根据作用方式区分毒物类别:地理物理化学描述符

M. Nendza, Martin Müller
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引用次数: 47

摘要

具有共同毒性作用模式(MOA)的环境污染物通常具有相似的结构和/或物理化学性质。通过MOA计算出的亲脂性、电子性和位阻性描述符,将115种测试化学品归类为9种不同的毒物类别(非极性非特异性毒物、极性非特异性毒物、氧化磷酸化解偶联剂、光合作用抑制剂、乙酰胆碱酯酶抑制剂、呼吸抑制剂、硫醇烷基化剂、反应性(刺激物)、雌激素化合物)。逐步判别分析的结果表明,89.6%的测试化学品被正确分类为MOA类。最终模型使用10个显著变量(log KOW, eHOMO, V+, QAV, HMAX+, MR, MW, DEFF, SASA, SAVOL)。同一数据集的PLS判别分析结果为三成分模型,r=0.89;区分力最大的变量是log KOW、HMAX+、DEFF和QAV。每个MOA类都显示出其物理化学性质的特征。相对于非特异性基线毒物的偏差对于每一类MOA都是特定的,反映了导致各自毒性的限速相互作用的结构依赖性(功能相似性)。通过结合对潜在过程的生理和化学知识,MOA有可能指出基于描述符的区分标准,作为合理选择和应用过程相关QSARS的必要前提。
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Discriminating Toxicant Classes by Mode of Action: 2. Physico‐Chemical Descriptors
Environmental contaminants with common mode of toxic action (MOA) are generally expected to have similar structures and/or physico-chemical properties. Calculated descriptors of lipophilic, electronic and steric properties were used to cluster 115 test chemicals by MOA into nine different toxicant classes (non-polar non-specific toxicants, polar non-specific toxicants, uncouplers of oxidative phosphorylation, inhibitors of photosynthesis, inhibitors of acetylcholinesterase, inhibitors of respiration, thiol-alkylating agents, reactives (irritants), estrogenic compounds). Stepwise discriminant analysis of the test chemicals resulted in 89.6% correct classifications into the MOA classes. The final model uses 10 significant variables (log KOW, eHOMO, V+, QAV, HMAX+, MR, MW, DEFF, SASA, SAVOL). PLS discriminant analysis of the same data set resulted in a three-component model with r=0.89; the variables with the highest discriminatory power are log KOW, HMAX+, DEFF and QAV. Each MOA class reveals a characteristic profile in physico-chemical properties. Deviations relative to non-specific baseline toxicants are specific for each MOA class and reflect the structural dependences of the rate-limiting interactions that are causing the respective toxicities (functional similarity). By combining physiological and chemical knowledge about underlying processes, it is possible to indicate descriptor-based discrimination criteria by MOA as an essential prerequisite for rational selection and application of process-related QSARS for predictive purposes.
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