Advanced Tissue Technologies of Blood-Brain Barrier Organoids as High Throughput Toxicity Readouts in Drug Development

Luisa Bell, Claire Simonneau, Chiara Zanini, Elena Kassianidou, Christelle Zundel, Rachel Neff, Bernd Steinhuber, Marco Tecilla, Alex Odermatt, Roberto Villaseñor, Nadine Stokar-Regenscheit
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Abstract

Recent advancements in engineering Complex in vitro models (CIVMs) such as Blood-brain barrier (BBB) organoids offer promising platforms for preclinical drug testing. However, their application in drug development, and especially for the regulatory purposes of toxicity assessment, requires robust and reproducible techniques. Here, we developed an adapted set of orthogonal image-based tissue methods including hematoxylin and eosin staining (HE), immunohistochemistry (IHC), multiplex immunofluorescence (mIF), and Matrix Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI) to validate CIVMs for drug toxicity assessments. We developed an artificial intelligence (AI) algorithm to increase the throughput and the reliability of histomorphologic evaluations of apoptosis for in vitro toxicity studies. Our data highlight the potential to integrate advanced morphology-based readouts such as histological techniques and digital pathology algorithms for use on CIVMs, as part of a standard preclinical drug development assessment.
将血脑屏障有器官组织的先进组织技术作为药物开发中的高通量毒性读数
血脑屏障(BBB)有机体等复杂体外模型(CIVMs)工程的最新进展为临床前药物测试提供了前景广阔的平台。然而,将其应用于药物开发,特别是用于毒性评估的监管目的,需要稳健且可重复的技术。在这里,我们开发了一套基于图像的正交组织方法,包括苏木精和伊红染色(HE)、免疫组织化学(IHC)、多重免疫荧光(mIF)和基质辅助激光解吸/电离质谱成像(MALDI-MSI),以验证用于药物毒性评估的CIVMs。我们开发了一种人工智能(AI)算法,以提高体外毒性研究中细胞凋亡组织形态学评估的通量和可靠性。我们的数据凸显了将组织学技术和数字病理学算法等基于形态学的先进读数整合到 CIVM 上的潜力,并将其作为临床前药物开发标准评估的一部分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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