DMPK, Respiratory, Inflammation, and Autoimmunity, IMED Biotech Unit, AstraZeneca, Gothenburg, Sweden.
DMPK, Cardiovascular and Metabolic Diseases, IMED Biotech Unit, AstraZeneca, Gothenburg, Sweden.
CPT Pharmacometrics Syst Pharmacol. 2018 Mar;7(3):147-157. doi: 10.1002/psp4.12270. Epub 2017 Dec 27.
Translational pharmacokinetic (PK) models are needed to describe and predict drug concentration-time profiles in lung tissue at the site of action to enable animal-to-man translation and prediction of efficacy in humans for inhaled medicines. Current pulmonary PK models are generally descriptive rather than predictive, drug/compound specific, and fail to show successful cross-species translation. The objective of this work was to develop a robust compartmental modeling approach that captures key features of lung and systemic PK after pulmonary administration of a set of 12 soluble drugs containing single basic, dibasic, or cationic functional groups. The model is shown to allow translation between animal species and predicts drug concentrations in human lungs that correlate with the forced expiratory volume for different classes of bronchodilators. Thus, the pulmonary modeling approach has potential to be a key component in the prediction of human PK, efficacy, and safety for future inhaled medicines.
转化药代动力学 (PK) 模型用于描述和预测作用部位肺部组织中的药物浓度-时间曲线,以便实现动物到人之间的转化,并预测吸入药物在人体中的疗效。目前的肺部 PK 模型通常是描述性的而不是预测性的,针对特定药物/化合物,无法成功进行跨物种转化。这项工作的目的是开发一种稳健的房室建模方法,该方法可以捕获一组包含单碱性、二碱性或阳离子官能团的 12 种可溶性药物经肺部给药后肺部和全身 PK 的关键特征。该模型可用于在动物物种之间进行转化,并预测不同类别的支气管扩张剂的药物在人肺部中的浓度,与用力呼气量相关。因此,肺部建模方法有可能成为预测未来吸入药物的人体 PK、疗效和安全性的关键组成部分。