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通过将体外神经毒性结果纳入毒代动力学模型来预测丙二醇甲醚(PGME)的人类神经毒性。

Predicting human neurotoxicity of propylene glycol methyl ether (PGME) by implementing in vitro neurotoxicity results into toxicokinetic modelling.

机构信息

Center for Primary Care and Public Health (Unisanté), Route de la Corniche 2, 1066 Epalinges-Lausanne, Switzerland.

Swiss 3R Competence Centre, Hochschulstrasse 6, CH-3012 Bern, Switzerland; Swiss Centre for Applied Human Toxicology (SCAHT), University of Basel, Missionsstrasse 64, CH-4055 Basel, Switzerland.

出版信息

Sci Total Environ. 2023 Aug 15;886:163767. doi: 10.1016/j.scitotenv.2023.163767. Epub 2023 May 6.

Abstract

Although organic solvents have been associated with CNS toxicity, neurotoxicity testing is rarely a regulatory requirement. We propose a strategy to assess the potential neurotoxicity of organic solvents and predict solvent air concentrations that will not likely produce neurotoxicity in exposed individuals. The strategy integrated an in vitro neurotoxicity, an in vitro blood-brain barrier (BBB), and an in silico toxicokinetic (TK) model. We illustrated the concept with propylene glycol methyl ether (PGME), widely used in industrial and consumer products. The positive control was ethylene glycol methyl ether (EGME) and negative control propylene glycol butyl ether (PGBE), a supposedly non-neurotoxic glycol ether. PGME, PGBE, and EGME had high passive permeation across the BBB (permeability coefficients (P) 11.0 × 10, 9.0 × 10, and 6.0 × 10 cm/min, respectively). PGBE was the most potent in in vitro repeated neurotoxicity assays. EGME's main metabolite, methoxyacetic acid (MAA) may be responsible for the neurotoxic effects reported in humans. No-observed adverse effect concentrations (NOAECs) for the neuronal biomarker were for PGME, PGBE, and EGME 10.2, 0.07, and 79.2 mM, respectively. All tested substances elicited a concentration-dependent increase in pro-inflammatory cytokine expressions. The TK model was used for in vitro-to-in vivo extrapolation from PGME NOAEC to corresponding air concentrations (684 ppm). In conclusion, we were able to predict air concentrations that would not likely result in neurotoxicity using our strategy. We confirmed that the Swiss PGME occupational exposure limit (100 ppm) will not likely produce immediate adverse effects on brain cells. However, we cannot exclude possible long-term neurodegenerative effects because inflammation was observed in vitro. Our simple TK model can be parameterized for other glycol ethers and used in parallel with in vitro data for systematically screening for neurotoxicity. If further developed, this approach could be adapted to predict brain neurotoxicity from exposure to organic solvents.

摘要

尽管有机溶剂已被证实与中枢神经系统毒性有关,但神经毒性测试通常不是监管要求。我们提出了一种策略,以评估有机溶剂的潜在神经毒性,并预测不会对暴露个体产生神经毒性的溶剂空气浓度。该策略集成了体外神经毒性、体外血脑屏障(BBB)和计算毒代动力学(TK)模型。我们用广泛用于工业和消费品的丙二醇甲醚(PGME)说明了这一概念。阳性对照物为乙二醇甲醚(EGME),阴性对照物为丙二醇丁醚(PGBE),这是一种据称没有神经毒性的二醇醚。PGME、PGBE 和 EGME 均具有较高的 BBB 被动渗透率(渗透系数(P)分别为 11.0×10、9.0×10 和 6.0×10 cm/min)。PGBE 在体外重复神经毒性测定中最具效力。EGME 的主要代谢物——甲氧基乙酸(MAA)可能是导致人类报告的神经毒性作用的原因。神经元生物标志物的无观察不良效应浓度(NOAEC)分别为 PGME、PGBE 和 EGME 为 10.2、0.07 和 79.2 mM。所有测试物质均引起促炎细胞因子表达的浓度依赖性增加。该 TK 模型用于从 PGME 的 NOAEC 到相应空气浓度(684 ppm)的体外到体内外推。总之,我们能够使用我们的策略预测不太可能导致神经毒性的空气浓度。我们证实,瑞士 PGME 职业暴露限值(100 ppm)不太可能对脑细胞产生即时的不良影响。然而,由于体外观察到炎症,我们不能排除可能的长期神经退行性影响。我们简单的 TK 模型可以针对其他二醇醚进行参数化,并与体外数据并行使用,以系统地筛选神经毒性。如果进一步开发,这种方法可以适应从暴露于有机溶剂预测大脑神经毒性。

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