Machine learning model to predict the fitness of AAV capsids for gene therapy
A new study in describes a machine learning (ML) model that can be used as a surrogate for laborious in vitro experiments. This in silico approach aims to increase the fitness of clinical adeno-associated virus (AAV) capsids to make gene therapies more economically viable for patients.
Developing AAV capsids with improved yield, or fitness, is a key strategy for reducing manufacturing costs in order to make gene therapies more affordable.
Christian Mueller and co-authors from Sanofi describe a state-of-the-art ML model that predicts the fitness of AAV2 capsid mutants based on the amino acid sequence of the capsid monomer.
"By combining a protein language model (PLM) and classical ML techniques, our model achieved a significantly high prediction accuracy (Pearson correlation = 0.818) for capsid fitness," stated the investigators. "Importantly, tests on completely independent datasets showed robustness and generalizability of our model, even for multi-mutant AAV capsids."
"The emergence of artificial intelligence (AI)-based approaches is an exciting development in capsid engineering that has the potential to be more systematic, comprehensive, and cost-effective than traditional directed evolution and rational design-based strategies. The study by Wu et al. is a great step forward in developing AI tools for the gene therapy field," says Managing Editor of Human Gene Therapy Thomas Gallagher, Ph.D., from the University of Massachusetts Chan Medical School.
More information: Jason Wu et al, Prediction of Adeno-Associated Virus Fitness with a Protein Language-Based Machine Learning Model, Human Gene Therapy (2025).
Journal information: Human Gene Therapy
Provided by Mary Ann Liebert, Inc
