Artificial Intelligence for the Design of New RNA Nanocarriers

Abstract:
To address the need for efficient RNA delivery beyond the liver, my lab designs inhalable, biocompatible nanocarriers for siRNA delivery to the lung. While siRNA drugs typically target the liver, the lung presents undruggable targets that RNA therapeutics could address. Nebulizable nanocarriers for pulmonary RNA administration can extend delivery beyond the liver. siRNA therapeutics are encapsulated in lipid nanoparticles, LNPs or administered as GalNac-conjugates to protect RNA and facilitate membrane delivery. However, approved LNPs pose immunogenic risks, primarily target the liver, and offer low drug loading. Additionally, lipid-based carriers lack stability in lung surfactant and under nebulization stresses. To overcome these issues, we design polymer-based siRNA formulations.

Poly beta-amino ester's,PBAEs are biodegradable cationic polymers promoting nucleic acid delivery and can be customized to explore structure–function relationships. While traditional biomaterials are optimized empirically, we use Design-of-Experiments, DoE, Molecular Dynamics Simulations, and Machine Learning, ML to expedite the discovery and optimization of polycationic siRNA nanocarriers. Despite the less defined morphology of polycationic siRNA nanoparticles compared to LNPs, microfluidic assembly can efficiently address issues of polydispersity and formulation reproducibility.

siRNA-based therapeutics offer the advantage of targeting genes considered ‘undruggable’ with conventional methods. The COVID-19 pandemic underscored the challenge of developing siRNA sequences against the coronaviral genome. Advanced lung models are available to screen therapeutic efficacy against respiratory infections, asthma, COPD, and mutated oncogenes in the presence of biological barriers.