Faculty of Pharmacy and Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran.
Advanced Drug Delivery Group, School of Pharmacy, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.
Int J Pharm. 2021 Jun 1;602:120568. doi: 10.1016/j.ijpharm.2021.120568. Epub 2021 Apr 2.
Dry powder inhalers (DPI) are widely used systems for pulmonary delivery of therapeutics. The inhalation performance of DPIs is influenced by formulation features, inhaler device and inhalation pattern. The current review presents the affecting factors with great focus on powder characteristics which include particle size, shape, surface, density, hygroscopicity and crystallinity. The properties of a formulation are greatly influenced by a number of physicochemical factors of drug and added excipients. Since available particle engineering techniques result in particles with a set of modifications, it is difficult to distinguish the effect of an individual feature on powder deposition behavior. This necessitates developing a predictive model capable of describing all influential factors on dry powder inhaler delivery. Therefore, in the current study, a model was constructed to correlate the inhaler device properties, inhalation flow rate, particle characteristics and drug/excipient physicochemical properties with the resultant fine particle fraction. The r value of established correlation was 0.74 indicating 86% variability in FPF values is explained by the model with the mean absolute errors of 0.22 for the predicted values. The authors believe that this model is capable of predicting the lung deposition pattern of a formulation with an acceptable precision when the type of inhaler device, inhalation flow rate, physicochemical behavior of active and inactive ingredients and the particle characteristics of DPI formulations are considered.
干粉吸入器（DPI）广泛用于肺部给药治疗。DPI 的吸入性能受制剂特征、吸入器装置和吸入方式的影响。目前的综述重点介绍了影响干粉吸入器输送的因素，其中包括粉末特性，如粒径、形状、表面、密度、吸湿性和结晶度。制剂的性质受药物和添加赋形剂的许多物理化学因素的极大影响。由于现有的颗粒工程技术会导致颗粒具有一系列的修饰，因此很难区分单个特征对粉末沉积行为的影响。这就需要开发一种能够描述干粉吸入器输送所有影响因素的预测模型。因此，在目前的研究中，建立了一个模型来关联吸入器装置特性、吸入气流率、颗粒特性以及药物/赋形剂的物理化学特性与细颗粒分数（FPF）的关系。建立的相关性的 r 值为 0.74，表明模型可以解释 86%的 FPF 值的变化，预测值的平均绝对误差为 0.22。作者认为，当考虑吸入器装置类型、吸入气流率、活性和非活性成分的物理化学行为以及干粉吸入制剂的颗粒特性时，该模型能够以可接受的精度预测制剂的肺部沉积模式。
