Inefficient injection of microparticles through conventional hypodermic needles can impose serious challenges on clinical translation of biopharmaceutical drugs and microparticle-based drug formulations. This study aims to determine the important factors affecting microparticle injectability and establish a predictive framework using computational fluid dynamics, design of experiments, and machine learning. A numerical multiphysics model was developed to examine microparticle flow and needle blockage in a syringe-needle system. Using experimental data, a simple empirical mathematical model was introduced. Results from injection experiments were subsequently incorporated into an artificial neural network to establish a predictive framework for injectability. Last, simulations and experimental results contributed to the design of a syringe that maximizes injectability in vitro and in vivo. The custom injection system enabled a sixfold increase in injectability of large microparticles compared to a commercial syringe. This study highlights the importance of the proposed framework for optimal injection of microparticle-based drugs by parenteral routes.

通过传统皮下注射针注射微粒的效率低下，可能对生物制药药物和基于微粒的药物制剂的临床转化带来严峻的挑战。本研究旨在确定影响微粒可注射性的重要因素，并利用计算流体动力学、实验设计和机器学习建立预测框架。开发了一种数值多物理场模型来检查注射器针头系统中的微粒流动和针头堵塞。利用实验数据，引入了一个简单的经验数学模型。注射实验的结果随后被纳入人工神经网络中，以建立可注射性的预测框架。最后，模拟和实验结果有助于设计最大限度地提高体外和体内注射能力的注射器。与商用注射器相比，定制注射系统使大微粒的注射能力提高了六倍。这项研究强调了所提出的框架对于通过肠胃外途径最佳注射基于微粒的药物的重要性。