Skin Sensitization Testing-What's Next?
(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.
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- author
- Grundström, Gunilla and Borrebaeck, Carl A.K. LU
- organization
- publishing date
- 2019-02-04
- 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
- MDPI AG
- external identifiers
-
- pmid:30720708
- 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
- 2024-04-01 20:19:43
@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}},
keywords = {{adverse outcome pathways; genomics; machine learning; next generation in vitro tests; skin sensitization}},
language = {{eng}},
month = {{02}},
number = {{3}},
publisher = {{MDPI AG}},
series = {{International Journal of Molecular Sciences}},
title = {{Skin Sensitization Testing-What's Next?}},
url = {{http://dx.doi.org/10.3390/ijms20030666}},
doi = {{10.3390/ijms20030666}},
volume = {{20}},
year = {{2019}},
}