Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK.
Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK.
Toxicol Appl Pharmacol. 2022 May 1;442:115992. doi: 10.1016/j.taap.2022.115992. Epub 2022 Mar 25.
Combined with in vitro bioactivity data, physiologically based kinetic (PBK) models has increasing applications in next generation risk assessment for animal-free safety decision making. A tiered framework of building PBK models for such application has been developed with increasing complexity and refinements, as model parameters determined in silico, in vitro, and with human pharmacokinetic data become progressively available. PBK modelling has been widely applied for oral/intravenous administration, but less so on topically applied chemicals. Therefore, building PBK models for topical applications and characterizing their uncertainties in the tiered approach is critical to safety decision making. The purpose of this study was to assess the confidence of PBK modelling of topically applied chemicals following the tiered framework, using non-animal methods derived parameters. Prediction of maximum plasma concentration (C) and area under the curve were compared to observed kinetics from published dermal clinical studies for five chemicals (diclofenac, salicylic acid, coumarin, nicotine, caffeine). A bespoke Bayesian statistical model was developed to describe the distributions of C errors between the predicted and observed data. We showed a general trend that confidence in model predictions increases when more quality in vitro data, particularly those on hepatic clearance and dermal absorption, are available as model input. The overall fold error distributions are useful for characterizing model uncertainty. We concluded that by identifying and quantifying the uncertainties in the tiered approach, we can increase the confidence in using PBK modelling to help make safety decisions on topically applied chemicals in the absence of human pharmacokinetic data.
结合体外生物活性数据，基于生理学的药代动力学（PBK）模型在下一代无动物安全性决策的风险评估中得到了越来越多的应用。为了实现这一应用，已经开发了一个具有递增复杂性和细化程度的 PBK 模型构建分层框架，因为在计算机、体外和人体药代动力学数据中确定的模型参数变得越来越可用。PBK 建模已广泛应用于口服/静脉给药，但在局部应用化学品方面的应用较少。因此，构建用于局部应用的 PBK 模型并在分层方法中对其不确定性进行特征描述，对于安全性决策至关重要。本研究的目的是使用非动物方法推导的参数，根据分层框架评估局部应用化学品 PBK 建模的置信度。使用已发表的皮肤临床研究中的数据，比较了 5 种化学品（双氯芬酸、水杨酸、香豆素、尼古丁、咖啡因）的最大血浆浓度（C）和曲线下面积的预测值与观察到的动力学值。开发了一种定制的贝叶斯统计模型来描述预测数据和观察数据之间 C 误差的分布。我们发现，当更多的高质量体外数据（特别是关于肝清除率和皮肤吸收的数据）可用作模型输入时，模型预测的置信度通常会提高。总的折叠误差分布对于描述模型不确定性很有用。我们得出的结论是，通过识别和量化分层方法中的不确定性，我们可以提高使用 PBK 建模来帮助在没有人体药代动力学数据的情况下对局部应用化学品做出安全性决策的信心。
