Background
Vascular disrupting agents (VDAs) are known to specifically target preexisting tumoural vasculature. However, systemic side effects as safety or toxicity issues have been reported from clinical trials, which call for further preclinical investigations. The
Conclusions
LSCI-CAM platform combining with deep learning technique proves useful in preclinical evaluations of vasoactive medications. Such new evidences provide new reference to clinical practice.
Methods
Based on a recently introduced platform consisting laser speckle contrast imaging (LSCI), chick embryo chorioallantoic membrane (CAM), and assisted deep learning techniques, for evaluation of vasoactive medicines, hemodynamics on embryonic day 12 under constant intravascular infusion of two VDAs were qualitatively observed and quantitatively measured in real time for 30 min. Blood perfusion, vessel diameter, vessel density, and vessel total length were further analyzed and compared between blank control and medicines dose groups by using multi-factor analysis of variance (ANOVA) analysis with factorial interactions. Conventional histopathology and fluorescent immunohistochemistry (FIHC) assays for endothelial cytoskeleton including ß-tubulin and F-actin were qualitatively demonstrated, quantitatively analyzed and further correlated with hemodynamic and vascular parameters.
Results
The normal vasculature was systemically negatively affected by VDAs with statistical significance (P<0.0001), as evidenced by four positively correlated parameters, which can explain the side-effects observed among clinical patients. Such effects appeared to be dose dependent (P<0.0001). FIHC assays qualitatively and quantitatively verified the results and exposed molecular mechanisms. Conclusions: LSCI-CAM platform combining with deep learning technique proves useful in preclinical evaluations of vasoactive medications. Such new evidences provide new reference to clinical practice.