Literature DB >> 25820183

Test battery with the human cell line activation test, direct peptide reactivity assay and DEREK based on a 139 chemical data set for predicting skin sensitizing potential and potency of chemicals.

Osamu Takenouchi1,2, Shiho Fukui3,2, Kenji Okamoto3,2, Satoru Kurotani4,2, Noriyasu Imai4,2, Miyuki Fujishiro5,2, Daiki Kyotani5,2, Yoshinao Kato6,2, Toshihiko Kasahara7,2, Masaharu Fujita7,2, Akemi Toyoda8,2, Daisuke Sekiya9,2, Shinichi Watanabe9,2, Hirokazu Seto10,2, Morihiko Hirota11,2, Takao Ashikaga11,2, Masaaki Miyazawa1,2.   

Abstract

To develop a testing strategy incorporating the human cell line activation test (h-CLAT), direct peptide reactivity assay (DPRA) and DEREK, we created an expanded data set of 139 chemicals (102 sensitizers and 37 non-sensitizers) by combining the existing data set of 101 chemicals through the collaborative projects of Japan Cosmetic Industry Association. Of the additional 38 chemicals, 15 chemicals with relatively low water solubility (log Kow > 3.5) were selected to clarify the limitation of testing strategies regarding the lipophilic chemicals. Predictivities of the h-CLAT, DPRA and DEREK, and the combinations thereof were evaluated by comparison to results of the local lymph node assay. When evaluating 139 chemicals using combinations of three methods based on integrated testing strategy (ITS) concept (ITS-based test battery) and a sequential testing strategy (STS) weighing the predictive performance of the h-CLAT and DPRA, overall similar predictivities were found as before on the 101 chemical data set. An analysis of false negative chemicals suggested a major limitation of our strategies was the testing of low water-soluble chemicals. When excluded the negative results for chemicals with log Kow > 3.5, the sensitivity and accuracy of ITS improved to 97% (91 of 94 chemicals) and 89% (114 of 128). Likewise, the sensitivity and accuracy of STS to 98% (92 of 94) and 85% (111 of 129). Moreover, the ITS and STS also showed good correlation with local lymph node assay on three potency classifications, yielding accuracies of 74% (ITS) and 73% (STS). Thus, the inclusion of log Kow in analysis could give both strategies a higher predictive performance.
Copyright © 2015 John Wiley & Sons, Ltd.

Entities:  

Keywords:  DEREK; DPRA; Skin sensitization; h-CLAT; in vitro test; integrated testing strategy; sequential testing strategy; test battery

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Year:  2015        PMID: 25820183     DOI: 10.1002/jat.3127

Source DB:  PubMed          Journal:  J Appl Toxicol        ISSN: 0260-437X            Impact factor:   3.446


  9 in total

1.  Tri-culture system for pro-hapten sensitizer identification and potency classification.

Authors:  Serom Lee; Talia Greenstein; Lingting Shi; Tim Maguire; Rene Schloss; Martin Yarmush
Journal:  Technology (Singap World Sci)       Date:  2018-06-29

2.  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

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

4.  Application of Defined Approaches for Skin Sensitization to Agrochemical Products.

Authors:  Judy Strickland; James Truax; Marco Corvaro; Raja Settivari; Joseph Henriquez; Jeremy McFadden; Travis Gulledge; Victor Johnson; Sean Gehen; Dori Germolec; David G Allen; Nicole Kleinstreuer
Journal:  Front Toxicol       Date:  2022-05-02

Review 5.  Non-animal methods to predict skin sensitization (II): an assessment of defined approaches *.

Authors:  Nicole C Kleinstreuer; Sebastian Hoffmann; Nathalie Alépée; David Allen; Takao Ashikaga; Warren Casey; Elodie Clouet; Magalie Cluzel; Bertrand Desprez; Nichola Gellatly; Carsten Göbel; Petra S Kern; Martina Klaric; Jochen Kühnl; Silvia Martinozzi-Teissier; Karsten Mewes; Masaaki Miyazawa; Judy Strickland; Erwin van Vliet; Qingda Zang; Dirk Petersohn
Journal:  Crit Rev Toxicol       Date:  2018-02-23       Impact factor: 5.635

6.  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

7.  How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology.

Authors:  Clemens Wittwehr; Hristo Aladjov; Gerald Ankley; Hugh J Byrne; Joop de Knecht; Elmar Heinzle; Günter Klambauer; Brigitte Landesmann; Mirjam Luijten; Cameron MacKay; Gavin Maxwell; M E Bette Meek; Alicia Paini; Edward Perkins; Tomasz Sobanski; Dan Villeneuve; Katrina M Waters; Maurice Whelan
Journal:  Toxicol Sci       Date:  2016-12-19       Impact factor: 4.849

8.  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

Review 9.  Standardisation and international adoption of defined approaches for skin sensitisation.

Authors:  Silvia Casati; David Asturiol; Patience Browne; Nicole Kleinstreuer; Michèle Régimbald-Krnel; Pierre Therriault
Journal:  Front Toxicol       Date:  2022-08-11
  9 in total

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