Department of Pharmacology and Toxicology, Radboud University Medical Center, Nijmegen, the Netherlands.
Department of Pharmacy, Radboud University Medical Center, Nijmegen, the Netherlands.
Toxicol In Vitro. 2022 Dec;85:105471. doi: 10.1016/j.tiv.2022.105471. Epub 2022 Sep 10.
Commercially available physiologically-based pharmacokinetic (PBPK) modeling platforms increasingly allow estimations of fetal exposure to xenobiotics. We aimed to explore a physiology-based approach in which literature data from ex vivo placenta perfusion studies are used to parameterize Simcyp's pregnancy-PBPK (p-PBPK) model, taking crizotinib as an example. First, a physiologically-based semi-mechanistic placenta (PBMP) model was developed in MATLAB to analyze placenta perfusion data of crizotinib. Mixed-effects modeling was performed to derive intrinsic unbound clearance values across the maternal-placental barrier and fetal-placental barrier. Values were then used for parameterization of the p-PBPK model. The PBMP model adequately described the perfusion data. Clearance was estimated to be 71 mL/min and 535 mL/min for the maternal placental uptake and efflux, and 8 mL/min and 163 mL/min for fetal placental uptake and efflux, respectively. For oral dosing of 250 mg twice daily, p-PBPK modeling predicted a C and AUC of 0.08 mg/L and 0.78 mg/L*h in the umbilical vein at steady-state, respectively. In placental tissue, a C of 5.04 mg/L was predicted. In conclusion, PBMP model-based data analysis and the associated p-PBPK modeling approach illustrate how ex vivo placenta perfusion data may be used for fetal exposure predictions.
市售的基于生理学的药代动力学(PBPK)建模平台越来越能够估算外源性物质对胎儿的暴露。我们旨在探索一种基于生理学的方法,该方法利用离体胎盘灌注研究的文献数据对 Simcyp 的妊娠 PBPK(p-PBPK)模型进行参数化,以克唑替尼为例。首先,在 MATLAB 中开发了一种基于生理学的半机械胎盘(PBMP)模型来分析克唑替尼的胎盘灌注数据。进行混合效应建模以推导出跨母体胎盘屏障和胎儿胎盘屏障的内在非结合清除值。然后将这些值用于 p-PBPK 模型的参数化。PBMP 模型充分描述了灌注数据。估计的清除率分别为母体胎盘摄取和外排 71 毫升/分钟和 535 毫升/分钟,以及胎儿胎盘摄取和外排 8 毫升/分钟和 163 毫升/分钟。对于每天两次口服 250 毫克的剂量,p-PBPK 建模预测在稳态时脐带静脉中的 C 和 AUC 分别为 0.08 毫克/升和 0.78 毫克/升*小时。在胎盘组织中,预测的 C 值为 5.04 毫克/升。总之,基于 PBMP 模型的数据分析和相关的 p-PBPK 建模方法说明了如何利用离体胎盘灌注数据进行胎儿暴露预测。