长效可注射(LAI)制剂可在数周至数月的延长期内提供持续的药物释放,以提高疗效。安全,和合规。然而,由于对注射颗粒的组织反应的了解有限,在LAI药物产品的开发和监管评估中出现了许多挑战(例如,炎症)影响体内性能。基于机制的计算机模拟方法可能支持对LAI-生理学相互作用的理解。这项研究的目的如下:(1)使用机械建模方法来描绘DepoSubQProvera®和临床前物种中制剂变体的体内性能;(2)根据从动物模型获得的知识来预测人类暴露。PBPK模型评估了LAI给药中涉及的不同元素,并显示(1)有效的体内粒径可能大于测得的体外粒径,这可能是由于注射部位的颗粒聚集,和(2)局部炎症是注射部位的关键过程,导致储库体积的短暂增加。这项工作强调了机械建模方法如何识别可能影响LAI体内性能的关键生理事件和产品属性。
Long-acting injectable (LAI) formulations provide sustained drug release over an extended period ranging from weeks to several months to improve efficacy, safety, and compliance. Nevertheless, many challenges arise in the development and regulatory assessment of LAI drug products due to a limited understanding of the tissue response to injected particles (e.g., inflammation) impacting in vivo performance. Mechanism-based in silico methods may support the understanding of LAI-physiology interactions. The objectives of this study were as follows: (1) to use a mechanistic modeling approach to delineate the in vivo performance of DepoSubQ Provera® and formulation variants in preclinical species; (2) to predict human exposure based on the knowledge gained from the animal model. The PBPK model evaluated different elements involved in LAI administration and showed that (1) the effective in vivo particle size is potentially larger than the measured in vitro particle size, which could be due to particle aggregation at the injection site, and (2) local inflammation is a key process at the injection site that results in a transient increase in depot volume. This work highlights how a mechanistic modeling approach can identify critical physiological events and product attributes that may affect the in vivo performance of LAIs.