Chen Lei, Wang Mingpei, Feng Yuanbo, Gao Lingjie, Yu Jie, Geng Lei, Xie Yiyang, Coudyzer Walter, Li Yue, Ni Yicheng
KU Leuven, Theragnostic Laboratory, Campus Gasthuisberg, Leuven, Belgium.
Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China.
Quant Imaging Med Surg. 2024 Feb 1;14(2):1729-1746. doi: 10.21037/qims-23-1065. Epub 2024 Jan 23.
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 purpose is to gain insights into their non-specific off-targeting effects on normal vasculature and provide clues for exploring underlying molecular mechanisms.
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.
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.
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.
血管破坏剂（VDAs）已知可特异性靶向已存在的肿瘤血管。然而，临床试验报告了其作为安全性或毒性问题的全身性副作用，这需要进一步的临床前研究。目的是深入了解它们对正常血管的非特异性脱靶效应，并为探索潜在的分子机制提供线索。
基于最近引入的由激光散斑对比成像（LSCI）、鸡胚绒毛尿囊膜（CAM）和辅助深度学习技术组成的平台，用于评估血管活性药物，在持续血管内输注两种VDAs的情况下，对胚胎第12天的血流动力学进行实时定性观察和定量测量，持续30分钟。通过使用具有因子交互作用的多因素方差分析（ANOVA），进一步分析和比较空白对照组和药物剂量组之间的血液灌注、血管直径、血管密度和血管总长度。对包括β-微管蛋白和F-肌动蛋白在内的内皮细胞骨架进行常规组织病理学和荧光免疫组织化学（FIHC）检测，进行定性证明、定量分析，并进一步与血流动力学和血管参数相关联。
VDAs对正常血管产生了全身性负面影响，具有统计学意义（P<0.0001），四个正相关参数证明了这一点，这可以解释临床患者中观察到的副作用。这种影响似乎呈剂量依赖性（P<0.0001）。FIHC检测从定性和定量上验证了结果，并揭示了分子机制。
结合深度学习技术的LSCI-CAM平台在血管活性药物的临床前评估中被证明是有用的。这些新证据为临床实践提供了新的参考。
