Engineered AAV Capsids with Enhanced Extracellular Vesicle Loading via Rational Design and Directed Evolution

Abstract

Overcoming immune responses and ensuring efficient delivery remain key obstacles to advancing adeno-associated virus (AAV)-based genetic medicines into the clinic. Encapsulating AAVs within naturally secreted extracellular vesicles (EV-AAV) can shield them from neutralising antibodies and increase transduction efficiency; however, wild-type AAV capsids are incorporated into EVs inefficiently, with the percentage of total AAV genomes in the EV-AAV fraction ranging from 0.5% to 12%, depending on AAV serotype. Here, we establish a high-throughput screening platform that links AAV capsid genotype to EV encapsulation phenotype to improve encapsulation efficiency. We combined rational design with directed evolution to engineer AAV9 variants that preferentially sort into EVs. A library of 392,550 AAV9 capsid mutants was generated by inserting Late (L) domains, established EV-sorting motifs, and fully random amino acid inserts in a surface-exposed region of the AAV9 capsid protein. Screening data were analysed using a statistical model-assisted protein selection (SMAPS) workflow - developed as a novel quantitative modelling and selection strategy for protein engineering challenges of this kind. SMAPS functions as a digital twin of the protein variants in our high-throughput screen, allowing us to monitor uncertainty in copy numbers over multiple steps in the experimental workflow. Using SMAPS, we identified five lead hits, which showed up to threefold increased EV-AAV yield compared to wild-type, without altering particle size. Moreover, in vitro transduction experiments demonstrated that the lead AAV9 capsid exhibits substantially higher transduction efficiency compared to wild-type AAV9 and retained functional activity in the presence of neutralising anti-AAV9 antibodies. Collectively, our study: (i) delivers a scalable high-throughput screen for EV-AAV engineering, (ii) introduces the fields first statistical model-guided quality-control framework for directed evolution, and (iii) provides best-in-class capsids for EV encapsulation.