Tabata Kenji, Hamakawa Nozomu, Sanoh Seigo, Terashita Shigeyuki, Teramura Toshio
Analysis & Pharmacokinetics Research Labs, Astellas Pharma Inc., 21 Miyukigaoka Tsukuba-city, Ibaraki, Japan.
Eur J Drug Metab Pharmacokinet. 2009 Apr-Jun;34(2):117-28. doi: 10.1007/BF03191160.
We have proposed a novel method by population pharmacokinetics analysis for forecasting the drug concentration time-course in humans. This method is based on the non-linear mixed effect model (NONMEM) combined with in vitro-in vivo extrapolation (IVIVE). Eleven clinically tested compounds were selected for retrospective analysis. The in vivo pharmacokinetic (pk) profiles (rats, dogs, monkeys, and humans) and in vitro ADME data [intrinsic clearance (CLint), plasma unbound fraction (fp), and blood-plasma partition ratio (Rb)] for each compound was routinely tested via a screening system to account for inter-compound differences in pk properties. When evaluating the pk parameters, the hepatic plasma flow (Qph) and plasma volume (Vp) were taken into account to compensate for differences in body size among species. All these data were used to conduct population pk (PPK) analyses under the hypothesis that all species constituted one population. The two-compartment model (ADVAN4 TRANS3) and NONMEM software were used for this analysis. The fixed effect model for total body clearance (CL) and central distribution volume (Vd) were constructed as theta(CL)Qph x Eh and theta(Vd) x Vp, respectively, where the hepatic extraction ratio Eh was calculated using the traditional dispersion model. NONMEM generates both fixed and random effects (eta). The key point of this concept was to substitute the eta values of each species (rats, dogs, and monkeys) into the human PPK model to simulate three kinds of pk profiles, compound by compound, for use as a general scaling factor. The NONMEM post hoc option was used to perform the simulation, after which the concentration vs. time courses were compared with actual clinical pk data. The true values were almost within the dynamic range. Thus, the advantage of this concept is that it can generate time-courses without the detail of drug-specific parameters, from which the elimination half time can be determined. This proposed exploratory population pharmacokinetic (e-PPK) approach is a useful and progressive tool that can be applied during the early stages of drug discovery research.
我们提出了一种通过群体药代动力学分析预测人体药物浓度-时间过程的新方法。该方法基于非线性混合效应模型（NONMEM）并结合体外-体内外推法（IVIVE）。选择了11种经过临床测试的化合物进行回顾性分析。每种化合物的体内药代动力学（pk）概况（大鼠、狗、猴子和人类）以及体外ADME数据[内在清除率（CLint）、血浆未结合分数（fp）和血-浆分配比（Rb）]通过筛选系统进行常规测试，以考虑化合物之间pk特性的差异。在评估pk参数时，考虑了肝血浆流量（Qph）和血浆体积（Vp）以补偿物种间体型差异。所有这些数据用于在所有物种构成一个群体的假设下进行群体pk（PPK）分析。采用二室模型（ADVAN4 TRANS3）和NONMEM软件进行此分析。全身清除率（CL）和中央分布容积（Vd）的固定效应模型分别构建为theta(CL)Qph x Eh和theta(Vd) x Vp，其中肝提取率Eh使用传统分散模型计算。NONMEM产生固定效应和随机效应（eta）。该概念的关键点是将每个物种（大鼠、狗和猴子）的eta值代入人类PPK模型，逐个化合物地模拟三种pk概况，用作通用缩放因子。使用NONMEM事后检验选项进行模拟，之后将浓度-时间过程与实际临床pk数据进行比较。真实值几乎在动态范围内。因此，该概念的优点是它可以在不考虑药物特定参数细节的情况下生成时间过程，从中可以确定消除半衰期。这种提出的探索性群体药代动力学（e-PPK）方法是一种有用且进步的工具，可应用于药物发现研究的早期阶段。
