Absorption Systems LP, Exton, Pennsylvania, USA.
J Pharm Sci. 2013 Sep;102(9):3436-46. doi: 10.1002/jps.23523. Epub 2013 Apr 5.
Madin-Darby canine kidney (MDCK) cells transfected with the multidrug resistance 1 (MDR1) gene, MDR1-MDCK, are widely used as an in vitro model to classify compounds as human P-glycoprotein (hPgp) substrates or nonsubstrates. Because MDCK cells express endogenous canine Pgp (cPgp), which is prone to downregulation after transfection with hPgp, this situation could lead to false-negative classification of hPgp substrates. The aim of this study was to investigate factors that influence hPgp substrate classification in MDR1-MDCK model and to seek ways to reduce false classification. Three-compartment models were used to derive flux equations describing the drug transport processes; factors influencing hPgp substrate classification were evaluated by simulations. Pgp functionality was assessed by determining the bidirectional permeability of a series of test compounds. Expressions of hPgp and cPgp were measured by quantitative polymerase chain reaction (qPCR). Kinetic model analysis revealed that the current net flux ratio calculation for hPgp substrate classification is influenced by endogenous cPgp expression as well as hPgp-cPgp expression ratio; the effect was more pronounced in low hPgp-cPgp region and diminished in high ratio region. On the basis of kinetic considerations, this study provides a rational experimental approach and appropriate mathematical corrections to minimize the potential occurrence of false-negative classification of new molecular entities.
转染多药耐药 1 基因(MDR1)的犬肾细胞(MDCK),MDR1-MDCK,被广泛用作体外模型,用于将化合物分类为人类 P 糖蛋白(hPgp)底物或非底物。由于 MDCK 细胞表达内源性犬 P 糖蛋白(cPgp),在用 hPgp 转染后 cPgp 容易下调,这种情况可能导致 hPgp 底物的假阴性分类。本研究旨在探讨影响 MDR1-MDCK 模型中 hPgp 底物分类的因素,并寻求减少假分类的方法。使用三室模型推导出描述药物转运过程的通量方程;通过模拟评估影响 hPgp 底物分类的因素。通过测定一系列测试化合物的双向通透性来评估 Pgp 功能。通过定量聚合酶链反应(qPCR)测量 hPgp 和 cPgp 的表达。动力学模型分析表明,当前用于 hPgp 底物分类的净通量比计算受到内源性 cPgp 表达以及 hPgp-cPgp 表达比的影响;在低 hPgp-cPgp 区域的影响更为明显,在高比值区域则减弱。基于动力学考虑,本研究提供了一种合理的实验方法和适当的数学修正,以最大程度地减少新分子实体假阴性分类的潜在发生。