Utilizing integrated testing strategy (ITSv1) defined approach and read across to predict skin sensitization of cannabidiol

IF 3.1 Q2 TOXICOLOGY
Ramez Labib, Ripal Amin, Chris Bartlett, Lisa Hoffman
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引用次数: 0

Abstract

Cannabidiol (CBD) is increasingly being used as an ingredient in cosmetics, but to date no pre-clinical studies have been published to address the skin sensitization end point. This case study investigated its skin sensitization potential for use in a face cream application at 0.3 % using Next Generation Risk Assessment (NGRA) framework. Based on chemical structure and in-silico prediction using Derek Nexus, CBD was predicted to be weak sensitizer with a resorcinol alert moiety. In vitro testing was conducted confirming it to be sensitizer, but the New Approach Methodologies (NAM) data could not provide sufficient confidence to determine a point of departure (PoD). Integrated testing strategy (ITS)v1 Defined Approach (DA), adopted in OECD Guideline No. 497, was used for skin sensitization potency categorization. However, ITSv1 DA alone is not used for further refinement of the potency prediction based on EC3 (the estimated concentration that produces a stimulation index of 3 in murine local lymph node assay) values. Therefore, the application of read-across using Derek Nexus derived a PoD derived from the LLNA EC3 of 42 %. This led to a favorable NGRA conclusion and to support use of CBD at 0.3 % in face cream application.

利用综合测试战略(ITSv1)定义的方法和横向读数预测大麻二酚的皮肤致敏性
大麻二酚(CBD)正越来越多地被用作化妆品成分,但迄今为止,尚未有针对皮肤过敏终点的临床前研究发表。本案例研究采用下一代风险评估(NGRA)框架,研究了在面霜中使用 0.3% 的大麻二酚对皮肤的致敏潜力。根据化学结构和使用 Derek Nexus 进行的室内预测,CBD 被预测为具有间苯二酚警戒分子的弱致敏剂。体外测试证实它是敏化剂,但新方法(NAM)数据无法提供足够的可信度来确定出发点(PoD)。综合测试策略(ITS)v1 定义方法(DA)已在经合组织第 497 号准则中采用,用于皮肤过敏效力分类。不过,仅使用 ITSv1 DA 并不能根据 EC3(在小鼠局部淋巴结试验中产生 3 级刺激指数的估计浓度)值进一步完善药效预测。因此,使用 Derek Nexus 进行交叉分析后,根据 LLNA EC3 得出的 PoD 为 42%。这就得出了有利的 NGRA 结论,并支持在面霜中使用 0.3 % 的 CBD。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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