In Silico Screening Accelerates Nanocarrier Design for Efficient mRNA Delivery

Abstract

Abstract Lipidic nanocarriers are a broad class of lipid‐based vectors with proven potential for packaging and delivering emerging nucleic acid therapeutics. An important early step in the clinical development cycle is large‐scale screening of diverse formulation libraries to assess particle quality and payload delivery efficiency. Due to the size of the screening space, this process can be both costly and time‐consuming. To address this, computational models capable of predicting clinically relevant physio‐chemical properties of dendrimer‐lipid nanocarriers, along with their mRNA payload delivery efficiency in human cells are developed. The models are then deployed on a large theoretical nanocarrier pool consisting of over 4.5 million formulations. Top predictions are synthesised for validation using cell‐based assays, leading to the discovery of a high quality, high performing, candidate. The methods reported here enable rapid, high‐throughput, in silico pre‐screening for high‐quality candidates, and have great potential to reduce the cost and time required to bring mRNA therapies to the clinic.

Keywords

• drug delivery
• LNP
• machine learning
• mRNA delivery
• nanocarriers
• nanoparticles