DataSheet1_Development and validation of PBPK models for genistein and daidzein for use in a next-generation risk assessment.PDF

IntroductionAll cosmetic ingredients must be evaluated for their safety to consumers. In the absence of in vivo data, systemic concentrations of ingredients can be predicted using Physiologically based Pharmacokinetic (PBPK) models. However, more examples are needed to demonstrate how they can be validated and applied in Next-Generation Risk Assessments (NGRA) of cosmetic ingredients. We used a bottom-up approach to develop human PBPK models for genistein and daidzein for a read-across NGRA, whereby genistein was the source chemical for the target chemical, daidzein.MethodsAn oral rat PBPK model for genistein was built using PK-Sim® and in vitro ADME input data. This formed the basis of the daidzein oral rat PBPK model, for which chemical-specific input parameters were used. Rat PBPK models were then converted to human models using human-specific physiological parameters and human in vitro ADME data. In vitro skin metabolism and penetration data were used to build the dermal module to represent the major route of exposure to cosmetics.ResultsThe initial oral rat model for genistein was qualified since it predicted values within 2-fold of measured in vivo PK values. This was used to predict plasma concentrations from the in vivo NOAEL for genistein to set test concentrations in bioassays. Intrinsic hepatic clearance and unbound fractions in plasma were identified as sensitive parameters impacting the predicted Cmax values. Sensitivity and uncertainty analyses indicated the developed PBPK models had a moderate level of confidence. An important aspect of the development of the dermal module was the implementation of first-pass metabolism, which was extensive for both chemicals. The final human PBPK model for daidzein was used to convert the in vitro PoD of 33 nM (from an estrogen receptor transactivation assay) to an external dose of 0.2% in a body lotion formulation.ConclusionPBPK models for genistein and daidzein were developed as a central component of an NGRA read-across case study. This will help to gain regulatory confidence in the use of PBPK models, especially for cosmetic ingredients.

引言所有化妆品成分均需评估其对消费者的安全性。在缺乏体内数据的情况下，可利用基于生理的药代动力学（PBPK）模型预测成分的系统浓度。然而，需要更多实例以展示其在化妆品成分下一代风险评估（NGRA）中的应用与验证。本研究采用自下而上的方法，开发了针对大豆苷元和金雀异黄素的PBPK模型，以实现跨物种风险评估，其中大豆苷元作为目标化合物金雀异黄素的源化合物。方法使用PK-Sim®构建了大豆苷元的口服大鼠PBPK模型，并利用体外ADME输入数据。以此为基础，结合针对金雀异黄素的特定输入参数，构建了金雀异黄素的口服大鼠PBPK模型。随后，利用针对人类的生理参数和人类体外ADME数据，将大鼠PBPK模型转化为人类模型。体外皮肤代谢和渗透数据被用于构建皮肤模块，以表示化妆品暴露的主要途径。结果大豆苷元的初始口服大鼠模型经验证，其预测值与体内测得的PK值相符度在2倍范围内。据此，从大豆苷元的体内NOAEL预测血浆浓度，以设定生物试验中的测试浓度。内源性肝清除率和血浆中未结合分数被确定为影响预测Cmax值的关键参数。敏感性及不确定性分析表明，所开发的PBPK模型具有中等置信度。皮肤模块开发的重要方面是首次通过代谢的实施，这两种化合物均存在广泛的首次通过代谢。最终，金雀异黄素的人类PBPK模型被用于将体外PoD值（来自雌激素受体转活化试验，为33 nM）转换为体乳配方中的外用剂量0.2%。结论本研究开发了大豆苷元和金雀异黄素的PBPK模型，作为下一代风险评估跨物种案例研究的核心组成部分。这将有助于增强监管机构对PBPK模型使用的信心，尤其是针对化妆品成分。