Toxicokinetic‐toxicodynamic (TKTD) models offer a mechanistic understanding of individual‐level toxicity over time and allow for meaningful extrapolations from laboratory tests to exposure conditions in the field. Thereby, they hold great potential for ecotoxicological studies, both in a regulatory context as well as for basic research. In contrast to mechanistic effect models at higher levels of biological organization, TKTD models can be, and generally are, parameterized by fitting them to data (results from toxicity tests). Fitting models comes with a range of statistical and numerical challenges, which may hamper the application of TKTD models in a practical setting. Especially in the context of environmental risk assessment, there is a need for robust and user‐friendly software tools to automatically extract the best‐fitting model parameters and quantify their uncertainty from any data set. The study presents a general outline for TKTD model analysis, rooted in likelihood‐based (“frequentist”) inference. The general outline is followed by a presentation of the specific algorithm that has been implemented into software for the robust and automated analysis of toxicity data for survival. However, the presented approach is more broadly applicable to low‐dimensional problems. Integr Environ Assess Manag 2021;17:388–397. © 2020 SETAC

中文翻译：

---

基于稳健性的毒物动力学-毒物动力学模型的自动优化和不确定性分析方法

毒物动力学-毒物动力学（TKTD）模型提供了对个体水平毒性随时间变化的机械理解，并允许从实验室测试到现场暴露条件进行有意义的推断。因此，无论是在法规方面还是在基础研究方面，它们在生态毒理学研究方面都具有巨大的潜力。与更高水平的生物组织的机械效应模型相反，TKTD模型可以并且通常通过将它们拟合到数据中来参数化（毒性测试的结果）。拟合模型面临一系列统计和数字挑战，这可能会妨碍TKTD模型在实际环境中的应用。特别是在环境风险评估的背景下，需要强大且用户友好的软件工具来自动提取最适合的模型参数并从任何数据集中量化其不确定性。该研究提出了TKTD模型分析的一般概述，该概述源于基于似然性（“频率”）的推断。概述之后是对特定算法的介绍，该算法已在软件中实现，可以对毒性数据进行健壮和自动分析以求生存。但是，本文提出的方法更广泛地适用于低维问题。概述之后是对特定算法的介绍，该算法已在软件中实现，可以对毒性数据进行健壮和自动分析以求生存。但是，本文提出的方法更广泛地适用于低维问题。概述之后是对特定算法的介绍，该算法已在软件中实现，可以对毒性数据进行健壮和自动分析以求生存。但是，本文提出的方法更广泛地适用于低维问题。Integr环境评估管理2021; 17：388-397。©2020 SETAC