药代动力学表征在药物发现和开发中起着至关重要的作用。尽管涉及许多容易出错的实验动物,劳动密集型,和耗时的程序,药代动力学分析在临床前研究中仍然是不可替代的.通过基于生理的药代动力学(PBPK)建模,体内药物吸收的概况,分布,新陈代谢,排泄是可以预测的。为了评估这种方法在临床前研究中的应用,微粒体酶的七个常用探针底物的血浆药代动力学曲线,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,还有黄芩素,使用自下而上的PBPK模型在大鼠中预测,该模型仅使用体外数据构建。通过与文献报道的体内药代动力学数据进行比较来评估预测的可靠性。PBPK模型的总体预测精度良好,大多数倍数误差在2以内,预测浓度数据与观察浓度数据之间的决定系数(R2)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。我们得出的结论是,具有可接受的准确性的PBPK模型可以纳入临床前研究以完善体内研究。PBPK建模是实践3Rs原理的可行策略。
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 in vivo 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 in vitro data alone. The prediction\'s reliability was assessed by comparison with in vivo 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 (R2) 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 in vivo investigations, and PBPK modeling is a feasible strategy to practice the principles of
3Rs.