Biopharma
FlowCam for AAV Aggregation Detection in Gene Therapies
Assess the stability and quality of the next generation of biotherapeutics designed to treat cancer, immunize against pathogens, and address genetic disorders.
Flow imaging microscopy detects aggregation of viral vectors such as AAVs, lentiviruses, and retroviruses as well as non-viral therapies like LNPs and exosomes.
Use FlowCam to:
- Monitor viral and nonviral vector aggregation in gene therapy formulations and drug products during formulation and process design
- Distinguish between inherent vector aggregates, intrinsic particles like glass flakes, and extrinsic contaminants
- Obtain reliable particle size and concentration measurements recommended by USP <1788> to support quality control processes
- Characterize particles as small as 300 nm with industry-leading image quality
“In the last 5 years, the [cell and gene therapy] field has expanded dramatically, with unprecedented approvals. There is now a greater need to move the discussion toward particulate management in cell- and gene-specific regulatory guidance.”
Sample FlowCam images of lentivirus aggregates
A FlowCam collage of particles in a heat-stressed LNP formulation
Formulation Development of Viral and Non-Viral Vectors
Like other parenteral biotherapeutics, injectables in gene therapy are subject to regulations like USP <788> that require particle testing to mitigate safety risks.
Flow imaging microscopy techniques allow for the identification and characterization of particle types in your formulation, enabling you to make formulation and process improvements during product development.
- Meet USP requirements and recommendations with FlowCam LO—a combined flow imaging and light obscuration instrument
- Detect gene vector aggregates such as LNP oligomers as small as 300 nm with FlowCam Nano
Additional Resources
Guides
Videos
Application Notes
White Papers
Published Research
- Of Particulate Importance – Flow Imaging Microscopy in Cell and Gene Therapy
- Flow Imaging Microscopy Coupled with Convolutional Neural Networks Demonstrates Reliable Method to Characterize and Identify Impurities in Cell-Based Medicinal Products
- Flow Imaging Microscopy Guides Forced Degradation of Cell-based Medicinal Products
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