Literature DB >> 24675087

Genomic allergen rapid detection in-house validation--a proof of concept.

Henrik Johansson1, Frida Rydnert, Jochen Kühnl, Andreas Schepky, Carl Borrebaeck, Malin Lindstedt.   

Abstract

Chemical sensitization is an adverse immunologic response to chemical substances, inducing hypersensitivity in exposed individuals. Identifying chemical sensitizers is of great importance for chemical, pharmaceutical, and cosmetic industries, in order to prevent the use of sensitizers in consumer products. Historically, chemical sensitizers have been assessed mainly by in vivo methods, however, recently enforced European legislations urge and promote the development of animal-free test methods able to predict chemical sensitizers. Recently, we presented a predictive biomarker signature in the myeloid cell line MUTZ-3, for assessment of skin sensitizers. The identified genomic biomarkers were found to be involved in immunologically relevant pathways, induced by recognition of foreign substances and regulating dendritic cell maturation and cytoprotective mechanisms. We have developed the usage of this biomarker signature into a novel in vitro assay for assessment of chemical sensitizers, called Genomic Allergen Rapid Detection (GARD). The assay is based on chemical stimulation of MUTZ-3 cultures, using the compounds to be assayed as stimulatory agents. The readout of the assay is a transcriptional quantification of the genomic predictors, collectively termed the GARD Prediction Signature (GPS), using a complete genome expression array. Compounds are predicted as either sensitizers or nonsensitizers by a Support Vector Machine model. In this report, we provide a proof of concept for the functionality of the GARD assay by describing the classification of 26 blinded and 11 nonblinded chemicals as sensitizers or nonsensitizers. Based on these classifications, the accuracy, sensitivity, and specificity of the assay were estimated to 89, 89, and 88%, respectively.

Entities:  

Keywords:  GARD; allergic contact dermatitis; chemical sensitizers; in vitro assay; predictive assay; skin sensitization

Mesh:

Substances:

Year:  2014        PMID: 24675087     DOI: 10.1093/toxsci/kfu046

Source DB:  PubMed          Journal:  Toxicol Sci        ISSN: 1096-0929            Impact factor:   4.849


  6 in total

1.  Predicting full thickness skin sensitization using a support vector machine.

Authors:  Serom Lee; David Xu Dong; Rohit Jindal; Tim Maguire; Bhaskar Mitra; Rene Schloss; Martin Yarmush
Journal:  Toxicol In Vitro       Date:  2014-07-12       Impact factor: 3.500

2.  Validation of the GARD™skin Assay for Assessment of Chemical Skin Sensitizers: Ring Trial Results of Predictive Performance and Reproducibility.

Authors:  Henrik Johansson; Robin Gradin; Angelica Johansson; Els Adriaens; Amber Edwards; Veronika Zuckerstätter; Anders Jerre; Florence Burleson; Helge Gehrke; Erwin L Roggen
Journal:  Toxicol Sci       Date:  2019-08-01       Impact factor: 4.849

3.  A machine learning algorithm for early detection of heel deep tissue injuries based on a daily history of sub-epidermal moisture measurements.

Authors:  Maayan Lustig; Dafna Schwartz; Ruth Bryant; Amit Gefen
Journal:  Int Wound J       Date:  2022-01-12       Impact factor: 3.099

4.  Prediction of chemical respiratory sensitizers using GARD, a novel in vitro assay based on a genomic biomarker signature.

Authors:  Andy Forreryd; Henrik Johansson; Ann-Sofie Albrekt; Carl A K Borrebaeck; Malin Lindstedt
Journal:  PLoS One       Date:  2015-03-11       Impact factor: 3.240

5.  Identification of transcriptome signatures and biomarkers specific for potential developmental toxicants inhibiting human neural crest cell migration.

Authors:  Giorgia Pallocca; Marianna Grinberg; Margit Henry; Tancred Frickey; Jan G Hengstler; Tanja Waldmann; Agapios Sachinidis; Jörg Rahnenführer; Marcel Leist
Journal:  Arch Toxicol       Date:  2015-12-26       Impact factor: 5.153

Review 6.  Artificial Intelligence Applications in Dermatology: Where Do We Stand?

Authors:  Arieh Gomolin; Elena Netchiporouk; Robert Gniadecki; Ivan V Litvinov
Journal:  Front Med (Lausanne)       Date:  2020-03-31
  6 in total

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