Yuan Yawen, He Qingfeng, Zhang Shunguo, Li Min, Tang Zhijia, Zhu Xiao, Jiao Zheng, Cai Weimin, Xiang Xiaoqiang
Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, China.
Department of Pharmacy, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Front Pharmacol. 2022 May 12;13:895556. doi: 10.3389/fphar.2022.895556. eCollection 2022.
Pharmacokinetic characterization plays a vital role in drug discovery and development. Although involving numerous laboratory animals with error-prone, labor-intensive, and time-consuming procedures, pharmacokinetic profiling is still irreplaceable in preclinical studies. With physiologically based pharmacokinetic (PBPK) modeling, the profiles of drug absorption, distribution, metabolism, and excretion can be predicted. To evaluate the application of such an approach in preclinical investigations, the plasma pharmacokinetic profiles of seven commonly used probe substrates of microsomal enzymes, including phenacetin, tolbutamide, omeprazole, metoprolol, chlorzoxazone, nifedipine, and baicalein, were predicted in rats using bottom-up PBPK models built with data alone. The prediction's reliability was assessed by comparison with pharmacokinetic data reported in the literature. The overall predicted accuracy of PBPK models was good with most fold errors within 2, and the coefficient of determination (R) between the predicted concentration data and the observed ones was more than 0.8. Moreover, most of the observation dots were within the prediction span of the sensitivity analysis. We conclude that PBPK modeling with acceptable accuracy may be incorporated into preclinical studies to refine investigations, and PBPK modeling is a feasible strategy to practice the principles of 3Rs.
药代动力学特征在药物发现和开发中起着至关重要的作用。尽管药代动力学分析涉及大量实验动物,且过程容易出错、 labor-intensive且耗时,但在临床前研究中它仍然是不可替代的。通过基于生理的药代动力学(PBPK)模型,可以预测药物吸收、分布、代谢和排泄的情况。为了评估这种方法在临床前研究中的应用,使用仅基于数据构建的自下而上的PBPK模型,在大鼠中预测了七种常用微粒体酶探针底物的血浆药代动力学情况,这些底物包括非那西丁、甲苯磺丁脲、奥美拉唑、美托洛尔、氯唑沙宗、硝苯地平和黄芩苷。通过与文献中报道的药代动力学数据进行比较,评估了预测的可靠性。PBPK模型的总体预测准确性良好,大多数倍差在2以内,预测浓度数据与观察数据之间的决定系数(R)大于0.8。此外,大多数观察点都在敏感性分析的预测范围内。我们得出结论,具有可接受准确性的PBPK建模可纳入临床前研究以完善研究,并且PBPK建模是践行3R原则的一种可行策略。 (注:原文中“labor-intensive”未翻译完整,推测可能是“劳动密集型”之意)