Bloch Sherri, Arnot Jon A, Kramer Nynke I, Armitage James M, Verner Marc-André
Department of Occupational and Environmental Health, School of Public Health, Université de Montréal, Montreal, QC, Canada.
Centre de Recherche en Santé Publique, Université de Montréal et CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montreal, QC, Canada.
Front Toxicol. 2022 Aug 22;4:911128. doi: 10.3389/ftox.2022.911128. eCollection 2022.
As toxicologists and risk assessors move away from animal testing and more toward using models and biological modeling, it is necessary to produce tools to quantify the chemical distribution within the environment prior to extrapolating concentrations to human equivalent doses. Although models predicting chemical distribution have been developed, very little has been done for repeated dosing scenarios, which are common in prolonged experiments where the medium needs to be refreshed. Failure to account for repeated dosing may lead to inaccurate estimations of exposure and introduce bias into subsequent to extrapolations. Our objectives were to develop a dynamic mass balance model for repeated dosing in systems; to evaluate model accuracy against experimental data; and to perform illustrative simulations to assess the impact of repeated doses on predicted cellular concentrations. A novel dynamic partitioning mass balance model (IV-MBM DP v1.0) was created based on the well-established fugacity approach. We parameterized and applied the dynamic mass balance model to single dose and repeat dosing scenarios, and evaluated the predicted medium and cellular concentrations against available empirical data. We also simulated repeated dosing scenarios for organic chemicals with a range of partitioning properties and compared the distributions over time. In single dose scenarios, for which only medium concentrations were available, simulated concentrations predicted measured concentrations with coefficients of determination ( ) of 0.85-0.89, mean absolute error within a factor of two and model bias of nearly one. Repeat dose scenario simulations displayed model bias <2 within the cell lysate, and ∼1.5-3 in the medium. The concordance between simulated and available experimental data supports the predictive capacity of the IV-MBM DP v1.0 tool, but further evaluation as empirical data becomes available is warranted, especially for cellular concentrations.
随着毒理学家和风险评估人员逐渐摒弃动物试验，更多地转向使用模型和生物建模，有必要开发工具来量化环境中的化学物质分布，然后再将浓度外推至人体等效剂量。尽管已经开发出了预测化学物质分布的模型，但对于重复给药情况的研究却很少，而在需要更换培养基的长期实验中，重复给药是很常见的。未能考虑重复给药可能会导致暴露估计不准确，并在后续外推中引入偏差。我们的目标是开发一种用于系统重复给药的动态质量平衡模型；根据实验数据评估模型准确性；并进行说明性模拟，以评估重复给药对预测细胞浓度的影响。基于成熟的逸度方法创建了一种新型动态分配质量平衡模型（IV-MBM DP v1.0）。我们对动态质量平衡模型进行了参数化，并将其应用于单剂量和重复给药情况，根据现有的经验数据评估预测的培养基和细胞浓度。我们还模拟了具有一系列分配特性的有机化学品的重复给药情况，并比较了随时间的分布。在仅提供培养基浓度的单剂量情况下，模拟浓度预测的测量浓度的决定系数（ ）为0.85 - 0.89，平均绝对误差在两倍以内，模型偏差接近1。重复剂量情况模拟显示，细胞裂解物中的模型偏差<2，培养基中的偏差约为1.5 - 3。模拟结果与现有实验数据之间的一致性支持了IV-MBM DP v1.0工具的预测能力，但随着经验数据的获得，有必要进行进一步评估，尤其是对于细胞浓度。
