使用三种QSAR工具对二胺杂质的基于风险的基变异性评估
Srinivas Birudukota1,2, Bhaskar Mangalapu1,3, Ramesha Andagar Ramakrishna3
1Department of Chemistry, School of Applied Sciences, Rukmini Knowledge Park, REVA University, Kattigenahalli, Yelahanka, Bangalore 560064, India.
计算 (Q) SAR模型有效地预测了二胺杂质的突变性,减少了对动物的测试. TOXTREE显示出高灵敏度和准确性,有助于早期评估这些精神活性药物污染物的风险.
科学领域:
- 计算毒理学计算毒理学
- 药品化学 药品化学 是一个
- 药物安全性评估 药物安全性评估
背景情况:
- 二,常见的精神活性药物,可以含有合成的突变性杂质.
- 由于动物试验要求,传统的突变性测试是缓慢的,昂贵的,并且在伦理上存在问题.
研究的目的:
- 使用in silico (Q) SAR模型评估88种与二相关的杂质的突变致病潜力.
- 为了比较自由可访问 (Q) SAR工具的性能,以预测突变性.
主要方法:
- 使用了三个 (Q) SAR工具:TOXTREE,毒性估计软件工具 (TEST) 和VEGA.
- 使用已知Ames测试结果的99种化学品数据集验证的工具.
- 评估了88种二胺杂质,并对其致变性风险进行了分类.
主要成果:
- 托克斯显示出最高的灵敏度 (80.7%) 和精度 (72.2%).
- VEGA和TEST提供了平衡的准确性 (66.2-66.7%) 和高特异性 (74.5-76.6%).
- 风险评估确定了21种杂质具有高风险,11种杂质具有中等至高风险.
结论:
- 多个 (Q) SAR 工具的整合有助于在药物杂质中早期检测致变性.
- 计算方法减少了对动物试验的依赖,并支持监管合规.
- 建议进一步改进模型,以提高风险评估的准确性.
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