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药效曲线下面积作为抗感染药物疗效的决定因素。

The area under the effect curve as an efficacy determinant for anti-infectives.

机构信息

Clinical Pharmacology Modelling and Simulation, GlaxoSmithKline, London, UK.

Clinical Pharmacology, Modelling, and Simulation, Parexel International, Dublin, Ireland.

出版信息

CPT Pharmacometrics Syst Pharmacol. 2022 Aug;11(8):1029-1044. doi: 10.1002/psp4.12811. Epub 2022 May 31.

Abstract

Pharmacokinetic/pharmacodynamic (PK/PD) indices making use of area under the curve, maximum concentration, and the duration that in vivo drug concentration is maintained above a critical level are commonly applied to clinical dose prediction from animal efficacy experiments in the infectious disease arena. These indices make suboptimal use of the nonclinical data, and the prediction depends on the shape of the PK profiles in the animals, determined by the species-specific absorption, distribution, metabolism, and elimination properties, and the dosing regimen used in the efficacy experiments. Motivated by the principle that efficacy is driven by pharmacology, we conducted simulations using a generalized pathogen dynamic model, to assess the properties of an alternative efficacy predictor: the area under the effect curve (AUEC), computed using in vitro PD and in vivo PK. Across a wide range of hypothetical scenarios, the AUEC consistently showed regimen-independent strong correlation (R 0.76-0.98) with in vivo efficacy, superior to all other indices. These findings serve as proof of concept that AUEC should be considered in practice as a translation tool for cross-species dose prediction. Using AUEC for clinical dose prediction could also potentially cut down animal use by reducing or avoiding dose fractionation experiments.

摘要

药代动力学/药效动力学(PK/PD)指数利用曲线下面积、最大浓度以及体内药物浓度维持在临界水平以上的时间,常用于传染病领域从动物疗效实验中预测临床剂量。这些指数没有充分利用非临床数据,而且预测取决于动物 PK 曲线的形状,这由特定物种的吸收、分布、代谢和消除特性以及疗效实验中使用的给药方案决定。受药效学驱动疗效的原理启发,我们使用广义病原体动力学模型进行了模拟,以评估替代疗效预测指标的特性:使用体外 PD 和体内 PK 计算得到的效应曲线下面积(AUEC)。在广泛的假设情况下,AUEC 与体内疗效始终表现出与方案无关的强相关性(R 0.76-0.98),优于所有其他指数。这些发现证明了 AUEC 应该在实践中被视为跨物种剂量预测的翻译工具。使用 AUEC 进行临床剂量预测,还可以通过减少或避免剂量分割实验,减少动物使用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/acee/9381909/c25931189c72/PSP4-11-1029-g003.jpg

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