Youdim Kuresh A, Zayed Aref, Dickins Maurice, Phipps Alex, Griffiths Michelle, Darekar Amanda, Hyland Ruth, Fahmi Odette, Hurst Susan, Plowchalk David R, Cook Jack, Guo Feng, Obach R Scott
Pfizer Global Research and Development, Department of Pharmacokinetics, Dynamics and Metabolism, Sandwich, Kent, UK.
Br J Clin Pharmacol. 2008 May;65(5):680-92. doi: 10.1111/j.1365-2125.2007.03070.x. Epub 2008 Feb 14.
Numerous retrospective analyses have shown the utility of in vitro systems for predicting potential drug-drug interactions (DDIs). Prediction of DDIs from in vitro data is commonly obtained using estimates of enzyme K(i), inhibitor and substrate concentrations and absorption rate for substrate and inhibitor.
Using a generic approach for all test compounds, the findings from the current study showed the use of recombinant P450s provide a more robust in vitro measure of P450 contribution (fraction metabolized, f(m)) than that achieved when using chemical inhibitors in combination with human liver microsomes, for the prediction of potential CYP3A4 drug-drug interactions prior to clinical investigation. The current study supported the use of SIMCYP(R), a modelling and simulation software in utilizing the in vitro measures in the prediction of potential drug-drug interactions.
The aim of this study was to explore and optimize the in vitro and in silico approaches used for predicting clinical DDIs. A data set containing clinical information on the interaction of 20 Pfizer compounds with ketoconazole was used to assess the success of the techniques.
The study calculated the fraction and the rate of metabolism of 20 Pfizer compounds via each cytochrome P450. Two approaches were used to determine fraction metabolized (f(m)); 1) by measuring substrate loss in human liver microsomes (HLM) in the presence and absence of specific chemical inhibitors and 2) by measuring substrate loss in individual cDNA expressed P450s (also referred to as recombinant P450s (rhCYP)) The fractions metabolized via each CYP were used to predict the drug-drug interaction due to CYP3A4 inhibition by ketoconazole using the modelling and simulation software SIMCYP.
When in vitro data were generated using Gentest supersomes, 85% of predictions were within two-fold of the observed clinical interaction. Using PanVera baculosomes, 70% of predictions were predicted within two-fold. In contrast using chemical inhibitors the accuracy was lower, predicting only 37% of compounds within two-fold of the clinical value. Poorly predicted compounds were found to either be metabolically stable and/or have high microsomal protein binding. The use of equilibrium dialysis to generate accurate protein binding measurements was especially important for highly bound drugs.
The current study demonstrated that the use of rhCYPs with SIMCYP provides a robust in vitro system for predicting the likelihood and magnitude of changes in clinical exposure of compounds as a consequence of CYP3A4 inhibition by a concomitantly administered drug.
众多回顾性分析已表明体外系统在预测潜在药物相互作用（DDIs）方面的效用。从体外数据预测药物相互作用通常是通过酶K(i)、抑制剂和底物浓度以及底物和抑制剂的吸收速率估算来实现的。
通过对所有测试化合物采用通用方法，当前研究结果表明，在临床研究前预测潜在的CYP3A4药物相互作用时，与使用化学抑制剂联合人肝微粒体相比，使用重组细胞色素P450能提供更可靠的体外细胞色素P450贡献量（代谢分数，f(m)）测量方法。当前研究支持使用SIMCYP（一种建模与模拟软件）利用体外测量来预测潜在药物相互作用。
本研究旨在探索并优化用于预测临床药物相互作用的体外和计算机模拟方法。使用一个包含20种辉瑞化合物与酮康唑相互作用临床信息的数据集来评估这些技术的成效。
该研究计算了20种辉瑞化合物通过每种细胞色素P450的代谢分数和代谢速率。采用两种方法来确定代谢分数（f(m)）；1）通过测量在存在和不存在特定化学抑制剂的情况下人肝微粒体（HLM）中的底物损失，以及2）通过测量单个cDNA表达的细胞色素P450（也称为重组细胞色素P450（rhCYP））中的底物损失。通过每种细胞色素P450代谢的分数用于使用建模与模拟软件SIMCYP预测由于酮康唑抑制CYP3A4导致的药物相互作用。
当使用Gentest超微粒体生成体外数据时，85%的预测值在观察到的临床相互作用的两倍范围内。使用PanVera杆状微粒体时，70%的预测值在两倍范围内。相比之下，使用化学抑制剂时准确性较低，仅37%的化合物预测值在临床值的两倍范围内。发现预测效果不佳的化合物要么代谢稳定和/或具有高微粒体蛋白结合率。使用平衡透析来生成准确的蛋白结合测量值对于高结合率药物尤为重要。
当前研究表明，将重组细胞色素P450与SIMCYP结合使用可提供一个可靠的体外系统，用于预测由于同时服用的药物抑制CYP3A4而导致化合物临床暴露量变化的可能性和幅度。
