Autonomous Lipid Nanoparticle Engineering

Abstract Lipid nanoparticles (LNPs) are the leading vehicles for encapsulating and delivering nucleic acid therapeutics. Yet, their process development remains labor-intensive and empirical, constrained by coupled quality attributes and limited mechanistic insight. We present an autonomous, pilot-scale platform for accelerating LNP process development by identifying critical process parameters (CPPs) that produce LNPs with target size attributes. The platform combines a size-control production method with inline dynamic light scattering (DLS) for real-time feedback, enabling closed-loop experimentation and accelerated optimization. With built-in automated design of experiments, dynamic parameter sweeps, and Bayesian optimization, the platform enables rapid, data-rich exploration of complex design spaces. We demonstrate that the platform rapidly maps complex process–property relationships for loaded LNPs, while minimizing experimental burden. The resulting data-rich outputs were used to develop a predictive model that quantitatively links process parameters to particle quality attributes. Operable in fully autonomous mode, the platform provides a scalable and flexible framework for rational LNP manufacturing and accelerates the broader development of nucleic acid therapeutics.