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具有种属特异性全身清除率的肝代谢药物的人药代动力学预测的改善。

Improved Human Pharmacokinetic Prediction of Hepatically Metabolized Drugs With Species-Specific Systemic Clearance.

机构信息

Research Laboratory for Development, Shionogi & Co., Ltd., Toyonaka, Osaka 561-0825, Japan.

Research Laboratory for Development, Shionogi & Co., Ltd., Toyonaka, Osaka 561-0825, Japan.

出版信息

J Pharm Sci. 2018 May;107(5):1443-1453. doi: 10.1016/j.xphs.2017.12.027. Epub 2018 Jan 10.

Abstract

Accurate prediction of human pharmacokinetics (PK) is important for the choice of promising compounds in humans. As the predictability of human PK by an empirical approach is low for drugs with species-specific PK, the utility of a physiologically based pharmacokinetic (PBPK) model was verified using 16 hepatically metabolized reference drugs. After the prediction method for total clearance (CL) and distribution volume at steady state (Vd) in the conventional PBPK model had been optimized, plasma concentrations following a single oral administration of each reference drug to healthy volunteers were simulated, and the prediction accuracy for human PK was compared between empirical approaches and the optimized PBPK model. In the drugs with low species-specific CL, there was little difference in predictability for maximum concentration (C), time to maximum plasma concentration (T), and area under the curve (AUC) (absolute average fold error: 1.3-2.4). In contrast, the optimized PBPK model predicted C and AUC of the drugs with high species-specific CL with lower absolute average fold error (C and AUC: 2.8 and 3.2, respectively) than those of the empirical approach (C and AUC: 2.6-4.9 and 3.9-10.7, respectively). Therefore, the optimized PBPK model is useful for human PK prediction of drugs with species-specific CL.

摘要

准确预测人体药代动力学(PK)对于在人体中选择有前途的化合物非常重要。由于对于具有物种特异性 PK 的药物,经验方法预测人体 PK 的可预测性较低,因此使用 16 种肝代谢的参考药物验证了生理基于药代动力学(PBPK)模型的实用性。在优化了常规 PBPK 模型中总清除率(CL)和稳态分布容积(Vd)的预测方法后,模拟了每个参考药物单次口服给予健康志愿者后的血浆浓度,并比较了经验方法和优化后的 PBPK 模型对人体 PK 的预测准确性。在具有低物种特异性 CL 的药物中,对于最大浓度(C)、最大血浆浓度时间(T)和曲线下面积(AUC)的预测性差异不大(绝对平均倍误差:1.3-2.4)。相比之下,优化后的 PBPK 模型以较低的绝对平均倍误差(C 和 AUC:分别为 2.8 和 3.2)预测了具有高物种特异性 CL 的药物的 C 和 AUC,而经验方法的预测值(C 和 AUC:分别为 2.6-4.9 和 3.9-10.7)。因此,优化后的 PBPK 模型可用于预测具有物种特异性 CL 的药物的人体 PK。

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