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香豆素在化妆品中的假设性皮肤致敏下一代风险评估。

A hypothetical skin sensitisation next generation risk assessment for coumarin in cosmetic products.

机构信息

Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire, MK44 1LQ, UK.

Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire, MK44 1LQ, UK.

出版信息

Regul Toxicol Pharmacol. 2021 Dec;127:105075. doi: 10.1016/j.yrtph.2021.105075. Epub 2021 Oct 30.

Abstract

Next generation Risk Assessment (NGRA) is an exposure-led, hypothesis-driven approach which integrates new approach methodologies (NAMs) to assure safety without generating animal data. This hypothetical skin allergy risk assessment of two consumer products - face cream containing 0.1% coumarin and deodorant containing 1% coumarin - demonstrates the application of our skin allergy NGRA framework which incorporates our Skin Allergy Risk Assessment (SARA) Model. SARA uses Bayesian statistics to provide a human relevant point of departure and risk metric for a given chemical exposure based upon input data that can include both NAMs and historical in vivo studies. Regardless of whether NAM or in vivo inputs were used, the model predicted that the face cream and deodorant exposures were low and high risk respectively. Using only NAM data resulted in a minor underestimation of risk relative to in vivo. Coumarin is a predicted pro-hapten and consequently, when applying this mechanistic understanding to the selection of NAMs the discordance in relative risk could be minimized. This case study demonstrates how integrating a computational model and generating bespoke NAM data in a weight of evidence framework can build confidence in safety decision making.

摘要

下一代风险评估(NGRA)是一种以暴露为导向、以假设为驱动的方法,它整合了新方法(NAMs),在不产生动物数据的情况下确保安全性。对两种消费品——含有 0.1%香豆素的面霜和含有 1%香豆素的除臭剂——的假设性皮肤过敏风险评估,展示了我们皮肤过敏 NGRA 框架的应用,该框架纳入了我们的皮肤过敏风险评估(SARA)模型。SARA 使用贝叶斯统计方法,根据输入数据,为特定化学暴露提供了人类相关的起始点和风险指标,这些输入数据既可以包括 NAMs,也可以包括体内研究。无论使用 NAM 还是体内数据,该模型均预测面霜和除臭剂的暴露风险分别为低风险和高风险。仅使用 NAM 数据会导致风险的轻微低估,相对于体内数据。香豆素是一种预测性原半抗原,因此,当将这种机制理解应用于 NAMs 的选择时,可以最小化相对风险的差异。本案例研究展示了如何在证据权重框架中整合计算模型和生成定制的 NAM 数据,从而增强对安全性决策的信心。

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