Recombinant adeno-associated virus (rAAV) has been utilized successfully for in vivo gene delivery for treatment of a variety of human diseases. To sustain the growth of recombinant AAV gene therapy products, there is a critical need for the development of accurate and robust analytical methods. Fifty percent tissue culture infectious dose (TCID50) assay is an in vitro cell-based method widely used to determine AAV infectivity, and this assay is historically viewed as a challenge due to its high variability. Currently, quantitative PCR (qPCR) serves as the endpoint method to detect the amount of replicated viral genome after infection. In this study, we optimize the TCID50 assay by adapting endpoint detection with droplet digital PCR (ddPCR). We performed TCID50 assays using ATCC AAV-2 reference standard stock material across 18 independent runs. The cell lysate from TCID50 assay was then analyzed using both qPCR and ddPCR endpoint to allow for direct comparison between the two methods. The long-term 1-year side-by-side comparison between qPCR and ddPCR as endpoint measurement demonstrated improved interassay precision when the ddPCR method was utilized. In particular, after the addition of a novel secondary set threshold for infectivity scoring of individual wells, the average infectious titer of 18 runs is 6.45E+08 with % coefficient of variation (CV) of 42.5 and 5.63E+08 with % CV of 34.9 by qPCR and ddPCR, respectively. In this study, we offer improvements of infectious titer assay with (1) higher interassay precision by adapting ddPCR as an endpoint method without the need of standard curve preparation; (2) identification of a second ‘‘set threshold’’ value in infectivity scoring that improves assay precision; and (3) application of statistical analysis to identify the acceptance range of infectious titer values. Taken together,we provide an optimized TCID50 method with improved interassay precision that is important for rAAV infectious titer testing during process development and manufacturing.

Authors: Tam Duong, James McAllister, Khalid Eldahan, Jennifer Wang, Eric Onishi, Kate Shen, Robert Schrock, Bingnan Gu, and Peng Wang

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