Center of Pharmacokinetics and Metabolism, School of Pharmacy, China Pharmaceutical University, Nanjing, China.
Pharm Res. 2023 Jul;40(7):1735-1750. doi: 10.1007/s11095-023-03531-y. Epub 2023 May 24.
To develop a whole physiologically based pharmacokinetic-pharmacodynamic (PBPK-PD) model to describe the pharmacokinetics and anti-gastric acid secretion of omeprazole in CYP2C19 extensive metabolizers (EMs), intermediate metabolizers (IMs), poor metabolizers (PMs) and ultrarapid metabolizers (UMs) following oral or intravenous administration.
A PBPK/PD model was built using Phoenix WinNolin software. Omeprazole was mainly metabolized by CYP2C19 and CYP3A4 and the CYP2C19 polymorphism was incorporated using in vitro data. We described the PD by using a turn-over model with parameter estimates from dogs and the effect of a meal on the acid secretion was also implemented. The model predictions were compared to 53 sets of clinical data.
Predictions of omeprazole plasma concentration (72.2%) and 24 h stomach pH after administration (85%) were within 0.5-2.0-fold of the observed values, indicating that the PBPK-PD model was successfully developed. Sensitivity analysis demonstrated that the contributions of the tested factors to the plasma concentration of omeprazole were V ≈ P > V > K, and contributions to its pharmacodynamic were V > k > k > P > V. The simulations showed that while the initial omeprazole dose in UMs, EMs, and IMs increased 7.5-, 3- and 1.25-fold compared to those of PMs, the therapeutic effect was similar.
The successful establishment of this PBPK-PD model highlights that pharmacokinetic and pharmacodynamic profiles of drugs can be predicted using preclinical data. The PBPK-PD model also provided a feasible alternative to empirical guidance for the recommended doses of omeprazole.
建立一个整体生理基于药代动力学 - 药效学（PBPK-PD）模型，以描述 CYP2C19 广泛代谢者（EMs）、中间代谢者（IMs）、弱代谢者（PMs）和超快代谢者（UMs）口服或静脉给予奥美拉唑后的药代动力学和抗胃酸分泌作用。
使用 Phoenix WinNolin 软件建立 PBPK/PD 模型。奥美拉唑主要由 CYP2C19 和 CYP3A4 代谢，并用体外数据纳入 CYP2C19 多态性。我们使用来自狗的参数估计的 turnover 模型来描述 PD，并实施了膳食对酸分泌的影响。模型预测与 53 组临床数据进行了比较。
奥美拉唑血浆浓度（72.2%）和给药后 24 小时胃 pH 值（85%）的预测值在观察值的 0.5-2.0 倍范围内，表明 PBPK-PD 模型成功建立。敏感性分析表明，测试因素对奥美拉唑血浆浓度的贡献为 V≈P>V>K，对药效学的贡献为 V>k>k>p>V。模拟表明，与 PMs 相比，UMs、EMs 和 IMs 的初始奥美拉唑剂量分别增加了 7.5、3 和 1.25 倍，但其治疗效果相似。
该 PBPK-PD 模型的成功建立表明，可以使用临床前数据预测药物的药代动力学和药效学特征。PBPK-PD 模型还为奥美拉唑推荐剂量的经验性指导提供了一种可行的替代方案。
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