Tsamandouras Nikolaos, Dickinson Gemma, Guo Yingying, Hall Stephen, Rostami-Hodjegan Amin, Galetin Aleksandra, Aarons Leon
Centre for Applied Pharmacokinetic Research, Manchester Pharmacy School, The University of Manchester, Stopford Building, Room 3.32, Oxford Road, Manchester, M13 9PT, UK,
Pharm Res. 2015 Jun;32(6):1864-83. doi: 10.1007/s11095-014-1581-2. Epub 2014 Dec 2.
To develop a population physiologically-based pharmacokinetic (PBPK) model for simvastatin (SV) and its active metabolite, simvastatin acid (SVA), that allows extrapolation and prediction of their concentration profiles in liver (efficacy) and muscle (toxicity).
SV/SVA plasma concentrations (34 healthy volunteers) were simultaneously analysed with NONMEM 7.2. The implemented mechanistic model has a complex compartmental structure allowing inter-conversion between SV and SVA in different tissues. Prior information for model parameters was extracted from different sources to construct appropriate prior distributions that support parameter estimation. The model was employed to provide predictions regarding the effects of a range of clinically important conditions on the SV and SVA disposition.
The developed model offered a very good description of the available plasma SV/SVA data. It was also able to describe previously observed effects of an OATP1B1 polymorphism (c.521 T > C) and a range of drug-drug interactions (CYP inhibition) on SV/SVA plasma concentrations. The predicted SV/SVA liver and muscle tissue concentrations were in agreement with the clinically observed efficacy and toxicity outcomes of the investigated conditions.
A mechanistically sound SV/SVA population model with clinical applications (e.g., assessment of drug-drug interaction and myopathy risk) was developed, illustrating the advantages of an integrated population PBPK approach.
建立辛伐他汀(SV)及其活性代谢产物辛伐他汀酸(SVA)的基于生理的群体药代动力学(PBPK)模型,以推断和预测它们在肝脏(疗效)和肌肉(毒性)中的浓度分布。
使用NONMEM 7.2同时分析34名健康志愿者的SV/SVA血浆浓度。所实施的机制模型具有复杂的房室结构,允许SV和SVA在不同组织之间相互转化。从不同来源提取模型参数的先验信息,以构建支持参数估计的适当先验分布。该模型用于预测一系列临床重要情况对SV和SVA处置的影响。
所开发的模型对现有的血浆SV/SVA数据提供了很好的描述。它还能够描述先前观察到的OATP1B1多态性(c.521 T>C)和一系列药物相互作用(CYP抑制)对SV/SVA血浆浓度的影响。预测的SV/SVA肝脏和肌肉组织浓度与所研究情况的临床观察到的疗效和毒性结果一致。
开发了一个具有临床应用价值(如评估药物相互作用和肌病风险)的机制合理的SV/SVA群体模型,阐明了综合群体PBPK方法的优势。