在化妆品中使用的化学品的危害评估中评估人类健康毒性、生态毒性和毒性动力学特征的计算机工具概述。

IF 3.8 3区 医学 Q2 CHEMISTRY, MEDICINAL
Pauline Lancia, Myriam Louazzani, Ludivine Gros, José Ginestar, Elena Fioravanzo, Aurélie Baleydier
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引用次数: 0

摘要

多年来,动物试验替代方法的发展势头强劲,其中包括更快、更具成本效益的计算机方法的快速增长。大量的计算工具已经发表,重点是Read-Across,(定量)结构-活性关系((Q)SAR)模型,以及基于生理的药代动力学(PBPK)模型。这些方法在化妆品的风险评估中起着至关重要的作用。然而,尽管各工作组不断努力,但由于其开发和应用缺乏标准化和透明度,这些方法并不总是被世界各地的监管机构所接受。本研究旨在识别可预测化妆品成分危害评估相关关键特性的计算机工具,旨在简化决策并协助毒理学家有效地选择和整合计算机预测。基于其预测能力确定了84种计算机工具,包括物理化学参数、毒理学/生态毒理学终点和使用不同计算方法的毒性动力学特性,例如(Q) sar;在。还考虑了QSAR模型的其他标准,帮助毒理学家将它们整合到风险评估过程中:(1)适用性域(AD)的定义,(2)模型性能,(3)目标物质的最近邻居。基于这些标准,这些模型被分类为对筛选有用或适合证据权重(WoE)方法。最后,这项研究强调了越来越多的计算工具可用于评估与化妆品安全相关的各种终点。工具的数量在不断增加,定期审查是必要的。对这些计算机工具的深入了解将有助于毒理学家使用它们,并提高它们在不同化妆品当局的监管目的中的接受度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Overview of In Silico Tools to Evaluate Human Health Toxicity, Ecotoxicity, and Toxicokinetic Profiles in the Hazard Assessment of Chemicals Used in Cosmetics.

The development of alternative methods to animal testing has gained momentum over the years, including the rapid growth of in silico methods, which are faster and more cost-effective. A large number of computational tools have been published, focusing on Read-Across, (quantitative) Structure-Activity Relationship ((Q)SAR) models, and Physiologically Based Pharmacokinetic (PBPK) models. All of these methods play a crucial role in the risk assessment for cosmetics. However, despite the continuous efforts of various working groups, these methods are not always accepted by regulatory authorities around the world due to a lack of standardization and transparency in their development and application. This study aimed to identify in silico tools that can predict key properties relevant to the hazard assessment of cosmetic ingredients, aiming to streamline decision-making and assist toxicologists in efficiently selecting and integrating in silico predictions. Eighty-four in silico tools were identified based on their predictive capabilities, covering physicochemical parameters, toxicological/ecotoxicological endpoints, and toxicokinetic properties using different computational methods, e.g., (Q)SARs; Read-Across. Additional criteria were also considered for QSAR models, helping toxicologists integrate them into risk assessment processes: (1) definition of the Applicability Domain (AD), (2) model performance, and (3) nearest neighbors of the target substance. Based on these criteria, the models were classified as either useful for screening or suitable for a Weight of Evidence (WoE) approach. Finally, this study highlights the growing number of computational tools available for assessing various endpoints relevant to cosmetic safety. The number of tools continues to increase, and regular reviews are necessary. A deeper understanding of these in silico tools will facilitate their use by toxicologists and improve their acceptance for regulatory purposes from different cosmetic authorities.

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来源期刊
CiteScore
7.90
自引率
7.30%
发文量
215
审稿时长
3.5 months
期刊介绍: Chemical Research in Toxicology publishes Articles, Rapid Reports, Chemical Profiles, Reviews, Perspectives, Letters to the Editor, and ToxWatch on a wide range of topics in Toxicology that inform a chemical and molecular understanding and capacity to predict biological outcomes on the basis of structures and processes. The overarching goal of activities reported in the Journal are to provide knowledge and innovative approaches needed to promote intelligent solutions for human safety and ecosystem preservation. The journal emphasizes insight concerning mechanisms of toxicity over phenomenological observations. It upholds rigorous chemical, physical and mathematical standards for characterization and application of modern techniques.
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