Navigating Lipid Nanostructure Design Space Through Continuous Microfluidic Automation

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Abstract
The versatility of nanoscale lipid particles has positioned them as the scaffold of choice for biomedical delivery, synthetic membrane engineering, and fundamental biophysical exploration. Across these fields, rational particle design has become a major bottleneck. Lipid composition, stoichiometry, size, morphology, phase behavior, and biophysical properties combine into an enormous, high‑dimensional space that is inherently difficult to explore, making conventional optimization approaches slow and limited. Identifying optimal lipid compositions in this vast space necessitates high‐throughput screening based on efficient low‐cost automation. Here we introduce a high‐throughput microfluidic platform for rapid screening of lipid nanoparticles, offering precise, programmable control over composition and morphology. The system integrates on‐chip microfluidic metering of lipid stocks with continuous particle self‐assembly, followed by robotic collection into 96‐well plates. This workflow enables the generation of over 200 unique formulations per hour, delivering a several‐orders‐of‐magnitude increase in throughput relative to conventional approaches. We apply the platform to systematically map biophysical space at unprecedented resolution, validate compositional trends in transfection efficiency, and identify non‐lamellar formulations with optimal functional performance. By coupling scalable synthesis with automated screening, we expect this platform to provide a robust foundation for data‐driven and AI‐integrated discovery of self‐assembled nanomaterials including lipid, polymeric, and biomolecular assemblies.
Funding Information
  • Biotechnology and Biological Sciences Research Council (BB/Z514895/1)
  • Imperial College London
  • Royal Academy of Engineering (CiET2021∖94)
  • Engineering and Physical Sciences Research Council (EP/Y530529/1)
  • Engineering and Physical Sciences Research Council (EP/V048651/1)

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