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Chemical reactivity indices and mechanism‐based read‐across for non‐animal based assessment of skin sensitisation potential
Author(s) -
Roberts David W.,
Aptula Aynur O.,
Patlewicz Grace,
Pease Camilla
Publication year - 2008
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.1293
Subject(s) - local lymph node assay , context (archaeology) , reactivity (psychology) , mechanism (biology) , nucleophile , electrophile , in silico , chemistry , in vivo , bioassay , toxicokinetics , biochemical engineering , hazard , computational biology , combinatorial chemistry , biological system , toxicology , in vitro , biochemistry , organic chemistry , microbiology and biotechnology , biology , medicine , toxicity , paleontology , philosophy , alternative medicine , genetics , pathology , potency , gene , engineering , catalysis , epistemology
Abstract The skin sensitisation potential of chemicals is currently assessed using in vivo methods where the murine local lymph node assay (LLNA) is typically the method of first choice. Current regulatory initiatives are driving the impetus for the use of in vitro/in silico alternative approaches to provide the relevant information needed for the effective assessment of skin sensitisation, for both hazard characterisation and risk assessment purposes. A chemical must undergo a number of steps for it to induce skin sensitisation but the main determining step is formation of a stable covalent association with carrier protein. The ability of a chemical to react covalently with carrier protein nucleophiles relates to both its electrophilic reactivity and its hydrophobicity. This paper focuses on quantitative indices of electrophilic reactivity with nucleophiles, in a chemical mechanism‐of‐action context, and compares and contrasts the experimental approaches available to generate reactivity data that are suitable for mathematical modelling and making predictions of skin sensitisation potential, using new chemistry data correlated against existing in vivo bioassay data. As such, the paper goes on to describe an illustrative example of how quantitative kinetic measures of reactivity can be usefully and simply applied to perform mechanism‐based read‐across that enables hazard characterisation of skin sensitisation potential. An illustration of the types of quantitative mechanistic models that could be built using databases of kinetic measures of reactivity, hydrophobicity and existing in vivo bioassay data is also given. Copyright © 2007 John Wiley & Sons, Ltd.