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相关概念视频

Mutagenicity and Carcinogenicity01:25

Mutagenicity and Carcinogenicity

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Mutagenicity and carcinogenicity refer to the ability of drugs to cause genetic defects and induce cancer, respectively. The International Agency for Research on Cancer (IARC) classifies agents into four groups based on their carcinogenic potential. Group 1 agents are known human carcinogens; group 2A agents are probably carcinogenic to humans; group 3 agents lack data to support their role in carcinogenesis; and group 4 includes agents for which data support that they are not likely to be...
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多类ARKA框架用于开发改善的q-RASAR模型,用于环境毒性终点.

Arkaprava Banerjee1, Kunal Roy1

  • 1Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India. arka.banerjee16@gmail.com.

Environmental science. Processes & impacts
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PubMed
概括

本研究介绍了ARKA-RASAR,这是一个改进的化学毒性预测工作流程,增强了定量结构-活性关系 (QSAR) 模型. 新方法为填补关键毒性数据缺口提供了更准确的预测.

科学领域:

  • 计算毒理学和化学信息学
  • 环境科学和风险评估
  • 定量结构-活动关系 (QSAR) 建模.

背景情况:

  • 准确的化学毒性数据对于监管和安全评估至关重要.
  • 现有的定量结构-活动关系 (QSAR) 和定量跨读结构-活动关系 (q-RASAR) 模型在预测准确性和交叉验证方面存在局限性.
  • 持续需要改进的建模策略,以有效地填补毒性数据缺口.

研究的目的:

  • 通过整合K组分析中的算术余数 (ARKA) 框架,开发一个改进的q-RASAR工作流,称为ARKA-RASAR.
  • 提高商业化学品毒性预测的准确性和可靠性.
  • 为计算新型描述符和开发强大的预测模型提供一个用户友好的工具.

主要方法:

  • 开发了ARKA-RASAR工作流程,将QSAR描述符和ARKA描述符结合起来,以确定化学同源之间的相似性.
  • 利用五个不同的毒性数据集进行模型开发和与现有的QSAR和q-RASAR模型进行比较.
  • 采用基于Java的工具,多类ARKA-v1.0,用于计算多类ARKA描述符,并开发混合ARKA-RASAR模型.

主要成果:

  • 与传统的QSAR和q-RASAR模型相比,ARKA-RASAR模型显示出更高的性能,通过排名差异总和 (SRD) 分析进行验证.

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  • 工作流显示了强大的培训,测试和交叉验证统计数据,其中显著差异得到了最不显著差异程序的证实.
  • 对农药代谢物的外部验证,用于急性鱼类毒性预测,产生了令人鼓舞和准确的结果.

    • 结论:

    • 阿尔卡-拉萨尔模型框架在预测化学毒性方面取得了重大进展,解决了以前方法的局限性.
    • 开发的工作流程是直接的,可重复的,可转移的,使其在环境毒性评估中更容易采用.
    • 阿尔卡-拉萨尔提出了一种有希望的方法,用于生成高度稳健和预测模型,以填补环境毒性数据中的关键缺口.