免疫肿瘤学中空纤维试验的探索性见解:一种连接体外和体内模型的试点方法。

IF 2.5 4区 医学 Q3 BIOCHEMICAL RESEARCH METHODS
Tove Selvin , Malin Berglund , Anders Åkerström , Marco Zia , Jakob Rudfeldt , Malin Jarvius , Rolf Larsson , Claes R Andersson , Mårten Fryknäs
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引用次数: 0

摘要

为了促进免疫疗法从实验室到床边的转化,预测性临床前模型是必不可少的。我们开发了体内免疫肿瘤中空纤维试验(HFA),以弥补基于细胞的简单体外试验和更复杂的免疫肿瘤药物评估小鼠模型之间的差距。该试验涉及将人类癌细胞系或原发患者来源的癌细胞与人类免疫细胞在半透性中空纤维内共培养。在小鼠腹腔内植入后,纤维捕获了aCD3和/或IL-2治疗后诱导的免疫细胞介导的癌细胞杀伤,证明了该试验的可行性。传统模型需要较长的观察期来监测肿瘤生长和治疗反应。免疫肿瘤学HFA使免疫制剂对癌症和人类起源的免疫细胞的快速初步体内评估成为可能,解决了动物研究中的两个3r -还原和细化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploratory insights from the immuno-oncology hollow fiber assay: A pilot approach bridging In Vitro and In Vivo models
To facilitate the translation of immunotherapies from bench to bedside, predictive preclinical models are essential. We developed the in vivo immuno-oncology Hollow Fiber Assay (HFA) to bridge the gap between simpler cell-based in vitro assays and more complex mouse models for immuno-oncology drug evaluation. The assay involves co-culturing human cancer cell lines or primary patient-derived cancer cells with human immune cells inside semipermeable hollow fibers. Implanted intraperitoneally in mice, the fibers captured treatment-induced immune cell-mediated cancer cell killing following treatments with aCD3 and/or IL-2, demonstrating the feasibility of the assay. Traditional models require lengthy observation periods to monitor tumor growth and treatment response. The immuno-oncology HFA enables a rapid initial in vivo evaluation of immunological agents on cancer and immune cells of human origin, addressing two of the 3Rs — reduction and refinement — in animal research.
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来源期刊
SLAS Technology
SLAS Technology Computer Science-Computer Science Applications
CiteScore
6.30
自引率
7.40%
发文量
47
审稿时长
106 days
期刊介绍: SLAS Technology emphasizes scientific and technical advances that enable and improve life sciences research and development; drug-delivery; diagnostics; biomedical and molecular imaging; and personalized and precision medicine. This includes high-throughput and other laboratory automation technologies; micro/nanotechnologies; analytical, separation and quantitative techniques; synthetic chemistry and biology; informatics (data analysis, statistics, bio, genomic and chemoinformatics); and more.
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