Self-amplifying RNA (saRNA) is a promising biotherapeutics research area due to the low dosage requirements compared to non-replicating mRNA. Upon saRNA entry to a host cell, the alphavirus-derived replicase is translated by host machinery and the replicase complex then amplifies the antigen sequence of interest. We are leveraging NGS approaches to deeply characterize each stage of saRNA replication and host response. These methods may ultimately facilitate regulatory documentation and saRNA design.

Lipid nanoparticles (LNPs) are highly sensitive to processing conditions, making it difficult to reproducibly achieve target quality attributes. This talk will examine the physicochemical origins of this sensitivity in the context of LNP self-assembly pathways. We will demonstrate how mechanistic insight enables the design of more robust processes that selectively tune specific quality attributes without compromising others. This rational method also supports predictive manufacturing by integrating mechanistic models with data-driven approaches.

A major bottleneck in translating mRNA therapies to market is seamless scale-up from bench to manufacturing. We present a "size- and morphology-control" technique to produce bleb-like mRNA-LNPs that scales from microfluidic systems to manufacturing-relevant turbulent mixers, while preserving critical quality attributes and efficacy—enabling both personalized medicines and large-scale vaccine production.