Deriving a no expected sensitization induction level for fragrance ingredients without animal testing: An integrated approach applied to specific case studies.

TOXICOLOGICAL SCIENCES(2018)

引用 21|浏览4
暂无评分
摘要
Cosmetic regulations prohibit animal testing for the purpose of safety assessment and recent registration, evaluation and authorization of chemicals guidance states that the local lymph node assay (LLNA) in mice shall only be conducted if in vitro data cannot give sufficient information for classification and labeling. However, Quantitative Risk Assessment for fragrance ingredients requires an NESIL (no expected sensitization induction level), a dose not expected to cause induction of skin sensitization in humans. In absence of human data, this is derived from the LLNA and it remains a key challenge for risk assessors to derive this value from nonanimal data. Here we present a workflow using structural information, reactivity data and KeratinoSens results to predict an LLNA result as a point of departure. Specific additional tests (metabolic activation, complementary reactivity tests) are applied in selected cases depending on the chemical domain of a molecule. Finally, in vitro and in vivo data on close analogues are used to estimate uncertainty of the prediction in the specific chemical domain. This approach was applied to three molecules which were subsequently tested in the LLNA and 22 molecules with available and sometimes discordant human and LLNA data. Four additional case studies illustrate how this approach is being applied to recently developed molecules in the absence of animal data. Estimation of uncertainty and how this can be applied to determine a final NESIL for risk assessment is discussed. We conclude that, in the data-rich domain of fragrance ingredients, sensitization risk assessment without animal testing is possible in most cases by this integrated approach.
更多
查看译文
关键词
skin sensitization,risk assessment,alternative methods,QRA,point of departure
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要