...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>The Journal of toxicological sciences >Development of QSAR models using artificial neural network analysis for risk assessment of repeated-dose, reproductive, and developmental toxicities of cosmetic ingredients
【24h】

Development of QSAR models using artificial neural network analysis for risk assessment of repeated-dose, reproductive, and developmental toxicities of cosmetic ingredients

机译：使用人工神经网络分析开发QSAR模型，以评估化妆品成分的重复剂量，生殖和发育毒性

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Use of laboratory animals for systemic toxicity testing is subject to strong ethical and regulatory constraints, but few alternatives are yet available. One possible approach to predict systemic toxicity of chemicals in the absence of experimental data is quantitative structure-activity relationship (QSAR) analysis. Here, we present QSAR models for prediction of maximum "no observed effect level" (NOEL) for repeated-dose, developmental and reproductive toxicities. NOEL values of 421 chemicals for repeated-dose toxicity, 315 for reproductive toxicity, and 156 for developmental toxicity were collected from Japan Existing Chemical Data Base (JECDB). Descriptors to predict toxicity were selected based on molecular orbital (MO) calculations, and QSAR models employing multiple independent descriptors as the input layer of an artificial neural network (ANN) were constructed to predict NOEL values. Robustness of the models was indicated by the root-mean-square (RMS) errors after 10-fold cross-validation (0.529 for repeated-dose, 0.508 for reproductive, and 0.558 for developmental toxicity). Evaluation of the models in terms of the percentages of predicted NOELs falling within factors of 2, 5 and 10 of the in-vivo-determined NOELs suggested that the model is applicable to both general chemicals and the subset of chemicals listed in International Nomenclature of Cosmetic Ingredients (INCI). Our results indicate that ANN models using in silico parameters have useful predictive performance, and should contribute to integrated risk assessment of systemic toxicity using a weight-of-evidence approach. Availability of predicted NOELs will allow calculation of the margin of safety, as recommended by the Scientific Committee on Consumer Safety (SCCS).
机译：使用实验室动物进行全身毒性测试受到严格的道德和法规约束，但目前尚无其他选择。在缺乏实验数据的情况下，预测化学品的系统毒性的一种可能方法是定量结构-活性关系（QSAR）分析。在这里，我们提出了QSAR模型，用于预测重复剂量，发育和生殖毒性的最大“无观察到的作用水平”（NOEL）。从日本现有化学数据库（JECDB）收集了421种重复剂量毒性化学物质，315种生殖毒性化学物质和156种发育毒性化学物质的NOEL值。基于分子轨道（MO）计算选择预测毒性的描述符，并构建采用多个独立描述符作为人工神经网络（ANN）输入层的QSAR模型来预测NOEL值。模型的稳健性由10倍交叉验证后的均方根（RMS）误差表示（重复剂量为0.529，生殖剂量为0.508，发育毒性为0.558）。根据预测的NOEL百分比在体内确定的NOEL的2、5和10因子之内对模型进行评估，表明该模型既适用于一般化学品，也适用于《国际化妆品命名法》中列出的化学品子集成分（INCI）。我们的结果表明，使用计算机模拟参数的ANN模型具有有用的预测性能，并且应该使用证据权重方法有助于系统毒性的综合风险评估。根据消费者安全科学委员会（SCCS）的建议，预测NOEL的可用性将可以计算安全系数。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号