A Single-Sector Higher Throughput Sedimentation Velocity Analytical Ultracentrifugation Method for Recombinant...

Recombinant adeno-associated virus (rAAV) has emerged as one of the most important gene delivery vectors in the field of gene therapy due to its unique advantages and characteristics. The empty and full ratio is a critical quality attribute in the quality control (QC) of rAAV, and its accurate evaluation is crucial for ensuring the safety, effectiveness, and consistency of gene therapy products. Analytical ultracentrifugation (AUC) technology, with its high resolution and accuracy, is widely recognized by the industry as the gold standard for identifying the empty and full ratio of rAAV. However, the conventional sedimentation velocity analytical ultracentrifugation (SV-AUC) method has limited throughput, failing to meet the large-scale detection needs of rAAV in process development and QC. This study aims to develop a single-sector higher throughput SV-AUC method without the need for a reference sector for blank control in order to improve the throughput of detecting the empty and full ratio of rAAV vectors. We optimized the traditional double-sector SV-AUC method, which requires a reference sector for blank control in the cell. By converting the light intensity data of AUC into pseudo-absorbance data, we significantly improve the analytical throughput. By tracking the variation of light intensity data with radius, we could clearly observe the sedimentation process of the rAAV sample. Despite a difference in the absolute value of pseudo-absorbance, the accurately fitted relative absorbance value and the traditional SV-AUC absorbance value with blank control were comparable, further verifying the applicability of this upgraded rAAV analytical method. The detailed comparison and verification between the upgraded method and the traditional SV-AUC method showed that the consistency and repeatability of the percentage and sedimentation coefficient were excellent both within the same cell and across different cells. The analysis results of samples from seven independent cells with a total of 14 sectors showed that the overall data exhibited good repeatability. The consistency of the high percentage empty capsid (HE) samples repeatability results was good, and the overlay of the C(s) distribution diagram also showed good pattern consistency. The relative standard deviation of the average percentage of empty, partial, and full capsids was maintained within 5%. The upgraded method demonstrated excellent consistency and repeatability in the analysis of rAAV samples with different empty and full ratios, aligning closely with the data obtained with the traditional SV-AUC method, the gold standard. Linear correlation analysis between the titers of HE samples and the overall absorbance (A value) of AUC, as well as the absorbance of empty, partial, and full capsids, revealed a good linear relationship, further confirming the applicability and reliability of the upgraded AUC method for evaluating rAAV samples with different titers. We also preliminarily explored the robustness of this method and found that even in the presence of slight fluctuations in sample volume, the test results remained stable, effectively alleviating concerns about the impact of inaccurate sample volume on the results. By dropping ink to simulate window contamination or wear, it was found that although the peak shape of the C(s) distribution was affected, the ratio results were consistent with those of the traditional SV-AUC method, proving that the new method exhibits good anti-interference ability under varying testing conditions. We conducted a comparability study on rAAV samples containing different proportions of empty, partial, and full capsids. rAAV samples with different proportions of empty and full showed high consistency and repeatability in the results obtained from both methods. In summary, the single-sector higher throughput SV-AUC method without a reference sector for blank control proposed in this study not only improves the analysis efficiency of rAAV samples but also ensures the accuracy and precision of the results, providing a new reliable analysis tool with higher throughput for gene therapy. This technology is expected to accelerate the development and evaluation process of gene therapy products.