Review demonstrates AI-guided microfluidic platforms accelerate nanocarrier synthesis in drug formulation, highlighting transitions toward automated self-driving laboratories.
The development of drug delivery systems (DDSs) is moving from empirical, trial-and-error formulation toward data-driven, artificial intelligence (AI)-guided, and automated workflows. Microfluidics provides precise control of microscale fluids, enabling high-throughput production of relatively homogeneous drug carriers such as liposomes, lipid nanoparticles (LNPs), and polymeric micelles. However, the high-dimensional parameter space of microfluidic reactors often exceeds the capacity of manual optimization. This review examines the integration of AI, particularly machine learning (ML), deep learning (DL), and Bayesian optimization, into microfluidic platforms for accelerating DDS design, optimizing critical quality attributes (CQAs), and supporting self-driving laboratory workflows. We discuss the technical foundations of AI-enabled microfluidics, applications in nanocarrier synthesis and phenotypic screening, and the evolving regulatory landscape for AI-assisted pharmaceutical development.
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Tang et al. (2026) studied this question.
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