Department of Environmental Toxicology, Eawag, Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600, Dübendorf, Switzerland.
Ecotoxicology. 2012 Oct;21(7):1828-40. doi: 10.1007/s10646-012-0917-0. Epub 2012 May 5.
Toxicokinetic-toxicodynamic (TKTD) models quantify the time-course of internal concentration, which is defined by uptake, elimination and biotransformation (TK), and the processes which lead to the toxic effects (TD). TKTD models show potential in predicting pesticide effects in fluctuating concentrations, but the data requirements and validity of underlying model assumptions are not known. We calibrated TKTD models to predict survival of Gammarus pulex in propiconazole exposure and investigated the data requirements. In order to assess the need of TK in survival models, we included or excluded simulated internal concentrations based on pre-calibrated TK. Adding TK did not improve goodness of fits. Moreover, different types of calibration data could be used to model survival, which might affect model parameterization. We used two types of data for calibration: acute toxicity (standard LC50, 4 d) or pulsed toxicity data (total length 10 d). The calibration data set influenced how well the survival in the other exposure scenario was predicted (acute to pulsed scenario or vice versa). We also tested two contrasting assumptions in ecotoxicology: stochastic death and individual tolerance distribution. Neither assumption fitted to data better than the other. We observed in 10-d toxicity experiments that pulsed treatments killed more organisms than treatments with constant concentration. All treatments received the same dose, i.e. the time-weighted average concentration was equal. We studied mode of toxic action of propiconazole and it likely acts as a baseline toxicant in G. pulex during 10-days of exposure for the endpoint survival.
毒代动力学-毒效动力学(TKTD)模型定量描述了内部浓度的时间过程,这一过程由摄取、消除和生物转化(TK)来定义,同时也定义了导致毒性效应(TD)的过程。TKTD 模型在预测波动浓度下的农药效应方面具有潜力,但尚不清楚其数据要求和潜在模型假设的有效性。我们使用 TKTD 模型来预测吡丙醚暴露下大型蚤的生存情况,并研究了数据需求。为了评估 TK 在生存模型中的必要性,我们根据预先校准的 TK 来包含或排除模拟的内部浓度。添加 TK 并不能提高拟合优度。此外,可以使用不同类型的校准数据来模拟生存情况,这可能会影响模型参数化。我们使用了两种类型的数据进行校准:急性毒性(标准 LC50,4 天)或脉冲毒性数据(总时长 10 天)。校准数据集影响了在其他暴露场景下预测生存的效果(急性到脉冲场景或反之亦然)。我们还测试了两种在生态毒理学中具有对比性的假设:随机死亡和个体耐受分布。这两种假设都没有比另一种假设更适合数据。我们在 10 天毒性实验中观察到,脉冲处理比恒定浓度处理杀死了更多的生物。所有处理都接受了相同的剂量,即时间加权平均浓度相等。我们研究了吡丙醚的作用模式,它可能在大型蚤暴露于吡丙醚 10 天的时间内作为一种基线毒物。