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Rapid Experimental Measurements of Physicochemical Properties to Inform Models and Testing

机译:理化性质的快速实验测量以告知模型和测试

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摘要

The structures and physicochemical properties of chemicals are important for determining their potential toxicological effects, toxicokinetics, and route(s) of exposure. These data are needed to prioritize the risk for thousands of environmental chemicals, but experimental values are often lacking. In an attempt to efficiently fill data gaps in physicochemical property information, we generated new data for 200 structurally diverse compounds, which were rigorously selected from the USEPA Distributed Structure-Searchable Toxicity Database (DSSTox). This pilot study evaluated rapid experimental methods to determine five physicochemical properties including the log of the octanol:water partition coefficient (known as log(Kow) or logP), vapor pressure, water solubility, Henry’s law constant, and the acid dissociation constant (pKa). For most compounds experiments were successful for at least one property; log(Kow) yielded the largest return (176 values). It was determined that the presence of 21 structural features may have played an overall role in rapid measurement method failures. To gauge consistency with traditional measurement methods, the new measurements were compared with previous measurements (where available). Since quantitative structure-activity/property relationship (QSAR/QSPR) models are used to fill gaps in physicochemical property information, 5 suites of QSPRs were evaluated for their predictive ability and chemical coverage or applicability domain of new experimental measurements. The ability to have accurate measurements of these properties will facilitate better exposure predictions in two ways: 1) direct input of these experimental measurements into exposure models; and 2) construction of QSPRs with a wider applicability domain, as their predicted physicochemical values can be used to parameterize exposure models in the absence of experimental data.
机译:化学品的结构和理化性质对于确定其潜在的毒理作用,毒物动力学和暴露途径很重要。需要这些数据来确定数千种环境化学品的风险的优先级,但是通常缺乏实验值。为了有效填补物理化学性质信息中的数据空白,我们针对200种结构多样的化合物生成了新数据,这些化合物是严格选自USEPA分布式结构可搜索毒性数据库(DSSTox)。这项前期研究评估了快速实验方法,以确定五种物理化学性质,包括辛醇的对数:水分配系数(称为log(Kow)或logP),蒸气压,水溶性,亨利定律常数和酸解离常数(pKa) )。对于大多数化合物而言,至少一项特性的实验是成功的。 log(Kow)产生最大的回报(176个值)。已确定21个结构特征的存在可能在快速测量方法失败中起了总体作用。为了衡量与传统测量方法的一致性,将新测量值与以前的测量值(如果有)进行了比较。由于使用了定量构效关系(QSAR / QSPR)模型来填补理化性质信息中的空白,因此对5套QSPR的预测能力和化学覆盖率或新实验测量的适用范围进行了评估。对这些特性进行精确测量的能力将通过两种方式促进更好的暴露预测:1)将这些实验测量值直接输入暴露模型中; 2)构建具有更广泛适用范围的QSPR,因为在没有实验数据的情况下,其预测的理化值可用于参数化暴露模型。

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