生物信息学
离体
皮肤致敏
不良结局途径
范围(计算机科学)
生化工程
计算模型
计算机科学
毒性
局部淋巴结试验
动物模型
计算生物学
动物试验
预测建模
机器学习
体内
敏化
人工智能
生物
生物技术
医学
生物化学
工程类
内科学
生态学
免疫学
程序设计语言
基因
作者
Priyanka Banerjee,Özge Cemiloğlu Ülker
标识
DOI:10.1080/15376516.2022.2053623
摘要
Human data on remains sparse and of varying quality and reproducibility. Ex vivo experiments and animal experiments currently is the most preferred way to predict the skin sensitization approved by the regulatory agencies across the world. However, there is a constant need and demand to reduce animal experiments and provide the scope of alternative methods to animal testing. In this study, we have compared the predictive performance of the published computational tools such as ProTox-II, SuperCYPsPred with the data obtained from ex-vivo experiments. From the results of the retrospective analysis, it can be observed that the computational predictions are in agreement with the experimental results. The computational models used here are generative models based on molecular structures and machine learning algorithms and can be applied also for the prediction of skin sensitization. Besides prediction of the toxicity endpoints, the models can also provide deeper insights into the molecular mechanisms and adverse outcome pathways (AOPs) associated with the chemicals used in cosmetic products.
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