Optimized Long-Read Sequencing & Quality Assessment for AAV Viral Vectors

Optimized Long-read QA for AAV
Categories
Drug substance
Process control
Data

Industry Need

Gene therapy using adeno-associated viruses (AAVs) is transforming medicine by treating previously incurable genetic disorders. However, verifying the quality of these therapies remains challenging. Current methods for checking AAV DNA are inadequate – they're either too imprecise to detect important genetic changes or too slow and expensive for routine use.

Approach

This project develops quality control techniques using long-read DNA sequencing technology from Oxford Nanopore Technologies and Pacific Biosciences. Unlike current methods, these platforms can read entire AAV genomes in a single pass, revealing crucial details about their structure and composition.

Impacts

Enabling earlier detection of vector genome issues during production

Accelerating manufacturing troubleshooting with detailed sequence information

Reducing development timelines by 30-50% through faster analytical turnaround

Enhancing product safety and consistency across the biopharmaceutical ecosystem

Supporting regulatory submissions with comprehensive genomic characterization data

Value Statement/Outcomes

This initiative is projected to cut AAV development timelines by up to 50%, significantly reduce quality control costs, and strengthen product safety and consistency. By enabling rapid, high-accuracy genomic analysis, it positions manufacturers to accelerate market entry and realize substantial long-term savings across the gene therapy pipeline.

Outputs/Deliverables

Validated protocols for AAV genome sequencing achieving >99.99% consensus accuracy, with detection of variants at

Optimized sample preparation workflows for both AAV plasmids and packaged vectors, including methods for ITR preservation

Control materials with engineered variants for method benchmarking and validation

End-to-end computational pipeline for processing long-read AAV data, incorporating UMI consensus algorithms

Comprehensive documentation for laboratory workflows and analytical methods

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Project Lead

Johns Hopkins University

Johns Hopkins University