Integrating Artificial Intelligence with Liposomal Nanocarriers for Advanced Drug Delivery

Liposomal drug delivery systems have achieved notable clinical success; however, their wider translation is often limited by complex formulation variables, unpredictable process behavior, and scalability challenges. Traditional trial-and-error optimization strategies struggle to address the nonlinear relationships that govern liposome quality, performance, and reproducibility. Recent advances in Artificial Intelligence (AI) and Machine Learning (ML) offer promising alternatives by enabling data-driven formulation design and process understanding.

This review critically evaluates peer-reviewed studies published over the past decade that report the application of AI and ML in liposomal formulation development, microfluidic manufacturing, quality attribute prediction, and therapeutic performance assessment. Emphasis was placed on studies integrating computational models with experimental and advanced manufacturing workflows.

The reviewed literature demonstrates that AI-based models, including neural networks, deep learning architectures, and ensemble approaches, consistently outperform conventional statistical tools in predicting critical quality attributes such as particle size, polydispersity index, drug entrapment efficiency, stability, and release behavior. Integration of AI with microfluidic platforms further enables real-time process monitoring, improved reproducibility, and continuous manufacturing.

These findings highlight AI as a powerful framework for capturing complex relationships among formulation, process, and performance. Beyond optimization, AI facilitates deeper mechanistic understanding and supports predictive control strategies for liposomal systems.

AI-integrated approaches represent a paradigm shift in liposome development, offering accelerated formulation design, enhanced scalability, and improved prospects for reliable clinical translation.