Steere Boyd, Baker Jessica A Roseberry, Hall Stephen D, Guo Yingying
Research IT Informatics (B.S.), Clinical Diagnostic Laboratory (J.A.R.B.), and Drug Disposition (S.D.H., Y.G.), Eli Lilly and Company, Indianapolis, Indiana
Research IT Informatics (B.S.), Clinical Diagnostic Laboratory (J.A.R.B.), and Drug Disposition (S.D.H., Y.G.), Eli Lilly and Company, Indianapolis, Indiana.
Drug Metab Dispos. 2015 Jun;43(6):870-83. doi: 10.1124/dmd.114.061523. Epub 2015 Apr 6.
It is important to examine the cytochrome P450 2C19 (CYP2C19) genetic contribution to drug disposition and responses of CYP2C19 substrates during drug development. Design of such clinical trials requires projection of genotype-dependent in vivo clearance and associated variabilities of the investigational drug, which is not generally available during early stages of drug development, but is essential for CYP2C19 substrates with multiple clearance pathways. This study evaluated the utility of pharmacogenetics-based mechanistic modeling in predicting such parameters. Hepatic CYP2C19 activity and variability within genotypes were derived from in vitro S-mephenytoin metabolic activity in genotyped human liver microsomes (N = 128). These data were then used in mechanistic models to predict genotype-dependent disposition of CYP2C19 substrates (i.e., S-mephenytoin, citalopram, pantoprazole, and voriconazole) by incorporating in vivo clearance or pharmacokinetics of wild-type subjects and parameters of other clearance pathways. Relative to the wild-type, the CYP2C19 abundance (coefficient of variation percentage) in CYP2C19*17/*17, *1/*17, *1/*1, *17/null, *1/null, and null/null microsomes was estimated as 1.85 (117%), 1.79 (155%), 1.00 (138%), 0.83 (80%), 0.38 (130%), and 0 (0%), respectively. The subsequent modeling and simulations predicted, within 2-fold of the observed, the means and variabilities of urinary S/R-mephenytoin ratio (36 of 37 genetic groups), the oral clearance of citalopram (9 of 9 genetic groups) and pantoprazole (6 of 6 genetic groups), and voriconazole oral clearance (4 of 4 genetic groups). Thus, relative CYP2C19 genotype-dependent hepatic activity and variability were quantified in vitro and used in a mechanistic model to predict pharmacokinetic variability, thus allowing the design of pharmacogenetics and drug-drug interaction trials for CYP2C19 substrates.
在药物研发过程中，研究细胞色素P450 2C19（CYP2C19）基因对药物处置以及CYP2C19底物反应的影响至关重要。此类临床试验的设计需要预测研究药物的基因型依赖性体内清除率及相关变异性，而这在药物研发早期通常无法获得，但对于具有多种清除途径的CYP2C19底物而言却至关重要。本研究评估了基于药物遗传学的机制模型在预测此类参数方面的效用。肝CYP2C19活性及基因型内的变异性源自基因分型的人肝微粒体（N = 128）中S-美芬妥因的体外代谢活性。然后，通过纳入野生型受试者的体内清除率或药代动力学以及其他清除途径的参数，将这些数据用于机制模型，以预测CYP2C19底物（即S-美芬妥因、西酞普兰、泮托拉唑和伏立康唑）的基因型依赖性处置。相对于野生型，CYP2C19*17/*17、*1/*17、*1/*1、*17/null、*1/null和null/null微粒体中CYP2C19丰度（变异系数百分比）分别估计为1.85（117%）、1.79（155%）、1.00（138%）、0.83（80%）、0.38（130%）和0（0%）。随后的建模与模拟在观察值的2倍范围内预测了尿S/R-美芬妥因比值（37个基因组中的36个）、西酞普兰（9个基因组中的9个）和泮托拉唑（6个基因组中的6个）的口服清除率以及伏立康唑口服清除率（4个基因组中的4个）的均值和变异性。因此，体外定量了相对CYP2C19基因型依赖性肝活性及变异性，并将其用于机制模型以预测药代动力学变异性，从而能够设计针对CYP2C19底物的药物遗传学和药物-药物相互作用试验。
