支持化妆品相关材料安全性评估的硅内毒理学方法综述

IF 3.1 Q2 TOXICOLOGY
Mark T.D. Cronin , Steven J. Enoch , Judith C. Madden , James F. Rathman , Andrea-Nicole Richarz , Chihae Yang
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引用次数: 15

摘要

计算机工具和资源现在普遍用于毒理学和支持化妆品成分或材料的“下一代风险评估”(NGRA)。本综述概述了用于评估化妆品成分暴露和危害的方法。对于危害和暴露,常规使用现有信息的数据库。此外,对于暴露,计算机方法包括使用系统生物利用度的经验法则以及基于生理的动力学(PBK)和用于估计器官或组织水平的内部暴露的多尺度模型。(内部)毒理学关注阈值适用于低浓度成分的安全性评估。使用结构规则、(定量)结构-活性关系(Q - sar)和跨读是预测危险最典型的建模方法。NGRA越来越多地将暴露数据和危害评估数据结合起来,对化妆品成分的安全性进行全面评估。所有的计算机方法都在其成熟度和健壮性方面进行了审查。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A review of in silico toxicology approaches to support the safety assessment of cosmetics-related materials

In silico tools and resources are now used commonly in toxicology and to support the “Next Generation Risk Assessment” (NGRA) of cosmetics ingredients or materials. This review provides an overview of the approaches that are applied to assess the exposure and hazard of a cosmetic ingredient. For both hazard and exposure, databases of existing information are used routinely. In addition, for exposure, in silico approaches include the use of rules of thumb for systemic bioavailability as well as physiologically-based kinetics (PBK) and multi-scale models for estimating internal exposure at the organ or tissue level. (Internal) Thresholds of Toxicological Concern are applicable for the safety assessment of ingredients at low concentrations. The use of structural rules, (Quantitative) Structure-Activity Relationships ((Q)SARs) and read-across are the most typically applied modelling approaches to predict hazard. Data from exposure and hazard assessment are increasingly being brought together in NGRA to provide an overall assessment of the safety of a cosmetic ingredient. All in silico approaches are reviewed in terms of their maturity and robustness for use.

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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
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