Coelho Eduardo Barbosa, Cusinato Diego Alberto Ciscato, Ximenez João Paulo, Lanchote Vera Lucia, Struchiner Claudio José, Suarez-Kurtz Guilherme
Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, Brazil.
Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, Brazil.
Front Pharmacol. 2020 Feb 14;11:22. doi: 10.3389/fphar.2020.00022. eCollection 2020.
Plasma concentration data points (n = 2,640) from 16 healthy adults were used to develop and validate limited sampling strategies (LSS) for estimation of phenotypic metrics for CYP enzymes and the ABCB1 transporter, using a cocktail of subtherapeutic doses of the selective probes caffeine (CYP1A2), metoprolol (CYP2D6), midazolam (CYP3A), losartan (CYP2C9), omeprazole (CYP2C19), and fexofenadine (ABCB1). All-subsets linear regression modelling was applied to estimate the AUC for caffeine, fexofenadine, and midazolam, and the AUC ratio of metoprolol: α-OH metoprolol and omeprazole:5-OH omeprazole. LSS-derived metrics were compared with the parameters' 'best estimates' obtained by non-compartmental analysis using all plasma concentration data points. The correlation coefficient ( ) was used to identify the LSS equations that provided the best fit for timed plasma samples, and the jack-knife statistics was used as an additional validation procedure for the LSS models. Single time-point LSS models provided values greater than 0.95 ( > 0.95) for the AUC ratio of metoprolol:α-OH metoprolol and omeprazole:5-OH omeprazole, whereas 2 time-point models were required for > 0.95 for the AUC of caffeine, fexofenadine, and midazolam. Increasing the number of sampling points to three led to minor increases in and/or the bias or prediction of the estimates. In conclusion, the LSS models provided accurate prediction of phenotypic indices for CYP1A2, CYP2C19, CYP2D6, CYP3A, and ABCB1, when using subtherapeutic doses of selective probes for these enzymes and transporter.
利用来自16名健康成年人的血浆浓度数据点（n = 2640），采用亚治疗剂量的选择性探针咖啡因（CYP1A2）、美托洛尔（CYP2D6）、咪达唑仑（CYP3A）、氯沙坦（CYP2C9）、奥美拉唑（CYP2C19）和非索非那定（ABCB1）的鸡尾酒，来开发和验证用于估计CYP酶和ABCB1转运蛋白表型指标的有限采样策略（LSS）。应用全子集线性回归模型来估计咖啡因、非索非那定和咪达唑仑的AUC，以及美托洛尔：α-羟基美托洛尔和奥美拉唑：5-羟基奥美拉唑的AUC比值。将LSS得出的指标与使用所有血浆浓度数据点通过非房室分析获得的参数“最佳估计值”进行比较。相关系数（ ）用于确定最适合定时血浆样本的LSS方程，留一法统计用作LSS模型的额外验证程序。单时间点LSS模型对于美托洛尔：α-羟基美托洛尔和奥美拉唑：5-羟基奥美拉唑的AUC比值提供的值大于0.95（ > 0.95），而对于咖啡因、非索非那定和咪达唑仑的AUC， > 0.95则需要2时间点模型。将采样点数量增加到三个导致 和/或估计值的偏差或预测略有增加。总之，当使用这些酶和转运蛋白的亚治疗剂量选择性探针时，LSS模型能够准确预测CYP1A2、CYP2C19、CYP2D6、CYP3A和ABCB1的表型指数。
