基于模块化实时监控平台探究吸入粉雾剂载体对肺部药物递送过程的影响机制

吸入粉雾剂具有广阔的市场前景，而高效、低毒吸入粉雾剂研发是亟需攻克的重点领域。载体是吸入粉雾剂递送药物的主体，构建高效递送吸入载体是一种有效策略。但由于缺乏对肺部药物递送过程及影响机理的充分认知，无法精准、高效地指导载体的定向构建，其根本原因在于现有评价体系的局限性。为此，本项目提出构建模块化实时监控平台，实现肺部药物递送过程的分步检测、实时监控及快速解析。前期研究结果证明，此平台可实现肺部递送过程中药物、药物团聚和载体的实时监控，相关结果与肺部药物递送效率具有良好相关性，且可进一步获得具体递送过程，具有用于递送机理研究的巨大潜力。本项目拟构建一系列不同理化性质吸入载体，通过上述平台阐明载体理化性质对肺部药物递送过程的影响机制，并建立载体理化性质与肺部药物递送效率的人工神经网络及定量关系。基于上述定量关系，提出高效递送吸入载体定向构建的理性化策略，为吸入粉雾剂研发提供理论基础与技术支撑。

Dry powder inhalers (DPIs) are known to hold broad market prospects and great application potential. The aerosolization performance enhancement is the pressing need for DPI formulations development. Carriers play a critical role in DPIs since they can facilitate drugs delivery to the lung. Although the construction of efficient carriers has been considered as an effective strategy to improve the aerosolization performance, the absence of awareness on the pulmonary drug delivery processes (PDDPs) and the relevant influence mechanism hinders its precise construction and application. The fundamental reason lies in the limitations of available evaluation approaches of DPIs, which unable to monitor the PDDPs in real-time efficiently and accurately. In view of this, modular process analysis platform (MPAP) is proposed in this project, to realize the divisional detection, real-time monitoring and rapid analysis of PDDPs. Demonstrated by preliminary studies, MPAP could real-time monitor the release of drugs, drug aggregations and carriers in each stage of PDDPs separately, to obtain continuous and specific PDDPs, and thus to be suitable for the investigation of PDDPs mechanism. The presented project schedules to construct a series of carriers with diverse physicochemical properties, and then clarify its influence mechanism on the PDDPs based on MPAP. The artificial neural network and quantitative relation between the physicochemical properties of carriers and aerosolization performance will be established, and thus a more accurate and precise strategy for efficient carrier construction would be obtained. Ultimately, it is expected that this project may provide a new theoretical insight and experimental support for DPIs research and development.