Data-driven modeling and characterization of anti-angiogenic molecule effects on tumoral vascular density
Résumé
Angiogenesis is the phenomenon by which new blood vessels are created from
preexisting ones. But this natural process is also involved, in a chaotic way,
in tumor development. Many molecules have shown particular efficiency in inhibiting
this phenomenon, hopefully leading to either: (i) a reorganization of the
neovessels allowing a better tumor uptake of cytotoxic molecules (as chemotherapy)
or (ii) a deprivation of the tumor vascular network with the view to starve
it. However, characterizing the anti-angiogenic effects of a molecule remains difficult,
mainly because the proposed physical modeling approaches have barely
been confronted to in vivo data, which are not directly available. This paper
presents an original approach to characterize and analyze the anti-angiogenic
responses in cancerology that allows biologists to account for spatial and dynamical
dimensions of the problem. The proposed solution relies on the association
of a specific biological in vivo protocol using skinfold chambers, image processing
and dynamic system identification. An empirical model structure of the
anti-angiogenic effect of a tested molecule is selected according to experimental
data. Finally the model is identified and its parameters are used to characterize
and compare responses of the tested molecule.