vessels are created frompreexisting ones. But this natural process is also involved, in a chaotic way,in tumor development. Many molecules have shown particular efficiency in inhibitingthis phenomenon, hopefully leading to either: (i) a reorganization of theneovessels allowing a better tumor uptake of cytotoxic molecules (as chemotherapy)or (ii) a deprivation of the tumor vascular network with the view to starveit. However, characterizing the anti-angiogenic effects of a molecule remains difficult,mainly because the proposed physical modeling approaches have barelybeen confronted to in vivo data, which are not directly available. This paperpresents an original approach to characterize and analyze the anti-angiogenicresponses in cancerology that allows biologists to account for spatial and dynamicaldimensions of the problem. The proposed solution relies on the associationof a specific biological in vivo protocol using skinfold chambers, image processingand dynamic system identification. An empirical model structure of theanti-angiogenic effect of a tested molecule is selected according to experimentaldata. Finally the model is identified and its parameters are used to characterizeand compare responses of the tested molecule