Mechanistic applicability domain classification of a local lymph node assay dataset for skin sensitization

被引:1340
作者
Roberts, David W.
Patlewicz, Grace
Kern, Petra S.
Gerberick, Frank
Kimber, Ian
Dearman, Rebecca J.
Ryan, Cindy A.
Basketter, David A.
Aptula, Aynur O. [1 ]
机构
[1] Unilever, SEAC, Sharnbrook, Beds, England
[2] Liverpool John Moores Univ, Sch Pharm & Chem, Liverpool L3 5UX, Merseyside, England
[3] Commiss European Communities, Joint Res Ctr, IHCP, European Chem Bur TP582, I-21020 Ispra, VA, Italy
[4] Procter & Gamble Eurocor, Brussels Innovat Ctr, Strombeek Bever, Belgium
[5] Procter & Gamble Co, Miami Valley Innovat Ctr, Cincinnati, OH USA
[6] Syngenta Cent Toxicol Lab, Macclesfield, Cheshire, England
关键词
D O I
10.1021/tx700024w
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
The goal of eliminating animal testing in the predictive identification of chemicals with the intrinsic ability to cause skin sensitization is an important target, the attainment of which has recently been brought into even sharper relief by the EU Cosmetics Directive and the requirements of the REACH legislation. Development of alternative methods requires that the chemicals used to evaluate and validate novel approaches comprise not only confirmed skin sensitizers and non-sensitizers but also substances that span the full chemical mechanistic spectrum associated with skin sensitization. To this end, a recently published database of more than 200 chemicals tested in the mouse local lymph node assay (LLNA) has been examined in relation to various chemical reaction mechanistic domains known to be associated with sensitization. It is demonstrated here that the dataset does cover the main reaction mechanistic domains. In addition, it is shown that assignment to a reaction mechanistic domain is a critical first step in a strategic approach to understanding, ultimately on a quantitative basis, how chemical properties influence the potency of skin sensitizing chemicals. This understanding is necessary if reliable non-animal approaches, including (quantitative) structure-activity relationships (Q)SARs, read-across, and experimental chemistry based models, are to be developed.
引用
收藏
页码:1019 / 1030
页数:12
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