Auto‐transduction in lentiviral vector bioprocessing: A quantitative assessment and a novel inhibition strategy
Year: 2024
Type: article
Abstract: Lentiviral vectors are highly efficient gene delivery vehicles used extensively in the rapidly growing field of cell and gene therapy. Demand for efficient, large-scale, lentiviral vector bioprocessing is growing as more therapies reach late-stage clinical trials and are commercialized. However, despite substantial progress, several process inefficiencies remain. The unintended auto-transduction of viral vector-producing cells by newly synthesized lentiviral vector particles during manufacturing processes constitutes one such inefficiency which remains largely unaddressed. In this study, we determined that over 60% of functional lentiviral vector particles produced during an upstream production process were lost to auto-transduction, highlighting a major process inefficiency likely widespread within the industry. Auto-transduction of cells by particles pseudotyped with the widely used vesicular stomatitis virus G protein was inhibited via the adoption of a reduced extracellular pH during vector production, impairing the ability of the vector to interact with its target receptor. Employing a posttransfection pH shift to pH 6.7-6.8 resulted in a sevenfold reduction in vector genome integration events, arising from lentiviral vector-mediated transduction, within viral vector-producing cell populations and ultimately resulted in improved lentiviral vector production kinetics. The proposed strategy is scalable and cost-effective, providing an industrially relevant approach to improve lentiviral vector production efficiencies.
Source: Biotechnology and Bioengineering
DOI: 10.1002/bit.28834
Authors: Thomas Williams‐Fegredo, Lee Davies, Carol Knevelman, James Miskin, Kyriacos Mitrophanous, Qasim A. Rafiq
Institutions: Oxford BioMedica (United Kingdom), University College London
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $5,010
OpenAlex ID: W4402343433
