seh consulting + services, Stembergring 15, 33106, Paderborn, Germany.
L'Oréal, Research & Innovation, Aulnay-sous-Bois, France.
Regul Toxicol Pharmacol. 2022 Jun;131:105169. doi: 10.1016/j.yrtph.2022.105169. Epub 2022 Apr 18.
The assessment of skin sensitisation is a key requirement in all regulated sectors, with the European Union's regulation of cosmetic ingredients being most challenging, since it requires quantitative skin sensitisation assessment based on new approach methodologies (NAMs). To address this challenge, an in-depth and harmonised understanding of NAMs is fundamental to inform the assessment. Therefore, we compiled a database of NAMs, and in vivo (human and local lymph node assay) reference data. Here, we expanded this database with 41 substances highly relevant for cosmetic industry. These structurally different substances were tested in six NAMs (Direct Peptide Reactivity Assay, KeratinoSens™, human Cell Line Activation Test, U-SENS™, SENS-IS, Peroxidase Peptide Reactivity Assay). Our analysis revealed that the substances could be tested without technical limitations, but were generally overpredicted when compared to reference results. Reasons for this reduced predictivity were explored through pairwise NAM comparisons and association of overprediction with hydrophobicity. We conclude that more detailed understanding of how NAMs apply to a wider range of substances is needed. This would support a flexible and informed choice of NAMs to be optimally applied in the context of a next generation risk assessment framework, ultimately contributing to the characterisation and reduction of uncertainty.
皮肤致敏评估是所有受监管领域的关键要求,其中欧盟对化妆品成分的监管最具挑战性,因为它需要基于新方法学(NAMs)进行定量皮肤致敏评估。为了应对这一挑战,深入而协调一致地了解 NAMs 对于提供评估信息至关重要。因此,我们编制了一个 NAMs 数据库,以及体内(人体和局部淋巴结测定)参考数据。在这里,我们用 41 种对化妆品行业非常重要的物质来扩展这个数据库。这些结构不同的物质在六种 NAMs 中进行了测试(直接肽反应性测定、KeratinoSens™、人细胞系激活试验、U-SENS™、SENS-IS、过氧化物酶肽反应性测定)。我们的分析表明,这些物质可以在没有技术限制的情况下进行测试,但与参考结果相比,通常被过度预测。通过 NAMs 两两比较和过度预测与疏水性的关联,我们探讨了导致这种预测能力降低的原因。我们的结论是,需要更详细地了解 NAMs 如何适用于更广泛的物质,以便在下一代风险评估框架中灵活和明智地选择 NAMs,最终有助于对不确定性进行特征描述和降低。