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Isolation of Next-Generation Gene Therapy Vectors through Engineering, Barcoding, and Screening of Adeno-Associated Virus AAV Capsid Variants
Published on: October 18, 2022
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Systematic multi-trait AAV capsid engineering for efficient gene delivery
Fatma-Elzahraa Eid1,2, Albert T Chen3, Ken Y Chan3
1Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA. fatma@broadinstitute.org.
Nature Communications
|August 3, 2024
Summary
Fit4Function, a machine learning approach, engineers multi-trait adeno-associated virus (AAV) capsids for gene therapy. This method efficiently identifies clinically relevant AAV vectors with improved manufacturability and targeting.
Area of Science:
- Biotechnology
- Molecular Biology
- Gene Therapy
Background:
- Developing gene therapies requires efficient and manufacturable viral vectors like adeno-associated virus (AAV).
- Current AAV capsid library screening methods are inefficient for identifying vectors with multiple essential clinical traits.
Purpose of the Study:
- To present Fit4Function, a generalizable machine learning (ML) approach for systematically engineering multi-trait AAV capsids.
- To develop a method for creating AAV vectors with enhanced manufacturability and specific cellular targeting for clinical applications.
Main Methods:
- Leveraged a capsid library for uniform sampling of the manufacturable sequence space to generate reproducible screening data.
- Trained six sequence-to-function ML models using in vivo (mouse) and in vitro (human) data.
- Combined ML models to design and validate a multi-trait (liver-targeted, manufacturable) AAV capsid library.
Main Results:
- 88% of the designed multi-trait AAV capsid library variants met all six predetermined criteria.
- ML models accurately predicted AAV capsid biodistribution in macaques using data from mice and humans.
- Top AAV candidates showed high production yields, efficient murine liver transduction, and significantly enhanced human hepatocyte transduction (up to 1000-fold).
Conclusions:
- The Fit4Function strategy enables prediction of cross-species traits for peptide-modified AAV capsids.
- This ML approach is crucial for developing an atlas to predict AAV capsid performance across numerous traits, advancing gene therapy vector development.
