High‐Throughput Strategies for Streamlining Lipid Nanoparticle Development Pipeline

This review highlights emerging state‐of‐the‐art high‐throughput strategies for optimising lipid nanoparticle formulation. By integrating combinatorial design, characterization, in vitro/in vivo screening, automation, and machine learning into a closed‐loop framework, it provides a roadmap to streamline discovery and accelerate the translation of clinically viable lipid nanoparticles for applications from vaccines to therapies. Abstract Lipid nanoparticles (LNPs) have become clinically validated nanocarriers for nucleic acid delivery, enabling applications in mRNA vaccines and therapies for cancer, ocular, and infectious diseases. Identifying LNPs formulations with optimal physicochemical and pharmacokinetic properties using traditional low‐throughput methods is resource‐intensive and impractical for evaluating large libraries. Recent advances in automation, high‐throughput platforms for lipid synthesis, characterization, and screening tools are transforming the landscape of LNP formulation. These strategies enable rapid multi‐parametric generation and evaluation of hundreds to thousands of formulations across key properties such as size, charge, stability, biodistribution, cellular uptake, and intracellular trafficking. In parallel, advanced biomimetic models and in vivo multiplexed barcoding screening strategies provide deeper insights into tissue targeting and therapeutic delivery outcomes. This review provides an integrated framework that combines automation with high‐throughput combinatorial synthesis, characterization, and in vitro/in vivo screening tools. In this development pipeline, performance benchmarks applied at each step systematically exclude suboptimal candidates, ensuring that only clinically viable LNP candidates advance. Future directions, including automation, high‐throughput, and closed‐loop machine learning guided design strategies, are further discussed to advance the development of next‐generation LNP therapeutics and accelerate their translation from bench to bedside. This review highlights emerging state-of-the-art high-throughput strategies for optimising lipid nanoparticle formulation. By integrating combinatorial design, characterization, in vitro/in vivo screening, automation, and machine learning into a closed-loop framework, it provides a roadmap to streamline discovery and accelerate the translation of clinically viable lipid nanoparticles for applications from vaccines to therapies. Abstract Lipid nanoparticles (LNPs) have become clinically validated nanocarriers for nucleic acid delivery, enabling applications in mRNA vaccines and therapies for cancer, ocular, and infectious diseases. Identifying LNPs formulations with optimal physicochemical and pharmacokinetic properties using traditional low-throughput methods is resource-intensive and impractical for evaluating large libraries. Recent advances in automation, high-throughput platforms for lipid synthesis, characterization, and screening tools are transforming the landscape of LNP formulation. These strategies enable rapid multi-parametric generation and evaluation of hundreds to thousands of formulations across key properties such as size, charge, stability, biodistribution, cellular uptake, and intracellular trafficking. In parallel, advanced biomimetic models and in vivo multiplexed barcoding screening strategies provide deeper insights into tissue targeting and therapeutic delivery outcomes. This review provides an integrated framework that combines automation with high-throughput combinatorial synthesis, characterization, and in vitro/in vivo screening tools. In this development pipeline, performance benchmarks applied at each step systematically exclude suboptimal candidates, ensuring that only clinically viable LNP candidates advance. Future directions, including automation, high-throughput, and closed-loop machine learning guided design strategies, are further discussed to advance the development of next-generation LNP therapeutics and accelerate their translation from bench to bedside. Advanced Science, Volume 12, Issue 42, November 13, 2025.