國家衛生研究院 NHRI:Item 3990099045/14574
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    題名: SkinSensPred as a promising in silico tool for integrated testing strategy on skin sensitization
    作者: Wang, SS;Wang, CC;Tung, CW
    貢獻者: Institute of Biotechnology and Pharmaceutical Research
    摘要: Skin sensitization is an important regulatory endpoint associated with allergic contact dermatitis. Recently, several adverse outcome pathway (AOP)-based alternative methods were developed to replace animal testing for evaluating skin sensitizers. The AOP-based assays were further integrated as a two-out-of-three method with good predictivity. However, the acquisition of experimental data is resource-intensive. In contrast, an integrated testing strategy (ITS) capable of maximizing the usage of laboratory data from AOP-based and in silico methods was developed as defined approaches (DAs) to both hazard and potency assessment. There are currently two in silico models, namely Derek Nexus and OECD QSAR Toolbox, evaluated in the OECD Testing Guideline No. 497. Since more advanced machine learning algorithms have been proposed for skin sensitization prediction, it is therefore desirable to evaluate their performance under the ITS framework. This study evaluated the performance of a new ITS DA (ITS-SkinSensPred) adopting a transfer learning-based SkinSensPred model. Results showed that the ITS-SkinSensPred has similar or slightly better performance compared to the other ITS models. SkinSensPred-based ITS is expected to be a promising method for assessing skin sensitization.
    日期: 2022-11-03
    關聯: International Journal of Environmental Research and Public Health. 2022 Nov 3;19(19):Article number 12856.
    Link to: http://dx.doi.org/10.3390/ijerph191912856
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000866780900001
    Cited Times(Scopus): https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85139811778
    顯示於類別:[童俊維] 期刊論文

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