Literature DB >> 26168096

LLNA variability: An essential ingredient for a comprehensive assessment of non-animal skin sensitization test methods and strategies.

Sebastian Hoffmann1.   

Abstract

The development of non-animal skin sensitization test methods and strategies is quickly progressing. Either individually or in combination, the predictive capacity is usually described in comparison to local lymph node assay (LLNA) results. In this process the important lesson from other endpoints, such as skin or eye irritation, to account for variability reference test results - here the LLNA - has not yet been fully acknowledged. In order to provide assessors as well as method and strategy developers with appropriate estimates, we investigated the variability of EC3 values from repeated substance testing using the publicly available NICEATM (NTP Interagency Center for the Evaluation of Alternative Toxicological Methods) LLNA database. Repeat experiments for more than 60 substances were analyzed - once taking the vehicle into account and once combining data over all vehicles. In general, variability was higher when different vehicles were used. In terms of skin sensitization potential, i.e., discriminating sensitizer from non-sensitizers, the false positive rate ranged from 14-20%, while the false negative rate was 4-5%. In terms of skin sensitization potency, the rate to assign a substance to the next higher or next lower potency class was approx.10-15%. In addition, general estimates for EC3 variability are provided that can be used for modelling purposes. With our analysis we stress the importance of considering the LLNA variability in the assessment of skin sensitization test methods and strategies and provide estimates thereof.

Entities:  

Keywords:  LLNA variability; skin sensitization; test method assessment; test strategy assessment

Mesh:

Substances:

Year:  2015        PMID: 26168096     DOI: 10.14573/altex.1505051

Source DB:  PubMed          Journal:  ALTEX        ISSN: 1868-596X            Impact factor:   6.043


  13 in total

1.  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

Review 2.  Perspectives on In Vitro to In Vivo Extrapolations.

Authors:  Thomas Hartung
Journal:  Appl In Vitro Toxicol       Date:  2018-12-08

Review 3.  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

4.  Mapping Chemical Respiratory Sensitization: How Useful Are Our Current Computational Tools?

Authors:  Emily Golden; Mikhail Maertens; Thomas Hartung; Alexandra Maertens
Journal:  Chem Res Toxicol       Date:  2020-12-15       Impact factor: 3.739

5.  Evaluation of a High-Throughput Peptide Reactivity Format Assay for Assessment of the Skin Sensitization Potential of Chemicals.

Authors:  Chin Lin Wong; Ai-Leen Lam; Maree T Smith; Sussan Ghassabian
Journal:  Front Pharmacol       Date:  2016-03-14       Impact factor: 5.810

6.  Analysis of publically available skin sensitization data from REACH registrations 2008-2014.

Authors:  Thomas Luechtefeld; Alexandra Maertens; Daniel P Russo; Costanza Rovida; Hao Zhu; Thomas Hartung
Journal:  ALTEX       Date:  2016-02-11       Impact factor: 6.043

7.  Standardisation of defined approaches for skin sensitisation testing to support regulatory use and international adoption: position of the International Cooperation on Alternative Test Methods.

Authors:  S Casati; K Aschberger; J Barroso; W Casey; I Delgado; T S Kim; N Kleinstreuer; H Kojima; J K Lee; A Lowit; H K Park; M J Régimbald-Krnel; J Strickland; M Whelan; Y Yang; Valérie Zuang
Journal:  Arch Toxicol       Date:  2017-11-10       Impact factor: 5.153

8.  QSAR models of human data can enrich or replace LLNA testing for human skin sensitization.

Authors:  Vinicius M Alves; Stephen J Capuzzi; Eugene Muratov; Rodolpho C Braga; Thomas Thornton; Denis Fourches; Judy Strickland; Nicole Kleinstreuer; Carolina H Andrade; Alexander Tropsha
Journal:  Green Chem       Date:  2016-10-06       Impact factor: 10.182

9.  Accounting for Precision Uncertainty of Toxicity Testing: Methods to Define Borderline Ranges and Implications for Hazard Assessment of Chemicals.

Authors:  Silke Gabbert; Miriam Mathea; Susanne N Kolle; Robert Landsiedel
Journal:  Risk Anal       Date:  2020-12-09       Impact factor: 4.302

10.  Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility.

Authors:  Thomas Luechtefeld; Dan Marsh; Craig Rowlands; Thomas Hartung
Journal:  Toxicol Sci       Date:  2018-09-01       Impact factor: 4.849

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