Saiki Naofumi, Adachi Akiko, Ohnishi Hiroshi, Koga Atsuro, Ueki Masaru, Kohno Kiyotaka, Hayashi Toshinori, Ohbayashi Tetsuya
Division of Medical Education, Department of Medical Education, Tottori University Faculty of Medicine, Yonago 683-8503, Japan.
Advanced Medicine & Translational Research Center, Organization for Research Initiative and Promotion, Tottori University, Yonago 683-8503, Japan.
Yonago Acta Med. 2024 Aug 27;67(3):233-241. doi: 10.33160/yam.2024.08.011. eCollection 2024 Aug.
The 3Rs (Reduction, Refinement, Replacement) principle is driving the need for alternative methods in animal testing. Despite advancements in in vitro testing, complex systemic toxicity tests still necessitate in vivo approaches. The aim of this study was to develop a developmental toxicity test protocol using the Iberian ribbed newt () as a model organism, integrating AI image analysis for embryo selection to improve test accuracy and reproducibility.
We established a developmental toxicity test protocol based on the zebrafish test. Gonadotropin was administered to induce ovulation, and in vitro fertilization was performed. Embryos were imaged at 5-6 and 6-7 h post-fertilization. AI image analysis was utilized to assess embryo viability. The test chemical was administered 24-48 h post-fertilization, and morphological changes were observed daily until day 8. Additionally, a time-lapse photography system was constructed to monitor embryonic development.
Out of 24 cultured embryos, 75% developed normally to the late tail bud stage or initial hatching stage, whereas 25% experienced developmental arrest or death. AI image analysis achieved high accuracy in classifying embryos, with overall accuracies of 92.0% and 92.9% for two learning models. The AI system demonstrated higher precision in the selection of viable embryos compared to visual inspection.
The Iberian ribbed newt presents a viable alternative model for developmental toxicity testing, adhering to the 3Rs principles. The integration of AI image analysis substantially enhances the accuracy and reproducibility of embryo selection, providing a reliable method for evaluating developmental toxicity in pharmaceuticals.
3R原则（减少、优化、替代）推动了动物实验替代方法的需求。尽管体外测试取得了进展，但复杂的全身毒性测试仍需要体内实验方法。本研究的目的是开发一种以伊比利亚肋突螈为模式生物的发育毒性测试方案，整合人工智能图像分析用于胚胎选择，以提高测试的准确性和可重复性。
我们基于斑马鱼测试建立了一种发育毒性测试方案。注射促性腺激素诱导排卵，并进行体外受精。在受精后5 - 6小时和6 - 7小时对胚胎进行成像。利用人工智能图像分析评估胚胎活力。在受精后24 - 48小时给予受试化学品，每天观察形态变化直至第8天。此外，构建了一个延时摄影系统来监测胚胎发育。
在24个培养的胚胎中，75%正常发育至尾芽后期或初始孵化阶段，而25%出现发育停滞或死亡。人工智能图像分析在胚胎分类方面具有很高的准确性，两种学习模型的总体准确率分别为92.0%和92.9%。与目视检查相比，人工智能系统在选择存活胚胎方面表现出更高的精度。
伊比利亚肋突螈是一种符合3R原则的可行的发育毒性测试替代模型。人工智能图像分析的整合显著提高了胚胎选择的准确性和可重复性，为评估药物的发育毒性提供了一种可靠的方法。
