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Integrated testing and assessment approaches for skin sensitization: a commentary
Author(s) -
Roberts David W.,
Patlewicz Grace Y.
Publication year - 2014
Publication title -
journal of applied toxicology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.784
H-Index - 87
eISSN - 1099-1263
pISSN - 0260-437X
DOI - 10.1002/jat.2943
Subject(s) - skin sensitization , potency , bayesian probability , sensitization , predictive value , computer science , biochemical engineering , machine learning , computational biology , chemistry , medicine , artificial intelligence , biology , in vitro , immunology , engineering , biochemistry
ABSTRACT A Bayesian integrated testing strategy (ITS) approach, aiming to assess skin sensitization potency, has been presented, in which data from various types of in vitro assays are integrated and assessed in combination for their ability to predict in vivo skin sensitization data. Here we discuss this approach and compare it to our quantitative mechanistic modeling (QMM) approach based on physical organic chemistry. The main findings of the Bayesian study are consistent with our chemistry‐based approach and our previously published assessment of the key determinants of sensitization potency, in particular the relatively high predictive value found for chemical reactivity data and the relatively low predictive value for bioavailability parameters. As it stands at present the Bayesian approach does not utilize the full range of predictive capability that is already available, and aims only to assign potency categories rather than numerical potency values per se . In contrast, for many chemicals the QMM approach can already provide numerical potency predictions. However, the Bayesian approach may have potential for those chemicals where a chemistry modeling approach cannot provide a complete answer (e.g. pro‐electrophiles whose in cutaneo activation cannot currently be modeled confidently). Nonetheless, our main message is of the importance of leveraging chemistry insights and read‐across approaches to the fullest extent possible. Copyright © 2013 John Wiley & Sons, Ltd.