Time-dependent sorption behavior of lentiviral vectors during anion-exchange chromatography.
George PamenterLee DaviesCarol KnevelmanJames MiskinKyriacos MitrophanousDuygu DikiciogluDaniel G BracewellPublished in: Biotechnology and bioengineering (2023)
Use of lentiviral vectors (LVs) in clinical Cell and Gene Therapy applications is growing. However, functional product loss during capture chromatography, typically anion-exchange (AIEX), remains a significant unresolved challenge for the design of economic processes. Despite AIEX's extensive use, variable performance and generally low recovery is reported. This poor understanding of product loss mechanisms highlights a significant gap in our knowledge of LV adsorption and other types of vector delivery systems. This work demonstrates HIV-1-LV recovery over quaternary-amine membrane adsorbents is a function of time in the adsorbed state. Kinetic data for product loss in the column bound state was generated. Fitting a second order-like rate model, we observed a rapid drop in functional recovery due to increased irreversible binding for vectors encoding two separate transgenes ( t Y 1 / 2 ${t}_{{Y}_{1/2}}$ = 12.7 and 18.7 min). Upon gradient elution, a two-peak elution profile implicating the presence of two distinct binding subpopulations is observed. Characterizing the loss kinetics of these two subpopulations showed a higher rate of vector loss in the weaker binding peak. This work highlights time spent in the adsorbed state as a critical factor impacting LV product loss and the need for consideration in LV AIEX process development workflows.
Keyphrases
- gene therapy
- mass spectrometry
- healthcare
- high speed
- liquid chromatography
- hiv positive
- stem cells
- ionic liquid
- tandem mass spectrometry
- human immunodeficiency virus
- single cell
- men who have sex with men
- risk assessment
- hiv testing
- cell therapy
- mesenchymal stem cells
- south africa
- antiretroviral therapy
- artificial intelligence
- machine learning
- hiv aids
- electronic health record
- ms ms
- big data
- heavy metals
- data analysis
- aqueous solution