SenzaGen AB, Medicon Village, S-223 81 Lund, Sweden.
Department of Immunotechnology, Lund University, Medicon Village (bldg 406), S-223 81 Lund, Sweden.
Int J Mol Sci. 2019 Feb 4;20(3):666. doi: 10.3390/ijms20030666.
There is an increasing demand for alternative in vitro methods to replace animal testing, and, to succeed, new methods are required to be at least as accurate as existing in vivo tests. However, skin sensitization is a complex process requiring coordinated and tightly regulated interactions between a variety of cells and molecules. Consequently, there is considerable difficulty in reproducing this level of biological complexity in vitro, and as a result the development of non-animal methods has posed a major challenge. However, with the use of a relevant biological system, the high information content of whole genome expression, and comprehensive bioinformatics, assays for most complex biological processes can be achieved. We propose that the Genomic Allergen Rapid Detection (GARD™) assay, developed to create a holistic data-driven in vitro model with high informational content, could be such an example. Based on the genomic expression of a mature human dendritic cell line and state-of-the-art machine learning techniques, GARD™ can today accurately predict skin sensitizers and correctly categorize skin sensitizing potency. Consequently, by utilizing advanced processing tools in combination with high information genomic or proteomic data, we can take the next step toward alternative methods with the same predictive accuracy as today's in vivo methods-and beyond.
人们越来越希望找到替代动物试验的体外方法,而新方法至少要与现有的体内试验同样准确,才能取得成功。但是,皮肤致敏是一个复杂的过程,需要多种细胞和分子之间协调和严格的相互作用。因此,在体外重现这种生物复杂性具有相当大的难度,因此开发非动物方法一直是一个重大挑战。然而,使用相关的生物系统、全基因组表达的高信息含量以及全面的生物信息学,可以实现大多数复杂生物过程的检测。我们提出,开发基因组过敏原快速检测(GARD™)检测方法是为了创建一个具有高信息含量的整体数据驱动的体外模型,这可能就是这样一个例子。基于成熟的人类树突状细胞系的基因组表达和最先进的机器学习技术,GARD™ 今天可以准确预测皮肤致敏原,并正确分类皮肤致敏强度。因此,通过利用先进的处理工具结合高信息量的基因组或蛋白质组数据,我们可以朝着与今天的体内方法一样准确的预测精度的替代方法迈进——甚至超越。