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The GARD platform for potency assessment of skin sensitizing chemicals

Zeller, Kathrin S. LU ; Forreryd, Andy LU ; Lindberg, Tim LU ; Gradin, Robin LU ; Chawade, Aakash LU and Lindstedt, Malin LU (2017) In Altex 34(4). p.539-559
Abstract

Contact allergy induced by certain chemicals is a common health concern, and several alternative methods have been developed to fulfill the requirements of European legislation with regard to hazard assessment of potential skin sensitizers. However, validated methods, which provide information about the potency of skin sensitizers, are still lacking. The cell-based assay Genomic Allergen Rapid Detection (GARD), targeting key event 3, dendritic cell activation, of the skin sensitization AOP, uses gene expression profiling and a machine learning approach for the prediction of chemicals as sensitizers or non-sensitizers. Based on the GARD platform, we here expanded the assay to predict three sensitizer potency classes according to the... (More)

Contact allergy induced by certain chemicals is a common health concern, and several alternative methods have been developed to fulfill the requirements of European legislation with regard to hazard assessment of potential skin sensitizers. However, validated methods, which provide information about the potency of skin sensitizers, are still lacking. The cell-based assay Genomic Allergen Rapid Detection (GARD), targeting key event 3, dendritic cell activation, of the skin sensitization AOP, uses gene expression profiling and a machine learning approach for the prediction of chemicals as sensitizers or non-sensitizers. Based on the GARD platform, we here expanded the assay to predict three sensitizer potency classes according to the European Classification, Labelling and Packaging (CLP) Regulation, targeting categories 1A (strong), 1B (weak) and no cat (non-sensitizer). Using a random forest approach and 70 training samples, a potential biomarker signature of 52 transcripts was identified. The resulting model could predict an independent test set consisting of 18 chemicals, six from each CLP category and all previously unseen to the model, with an overall accuracy of 78%. Importantly, the model was shown to be conservative and only underestimated the class label of one chemical. Furthermore, an association of defined chemical protein reactivity with distinct biological pathways illustrates that our transcriptional approach can reveal information contributing to the understanding of underlying mechanisms in sensitization.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Biomarkers, In vitro assay, Potency, Random forest, Sensitization
in
Altex
volume
34
issue
4
pages
21 pages
publisher
Spektrum Akad Verlag
external identifiers
  • scopus:85035806329
  • wos:000417378000001
ISSN
1868-596X
DOI
10.14573/altex.1701101
language
English
LU publication?
yes
id
ff391e5c-48cb-410b-b9ce-300714075a95
date added to LUP
2017-12-12 14:46:38
date last changed
2018-02-18 05:05:55
@article{ff391e5c-48cb-410b-b9ce-300714075a95,
  abstract     = {<p>Contact allergy induced by certain chemicals is a common health concern, and several alternative methods have been developed to fulfill the requirements of European legislation with regard to hazard assessment of potential skin sensitizers. However, validated methods, which provide information about the potency of skin sensitizers, are still lacking. The cell-based assay Genomic Allergen Rapid Detection (GARD), targeting key event 3, dendritic cell activation, of the skin sensitization AOP, uses gene expression profiling and a machine learning approach for the prediction of chemicals as sensitizers or non-sensitizers. Based on the GARD platform, we here expanded the assay to predict three sensitizer potency classes according to the European Classification, Labelling and Packaging (CLP) Regulation, targeting categories 1A (strong), 1B (weak) and no cat (non-sensitizer). Using a random forest approach and 70 training samples, a potential biomarker signature of 52 transcripts was identified. The resulting model could predict an independent test set consisting of 18 chemicals, six from each CLP category and all previously unseen to the model, with an overall accuracy of 78%. Importantly, the model was shown to be conservative and only underestimated the class label of one chemical. Furthermore, an association of defined chemical protein reactivity with distinct biological pathways illustrates that our transcriptional approach can reveal information contributing to the understanding of underlying mechanisms in sensitization.</p>},
  author       = {Zeller, Kathrin S. and Forreryd, Andy and Lindberg, Tim and Gradin, Robin and Chawade, Aakash and Lindstedt, Malin},
  issn         = {1868-596X},
  keyword      = {Biomarkers,In vitro assay,Potency,Random forest,Sensitization},
  language     = {eng},
  number       = {4},
  pages        = {539--559},
  publisher    = {Spektrum Akad Verlag},
  series       = {Altex},
  title        = {The GARD platform for potency assessment of skin sensitizing chemicals},
  url          = {http://dx.doi.org/10.14573/altex.1701101},
  volume       = {34},
  year         = {2017},
}