Overcoming Immunological Challenges Limiting Capsid-Mediated Gene Therapy With Machine Learning
Anna Z. Wec, Kathy S. Lin, Jamie C. Kwasnieski, Sam Sinai, Jeff Gerold, Eric D. Kelsic
Abstract
A key hurdle to making adeno-associated virus (AAV) capsid mediated gene therapy broadly beneficial to all patients is overcoming pre-existing and therapy-induced immune responses to these vectors. Recent advances in high-throughput DNA synthesis, multiplexing and sequencing technologies have accelerated engineering of improved capsid properties such as production yield, packaging efficiency, biodistribution and transduction efficiency. Here we outline how machine learning, advances in viral immunology, and high-throughput measurements can enable engineering of a new generation of de-immunized capsids beyond the antigenic landscape of natural AAVs, towards expanding the therapeutic reach of gene therapy.