Advanced

Skin Sensitization Testing-What's Next?

Grundström, Gunilla and Borrebaeck, Carl A.K. LU (2019) In International Journal of Molecular Sciences 20(3).
Abstract

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... (More)

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.

(Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
adverse outcome pathways, genomics, machine learning, next generation in vitro tests, skin sensitization
in
International Journal of Molecular Sciences
volume
20
issue
3
publisher
MOLECULAR DIVERSITY PRESERVATION INT
external identifiers
  • scopus:85061131668
ISSN
1422-0067
DOI
10.3390/ijms20030666
language
English
LU publication?
yes
id
07c32919-b4bb-4609-abfc-2e1866a21185
date added to LUP
2019-02-15 08:03:12
date last changed
2019-03-12 04:21:24
@article{07c32919-b4bb-4609-abfc-2e1866a21185,
  abstract     = {<p>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.</p>},
  author       = {Grundström, Gunilla and Borrebaeck, Carl A.K.},
  issn         = {1422-0067},
  keyword      = {adverse outcome pathways,genomics,machine learning,next generation in vitro tests,skin sensitization},
  language     = {eng},
  month        = {02},
  number       = {3},
  publisher    = {MOLECULAR DIVERSITY PRESERVATION INT},
  series       = {International Journal of Molecular Sciences},
  title        = {Skin Sensitization Testing-What's Next?},
  url          = {http://dx.doi.org/10.3390/ijms20030666},
  volume       = {20},
  year         = {2019},
}