4.8 Article

Development of a Two-Dimensional Liquid Chromatography-Mass Spectrometry Platform for Simultaneous Multi-Attribute Characterization of Adeno-Associated Viruses

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ANALYTICAL CHEMISTRY
卷 94, 期 7, 页码 3219-3226

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AMER CHEMICAL SOC
DOI: 10.1021/acs.analchem.1c04873

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Adeno-associated viruses (AAVs) have emerged as an attractive vector for gene therapy applications. To support process development and product characterization, a new online two-dimensional liquid chromatography-mass spectrometry method was developed for AAV characterization.
Adeno-associated viruses (AAVs) are non-enveloped, single-stranded DNA viruses that have recently emerged as an attractive vector for delivering genetic materials to hosts for gene therapy applications. Due to their ability to transduce a wide range of species and tissues in vivo, low risk of immunotoxicity, and mild innate and adaptive immune responses, AAVs are currently used in research and clinical studies as a monotherapy or with other biomolecules to perform gene editing, replacement, addition, and silencing. As AAVs are a new and complex therapeutic modality with molecular weights into the megadalton range, new analytical techniques are therefore needed to support process development, product characterization, and release. In this study, an online two-dimensional liquid chromatography-mass spectrometry (2DLC-MS) method was developed for AAV characterization. Our method uses high-resolution anion-exchange chromatography (AEX) in the first dimension to separate and measure empty and full capsids in AAV samples, followed by reversed-phase liquid chromatography coupled with mass spectrometry (RPLC-MS) to separate and characterize viral proteins. In this technique, online denaturation and removal of MS-incompatible salt were performed following AEX. The viral proteins present in the peak of interest after first-dimensional AEX are subjected to intact protein separation on the second-dimensional RPLC column and then characterized by MS. The 2DLC-MS method demonstrated in this study allows for high-throughput and multi-attribute AAV characterization in a single run, with minimal sample handling required for different AAV serotypes.

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