(Q)SAR tools for priority setting: A case study with printed paper and board food contact material substances

被引:19
作者
Van Bossuyt, Melissa [1 ,2 ]
Van Hoeck, Els [1 ]
Raitano, Giuseppa [3 ]
Manganelli, Serena [3 ]
Braeken, Els [4 ]
Ates, Gamze [2 ]
Vanhaecke, Tamara [2 ]
Van Miert, Sabine [4 ]
Benfenati, Emilio [3 ]
Mertens, Birgit [1 ]
Rogiers, Vera [2 ]
机构
[1] Sci Inst Publ Hlth, Dept Food Med & Consumer Safety, Juliette Wytsmanstr 14, Brussels, Belgium
[2] Vrije Univ Brussel, Dept Vitro Toxicol & Dermatocosmetol, Laarbeeklaan 103, Brussels, Belgium
[3] Ist Ric Farmacol Mario Negri, Dept Environm Hlth Sci, Via Giuseppe La Masa 19, Milan, Italy
[4] Thomas More Kempen, Kleinhoefstr 4, Geel, Belgium
关键词
(Q)SAR; Mutagenicity; Prioritization; Food contact materials; Alternative methods; QSAR MODELS; MUTAGENICITY; PREDICTION;
D O I
10.1016/j.fct.2017.02.002
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
Over the last years, more stringent safety requirements for an increasing number of chemicals across many regulatory fields (e.g. industrial chemicals, pharmaceuticals, food, cosmetics,...) have triggered the need for an efficient screening strategy to prioritize the substances of highest Concern. In this context, alternative methods such as in silica (i.e. computational) techniques gain more and more importance. In the current study, a new prioritization strategy for identifying potentially mutagenic substances was developed based on the combination of multiple (quantitative) structure-activity relationship ((Q)SAR) tools. Non-evaluated substances used in printed paper and board food contact materials (FCM) were selected for a case study. By applying our strategy, 106 out of the 1723 substances were assigned 'high priority' as they were predicted mutagenic by 4 different (Q)SAR models. Information provided within the models allowed to identify 53 substances for which Ames mutagenicity prediction already has in vitro Ames test results. For further prioritization, additional support could be obtained by applying local i.e. specific models, as demonstrated here for aromatic azo compounds, typically found in printed paper and board FCM. The strategy developed here can easily be applied to other groups of chemicals facing the same need for priority ranking. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:109 / 119
页数:11
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