Risk Assessment Division, Office of Pollution Prevention and Toxics, Office of Chemical Safety and Pollution Prevention, U.S. Environmental Protection Agency, Washington, DC, USA; ORISE Postdoctoral Research Fellow, National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA; The Hamner Institutes for Health Sciences, Research Triangle Park, NC, USA.
ORISE Postdoctoral Research Fellow, National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA; ScitoVation, 6 Davis Drive, PO Box 110566, Research Triangle Park, NC, USA.
Sci Total Environ. 2018 Feb 15;615:150-160. doi: 10.1016/j.scitotenv.2017.09.033. Epub 2017 Sep 29.
Chemical ionization plays an important role in many aspects of pharmacokinetic (PK) processes such as protein binding, tissue partitioning, and apparent volume of distribution at steady state (Vd). Here, estimates of ionization equilibrium constants (i.e., pK) were analyzed for 8132 pharmaceuticals and 24,281 other compounds to which humans might be exposed in the environment. Results revealed broad differences in the ionization of pharmaceutical chemicals and chemicals with either near-field (in the home) or far-field sources. The utility of these high-throughput ionization predictions was evaluated via a case-study of predicted PK Vd for 22 compounds monitored in the blood and serum of the U.S. population by the U.S. Centers for Disease Control and Prevention National Health and Nutrition Examination Survey (NHANES). The chemical distribution ratio between water and tissue was estimated using predicted ionization states characterized by pK. Probability distributions corresponding to ionizable atom types (IATs) were then used to analyze the sensitivity of predicted Vd on predicted pK using Monte Carlo methods. 8 of the 22 compounds were predicted to be ionizable. For 5 of the 8 the predictions based upon ionization are significantly different from what would be predicted for a neutral compound. For all but one (foramsulfuron), the probability distribution of predicted Vd generated by IAT sensitivity analysis spans both the neutral prediction and the prediction using ionization. As new data sets of chemical-specific information on metabolism and excretion for hundreds of chemicals are being made available (e.g., Wetmore et al., 2015), high-throughput methods for calculating Vd and tissue-specific PK distribution coefficients will allow the rapid construction of PK models to provide context for both biomonitoring data and high-throughput toxicity screening studies such as Tox21 and ToxCast.
化学电离在药代动力学（PK）过程的许多方面发挥着重要作用，如蛋白质结合、组织分配和稳态表观分布体积（Vd）。在这里，分析了 8132 种药物和 24281 种其他化合物的电离平衡常数（即 pK）估计值，这些药物和化合物是人类在环境中可能接触到的。结果表明，药物化学物质和具有近场（在家中）或远场源的化学物质的电离有很大差异。这些高通量电离预测的实用性通过对美国疾病控制与预防中心（CDC）国家健康和营养检查调查（NHANES）监测的 22 种化合物的预测 PK Vd 的案例研究进行了评估。使用特征为 pK 的预测电离态估计水和组织之间的化学分配比。然后，使用概率分布对应于可电离原子类型（IAT）来分析使用蒙特卡罗方法预测的 pK 对预测 Vd 的敏感性。在 22 种化合物中有 8 种被预测为可电离的。对于 8 种中的 5 种，基于电离的预测与中性化合物的预测有显著差异。对于除一个（foramsulfuron）之外的所有化合物，通过 IAT 敏感性分析生成的预测 Vd 的概率分布都跨越了中性预测和使用电离的预测。随着越来越多的关于数百种化学物质代谢和排泄的化学特异性信息数据集的出现（例如，Wetmore 等人，2015），用于计算 Vd 和组织特异性 PK 分布系数的高通量方法将允许快速构建 PK 模型，为生物监测数据和高通量毒性筛选研究（如 Tox21 和 ToxCast）提供背景。
