Polymer microparticles in an evolving drug delivery landscape: challenges and the role of machine learning
Zeqing Bao, Jongwhi Kim, Frantz Le Dévédec, Aaron J. Clasky, Christine Allen
Abstract
Polymer microparticles (MPs) have long been a cornerstone of long-acting injectable (LAI) drug delivery, offering controlled drug release, reduced dosing frequency, and improved patient adherence. Among these, poly(lactide-co-glycolide) (PLGA)-based MPs have demonstrated clinical viability and remain the most widely used platform. However, the broad and complex formulation design space, coupled with significant manufacturing challenges, has limited further development and often leads scientists to explore alternative delivery strategies. This paper examines the key barriers to polymer MP development and their implications for the advancement of LAI therapies. We also highlight the transformative potential of machine learning (ML) in addressing these challenges. ML-driven approaches offer new opportunities to navigate formulation complexity, streamline development, and accelerate the creation of innovative, scalable LAI systems.