毒性杂质的预测研究策略

Prediction strategy of toxic impurities

对药物中杂质化合物的毒性预测研究方法包括警示结构、Ames试验、计算机辅助预测方法和斑马鱼快速评价方法等。具有警示结构的化合物主要有芳香族化合物、烷烃和环烷烃化合物及杂原子化合物等;具有基因毒性的化合物在Ames试验中会使鼠伤寒沙门氏细菌产生回复突变,从而有可见菌落产生;计算机辅助预测包括定量构效关系(Quantity Structure-Activity Relationship,QSAR)和数据库软件系统的使用,都是以化合物化学结构、分子结构参数和化合物的生物活性或效应之间关系为基础,通过QSAR模型或TOPKAT,TOXNET等毒理学数据库而预测杂质化合物的毒理学特性;斑马鱼模型有助于快速实现高通量和高内涵药物筛选。使用警示结构、Ames试验、计算机辅助预测及斑马鱼模型对杂质的毒性进行预测评估,可以节约药物研发成本,缩短研发周期,将会在药物研发领域发挥越来越重要的作用。

The methods to predict the toxicity of impurity compounds include structure alerts, computer-ai- ded methods and zebrafish rapid assessment methods. Structure alerts mainly conclude aromatic compounds, al- kanes, cycloalkanes compounds and heteroatom compounds; Salmonella typhimurium＇s reverse mutation appear in the Ames test because of genotoxic compounds, and the visible colonies were produced; Computer-aided methods include QSAR （Quantity Structure-Activity Relationship, QSAR） and database software systems, both of which are based on the chemical structure, the molecular structure parameters and biological activity or effects of the com- pounds, and predict toxicological properties of impurity compounds model by means of QSAR models, TOPKAT, and TOXNET database. Zebrafish model helps quickly achieve high throughput and high content screening. Struc- ture alerts, Ames test, computer-aided prediction and zebrafish models do benefit to the prediction and assessment of impurities with toxicity, which can shorten the drug development cycle and will play an increasingly important role in the research and development of drugs.