Literature DB >> 21767275

Predictive performance for human skin sensitizing potential of the human cell line activation test (h-CLAT).

Yuko Nukada1, Takao Ashikaga, Hitoshi Sakaguchi, Sakiko Sono, Nanae Mugita, Morihiko Hirota, Masaaki Miyazawa, Yuichi Ito, Hitoshi Sasa, Naohiro Nishiyama.   

Abstract

BACKGROUND: Recent changes in regulatory restrictions and social opposition to animal toxicology experiments have driven the need for reliable in vitro tests for predicting the skin sensitizing potentials of a wide variety of industrial chemicals. Previously, we developed the human cell line activation test (h-CLAT) as a cell-based assay to predict the skin sensitizing potential of chemicals, and showed the correspondence between the h-CLAT and the murine local lymph node assay results.
OBJECTIVES: This study was conducted to investigate the predictive performance of the h-CLAT for human skin sensitizing potential. MATERIALS/
METHODS: We selected a total of 66 test chemicals with known human sensitizing potential, and tested all chemicals with the h-CLAT. We then evaluated the performance of the h-CLAT in predicting human sensitizing potential. RESULTS AND
CONCLUSION: Forty-five of 51 tested sensitizers were positive in the h-CLAT, indicating relatively high sensitivity. Also, 10 of 15 non-sensitizers were correctly detected as negative. The overall agreement between human data and h-CLAT outcome was 83%. Furthermore, the h-CLAT could accurately predict the human sensitizing potential of 23 tested chemicals that were amines, heterocyclic compounds, or sulfur compounds. Our data indicate the utility of the h-CLAT for predicting the human skin sensitizing potential of a variety of chemicals.
© 2011 John Wiley & Sons A/S.

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Year:  2011        PMID: 21767275     DOI: 10.1111/j.1600-0536.2011.01952.x

Source DB:  PubMed          Journal:  Contact Dermatitis        ISSN: 0105-1873            Impact factor:   6.600


  7 in total

1.  Prediction of skin sensitization potency using machine learning approaches.

Authors:  Qingda Zang; Michael Paris; David M Lehmann; Shannon Bell; Nicole Kleinstreuer; David Allen; Joanna Matheson; Abigail Jacobs; Warren Casey; Judy Strickland
Journal:  J Appl Toxicol       Date:  2017-01-10       Impact factor: 3.446

2.  Skin sensitization in silico protocol.

Authors:  Candice Johnson; Ernst Ahlberg; Lennart T Anger; Lisa Beilke; Romualdo Benigni; Joel Bercu; Sol Bobst; David Bower; Alessandro Brigo; Sarah Campbell; Mark T D Cronin; Ian Crooks; Kevin P Cross; Tatyana Doktorova; Thomas Exner; David Faulkner; Ian M Fearon; Markus Fehr; Shayne C Gad; Véronique Gervais; Amanda Giddings; Susanne Glowienke; Barry Hardy; Catrin Hasselgren; Jedd Hillegass; Robert Jolly; Eckart Krupp; Liat Lomnitski; Jason Magby; Jordi Mestres; Lawrence Milchak; Scott Miller; Wolfgang Muster; Louise Neilson; Rahul Parakhia; Alexis Parenty; Patricia Parris; Alexandre Paulino; Ana Theresa Paulino; David W Roberts; Harald Schlecker; Reinhard Stidl; Diana Suarez-Rodrigez; David T Szabo; Raymond R Tice; Daniel Urbisch; Anna Vuorinen; Brian Wall; Thibaud Weiler; Angela T White; Jessica Whritenour; Joerg Wichard; David Woolley; Craig Zwickl; Glenn J Myatt
Journal:  Regul Toxicol Pharmacol       Date:  2020-07-01       Impact factor: 3.271

3.  Integrated decision strategies for skin sensitization hazard.

Authors:  Judy Strickland; Qingda Zang; Nicole Kleinstreuer; Michael Paris; David M Lehmann; Neepa Choksi; Joanna Matheson; Abigail Jacobs; Anna Lowit; David Allen; Warren Casey
Journal:  J Appl Toxicol       Date:  2016-02-06       Impact factor: 3.446

Review 4.  [Toxicological risk assessment using the example of potential contact sensitization to resorcinol].

Authors:  C Goebel; M Kock; H Merk
Journal:  Hautarzt       Date:  2019-12       Impact factor: 0.751

5.  Multivariate models for prediction of human skin sensitization hazard.

Authors:  Judy Strickland; Qingda Zang; Michael Paris; David M Lehmann; David Allen; Neepa Choksi; Joanna Matheson; Abigail Jacobs; Warren Casey; Nicole Kleinstreuer
Journal:  J Appl Toxicol       Date:  2016-08-02       Impact factor: 3.446

6.  Weight of Evidence Approach for Skin Sensitization Potency Categorization of Fragrance Ingredients.

Authors:  Mihwa Na; Devin O'Brien; Maura Lavelle; Isabelle Lee; G Frank Gerberick; Anne Marie Api
Journal:  Dermatitis       Date:  2022 Mar-Apr 01       Impact factor: 4.867

7.  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

  7 in total

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