{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T22:08:22Z","timestamp":1788300502084,"version":"build-2803163510"},"reference-count":22,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,2,4]],"date-time":"2019-02-04T00:00:00Z","timestamp":1549238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats: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\u2122) 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\u2122 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\u2019s in vivo methods\u2014and beyond.<\/jats:p>","DOI":"10.3390\/ijms20030666","type":"journal-article","created":{"date-parts":[[2019,2,5]],"date-time":"2019-02-05T11:31:07Z","timestamp":1549366267000},"page":"666","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Skin Sensitization Testing\u2014What\u2019s Next?"],"prefix":"10.3390","volume":"20","author":[{"given":"Gunilla","family":"Grundstr\u00f6m","sequence":"first","affiliation":[{"name":"SenzaGen AB, Medicon Village, S-223 81 Lund, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carl A.K.","family":"Borrebaeck","sequence":"additional","affiliation":[{"name":"SenzaGen AB, Medicon Village, S-223 81 Lund, Sweden"},{"name":"Department of Immunotechnology, Lund University, Medicon Village (bldg 406), S-223 81 Lund, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,4]]},"reference":[{"key":"ref_1","unstructured":"Organization for Economic Co-operation and Development (OECD) (2014). The Adverse Outcome Pathway for Skin Sensitisation Initiated by Covalent Binding to Proteins, OECD Publishing."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1038\/nrc.2016.153","article-title":"Precision diagnostics: Moving toward biomarker signatures of clinical utility in cancer","volume":"17","author":"Borrebaeck","year":"2017","journal-title":"Nat. Rev. Cancer"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1093\/toxsci\/kfx115","article-title":"Skin Sensitization: Challenging the Conventional Thinking\u2014A Case Against 2 Out of 3 as Integrated Testing Strategy","volume":"159","author":"Johansson","year":"2017","journal-title":"Toxicol. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.yrtph.2018.07.014","article-title":"Is a combination of assays really needed for non-animal prediction of skinsensitization potential? Performance of the GARD\u2122 (Genomic Allergen Rapid Detection) assay in comparison with OECD guideline assays alone and in combination","volume":"98","author":"Roberts","year":"2018","journal-title":"Regul. Toxicol. Pharmacol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1038\/nrg3920","article-title":"Machine learning applications in genetics and genomics","volume":"16","author":"Libbrecht","year":"2015","journal-title":"Nat. Rev. Genet."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1920","DOI":"10.1161\/CIRCULATIONAHA.115.001593","article-title":"Machine Learning in Medicine","volume":"132","author":"Deo","year":"2015","journal-title":"Circulation"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.tiv.2016.07.014","article-title":"Consensus of classification trees for skin sensitisation hazard prediction","volume":"36","author":"Asturiol","year":"2016","journal-title":"Toxicol. Vitr."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"792","DOI":"10.1002\/jat.3424","article-title":"Prediction of skin sensitization potency using machine learning approaches","volume":"37","author":"Zang","year":"2017","journal-title":"J. Appl. Toxicol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1002\/jat.3366","article-title":"Multivariate Models for Prediction of Human Skin Sensitization Hazard","volume":"37","author":"Strickland","year":"2017","journal-title":"J. Appl. Toxicol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Johansson, H., Lindstedt, M., Albrekt, A.S., and Borrebaeck, C.A.K. (2011). A genomic biomarker signature can predict skin sensitisers using a cell-based in vitro alternative to animal tests. BMC Genom., 12.","DOI":"10.1186\/1471-2164-12-399"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1163","DOI":"10.1016\/j.tiv.2012.05.019","article-title":"The GARD assay for assessment of chemical skin sensitizers","volume":"37","author":"Johansson","year":"2013","journal-title":"Toxicol. Vitr."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1111\/j.1365-2567.2005.02274.x","article-title":"Functional and transcriptional profiling of MUTZ-3: A myeloid cell line acting as a model for dendritic cells","volume":"117","author":"Larsson","year":"2006","journal-title":"Immunology"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Forreryd, A., Johansson, H., Albrekt, A.S., Borrebaeck, C.A.K., and Lindstedt, M. (2015). Prediction of chemical respiratory sensitizers using GARD, a novel in vitro assay based on a genomic biomarker signature. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0118808"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.toxlet.2016.07.408","article-title":"The GARD assay expanded: Potency assessment of skin sensitizing chemicals","volume":"259","author":"Zeller","year":"2016","journal-title":"Toxicol. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s10565-008-9059-9","article-title":"The relationship between CD86\/CD54 expression and THP-1 cell viability in an in vitro skin sensitization test: Human cell line activation test (h-CLAT)","volume":"25","author":"Sakaguchi","year":"2009","journal-title":"Cell. Biol. Toxicol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.toxlet.2011.02.015","article-title":"Functionality and specificity of gene markers for skin sensitization in dendritic cells","volume":"203","author":"Lambrechts","year":"2011","journal-title":"Toxicol. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1093\/toxsci\/kfm259","article-title":"Skin sensitizers induce antioxidant response element dependent genes: Application to the in vitro testing of sensitisation potential of chemicals","volume":"102","author":"Natsch","year":"2008","journal-title":"Toxicol. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"292","DOI":"10.3109\/15569527.2012.667031","article-title":"An in vitro method for detecting chemical sensitization using human reconstructed skin models and its applicability to cosmetic, pharmaceutical and medical device safety testing","volume":"31","author":"McKim","year":"2012","journal-title":"Cutan. Ocul. Toxicol."},{"key":"ref_19","first-page":"227","article-title":"The murine local lymph node assay","volume":"43","author":"Hilton","year":"1995","journal-title":"Methods Mol. Biol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1084\/jem.64.4.625","article-title":"Studies on the sensitisation of animals with simple chemicals","volume":"64","author":"Landsteiner","year":"1936","journal-title":"J. Exp. Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.tiv.2016.01.007","article-title":"SENS-IS, a 3D reconstituted epidermis based model for quantifying chemical sensitization potency: Reproducibility and predictivity results from an inter-laboratory study","volume":"32","author":"Cottrez","year":"2016","journal-title":"Toxicol. Vitr."},{"key":"ref_22","first-page":"83","article-title":"A Machine Learning Algorithm for Identifying Atopic Dermatitis in Adults from Electronic Health Records","volume":"2017","author":"Gustafson","year":"2017","journal-title":"IEEE Int. Conf. Healthc. 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