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A triangular approach for the validation of new approach methods for skin sensitization
Author(s) -
Andreas Natsch,
Robert Landsiedel,
Susanne N. Kolle
Publication year - 2021
Publication title -
altex/alternatives to animal experimentation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.975
H-Index - 51
eISSN - 1868-8551
pISSN - 1868-596X
DOI - 10.14573/altex.2105111
Subject(s) - local lymph node assay , skin sensitization , sensitization , computer science , human use , data mining , machine learning , medicine , biology , microbiology and biotechnology , immunology
The availability of reference data is a key requirement for the development of new approach methods (NAM), i.e., in vitro, in chemico and in silico methods and integrated approaches, like defined approaches (DA), which combine these data sources. Reference data are of even greater importance for regulatory acceptance. In contrast to most other adverse effects, human skin sensitization data on many chemicals are available, next to data from animal studies, such as the local lymph node assay (LLNA). Skin sensitization NAM data can therefore be compared to different reference datasets. Recent publications and validation at the OECD focused on human and LLNA reference data. The “2 out of 3” DA (2o3 DA) is the first DA for skin sensitization solely based on experimental data from validated tests and was recently adopted as an OECD test guideline. Here we review the predictivity of the 2o3 DA on multiple human and LLNA reference datasets. Concomitantly, we compare the predictivity of the LLNA for human data within the same datasets. Comparing predictivity of methods not only bilaterally (NAM or DA vs. animal method) but including human data in a triangle “NAM data – animal data – human data” offers a comprehensive assessment of the NAM’s and DA’s predictivity. In all these assessments, the 2o3 DA was superior to the LLNA in predicting human skin sensitization hazard. This highlights the importance of a holistic view of reference data instead of limiting validation of NAMs and DAs to data from a single animal test only.

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