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.
Highlights
Approved associated virus (AAV)-based therapeutics and numerous therapeutic candidates in advanced clinical development [1] have demonstrated the transformative and life-saving potential of viral capsids as vectors for gene therapy (GT)
Each methodology has contributed valuable capsids to the available catalog of GT vectors, but limitations related to speed and throughput of discovery persist because the total number of possible capsids far exceeds the capacity of current screening approaches
Effective machine learning methods often make use of internal latent representations, known as embeddings, which attempt to represent the information contained in raw inputs in a way that is more amenable to human understanding
Summary
Approved AAV-based therapeutics and numerous therapeutic candidates in advanced clinical development [1] have demonstrated the transformative and life-saving potential of viral capsids as vectors for gene therapy (GT). Development of generation capsids that enable more precise, efficient, and durable gene delivery will be key to improving the effectiveness and safety of such therapies In this perspective, we explore how high throughput (HT) measurement and characterization methods can be combined with machine learning (ML) approaches to identify such capsids by efficiently optimizing capsid sequences for both improved transduction and reduced immunogenicity. We explore how high throughput (HT) measurement and characterization methods can be combined with machine learning (ML) approaches to identify such capsids by efficiently optimizing capsid sequences for both improved transduction and reduced immunogenicity Combining these technologies will generate capsid-mediated gene therapies with broader therapeutic uses that are accessible to all individuals in need
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