Accelerating ionizable lipid discovery for mRNA delivery using machine learning and combinatorial chemistry.

To unlock the full promise of messenger (mRNA) therapies, expanding the toolkit of lipid nanoparticles is paramount. However, a pivotal component of lipid nanoparticle development that remains a bottleneck is identifying new ionizable lipids. Here we describe an accelerated approach to discovering effective ionizable lipids for mRNA delivery that combines machine learning with advanced combinatorial chemistry tools.

Enhancing the immunogenicity of lipid-nanoparticle mRNA vaccines by adjuvanting the ionizable lipid and the mRNA.

To elicit optimal immune responses, messenger RNA vaccines require intracellular delivery of the mRNA and the careful use of adjuvants. Here we report a multiply adjuvanted mRNA vaccine consisting of lipid nanoparticles encapsulating an mRNA-encoded antigen, optimized for efficient mRNA delivery and for the enhanced activation of innate and adaptive responses. We optimized the vaccine by screening a library of 480 biodegradable ionizable lipids with headgroups adjuvanted with cyclic amines and by adjuvanting the mRNA-encoded antigen by fusing it with a natural adjuvant derived from the C3 complement protein.

Polymeric Nanocarrier Formulations of Biologics Using Inverse Flash NanoPrecipitation.

The encapsulation of water-soluble therapeutics and biologics into nanocarriers to produce novel therapeutics has been envisioned for decades, but clinical translation has been hampered by complex synthesis strategies. The methods that have been developed are often limited by poor encapsulation efficiency/loading or complex processing to achieve therapeutic loadings high enough to be medically relevant. To address this unmet need, we introduce a solubility-driven self-assembly process to form polymeric nanocarriers comprising a biologic in a hydrophilic core, encapsulated by a poly(lactic acid) shell, and stabilized by a poly(ethylene glycol) brush.