药代动力学
体内
车辆段
药理学
医学
肌肉注射
吸收(声学)
给药途径
全身给药
血浆浓度
离体
药效学
口服
化学
全身循环
包络线(雷达)
注射部位
生物医学工程
麻醉
悬挂(拓扑)
效力
炎症
皮下组织
分布(数学)
作者
Daniela Amaral Silva,Maxime Le Merdy,James K. Mullin,Nilesh Malavia,Khondoker Alam,Eleftheria Tsakalozou,Abdullah Al Shoyaib,Yan Wang,Diane J. Burgess,Viera Lukáčová
出处
期刊:Aaps Journal
[Springer Science+Business Media]
日期:2025-10-24
卷期号:28 (1): 4-4
被引量:6
标识
DOI:10.1208/s12248-025-01171-1
摘要
Abstract The tissue response to long-acting injectables (LAIs) suspension injection may impact the product in vivo performance. One such response is the formation of an inflammatory cell layer (ICL) resulting in an envelope around the injected particles. This study aims to use a mechanistic model to describe the clinical in vivo exposure and performance of an intramuscular LAI suspension and evaluate impact of ICL physiological response at the injection site in humans. Aripiprazole lauroxil (AR-L) was used as the model drug. A baseline pharmacokinetics model was built and validated for aripiprazole. The impact of inflammation on the LAI in vivo performance was assessed by including an ICL model. The developed pharmacokinetic model adequately described the observed plasma profiles of AR following intravenous and oral administration in humans. The initial intramuscular predictions assumed that the absorption rate is dependent on the dissolution and partitioning of AR-L into the systemic circulation from the intramuscular (IM) depot. The simulation resulted in a shape mismatch between the simulated and observed data and an earlier predicted T max. The inclusion of an ICL in the model resulted in adequate predictions (fold errors less than 25%) of the exposure and shape of the plasma concentration–time profiles. Utilizing a time-dependent change in ICL thickness resulted in reasonable predictions of AR pharmacokinetic profiles following IM administration of multiple strengths of the AR-L suspension. This shows the utility of physiologically based pharmacokinetic (PBPK) model in mechanistically describing the in vivo performance of LAIs. Graphical Abstract
科研通智能强力驱动
Strongly Powered by AbleSci AI