Kapralos Iasonas, Dokoumetzidis Aristides
Laboratory of Biopharmaceutics-Pharmacokinetics, Department of Pharmacy, National and Kapodistrian University of Athens, 15771 Athens, Greece.
Athena Research and Innovation Center in Information, Communication and Knowledge Technologies, 15125 Athens, Greece.
Pharmaceutics. 2021 Sep 28;13(10):1578. doi: 10.3390/pharmaceutics13101578.
The aim of the study is to develop a population pharmacokinetic (PPK) model, of Octreotide long acting repeatable (LAR) formulation in healthy volunteers, which describes the highly variable, multiple peak absorption pattern of the pharmacokinetics of the drug, in individual and population levels. An empirical absorption model, coupled with a one-compartment distribution model with linear elimination was found to describe the data well. Absorption was modelled as a weighted sum of a first order and three transit compartment absorption processes, with delays and appropriately constrained model parameters. Identifiability analysis verified that all twelve parameters of the structural model are identifiable. A machine learning method, i.e., cluster analysis, was performed as pre-processing of the PK profiles, to define subpopulations, before PPK modelling. It revealed that 13% of the patients deviated considerably from the typical absorption pattern and allowed better characterization of the observed heterogeneity and variability of the study, while the approach may have wider applicability in building PPK models. The final model was evaluated by goodness of fit plots, Visual Predictive Check plots and bootstrap. The present model is the first to describe the multiple-peak absorption pattern observed after octreotide LAR administration and may be useful to provide insights and validate hypotheses regarding release from PLGA-based formulations.
本研究的目的是建立健康志愿者中奥曲肽长效重复注射（LAR）制剂的群体药代动力学（PPK）模型，该模型在个体和群体水平上描述该药物药代动力学高度可变的多峰吸收模式。发现一个经验吸收模型与一个具有线性消除的单室分布模型相结合能很好地描述数据。吸收被建模为一级吸收过程和三个转运室吸收过程的加权和，并带有延迟和适当受限的模型参数。可识别性分析证实结构模型的所有十二个参数都是可识别的。在进行PPK建模之前，采用一种机器学习方法即聚类分析对药代动力学（PK）曲线进行预处理以定义亚组。结果显示13%的患者与典型吸收模式有很大偏差，这有助于更好地表征研究中观察到的异质性和变异性，同时该方法在建立PPK模型方面可能具有更广泛的适用性。通过拟合优度图、视觉预测检查图和自抽样法对最终模型进行评估。本模型是首个描述奥曲肽LAR给药后观察到的多峰吸收模式的模型，可能有助于提供关于基于聚乳酸-羟基乙酸共聚物（PLGA）制剂释放的见解并验证相关假设。
