Optimisation and Scale up of Engineered Extracellular Vesicles with Enhanced Therapeutic Efficacy

Extracellular vesicles (EVs) are nanoscale particles that transport biomolecules between cells, with remarkable ability to cross biological barriers. EVs have significant therapeutic potential due to inherent therapeutic properties and/or drug delivery potential. EVs outperform lipid nanoparticles (LNPs) in cellular delivery of RNA. To fulfil their potential as future advanced and powerful therapeutics, EVs require engineering to modulate their biological interaction (e.g. targeting) and enable drug loading. Generation of EV-LNP hybrids is the most promising EV engineering approach. However, the rules governing this engineering, including optimisation of EV-LNP fusion, remain completely unknown. We will utilise Machine Learning (ML) approaches, including both black box (Bayesian optimisation, particle swarm optimisation) and mechanistic, physics-based process models, to perform closed loop-optimisations and generate data for digital twins to gain process understanding. This project aims to address this knowledge gap and generate a scalable manufacturing approach for engineering of EVs into next generation therapies.