Optimization of AAV Production using Statistical Design of Experiment

Abstract

Applications of adeno-associated virus (AAV) in gene therapy has increased significantly over the past two decades due to its remarkable safety profiles and efficiency of gene delivery into various tissues. Demand for the supply of recombinant AAV vectors has also increased to fulfill pre-clinical and clinical needs. Low titer and process scalability remain two key issues in AAV manufacturing. Productivity of AAV vectors is often affected by the AAV variant, cell culture type, the size of Gene of Interest (GOI), cell density, transfection method, plasmid DNA concentration/ratio and other factors. Based on risk assessment of factors that may impact AAV production, we have evaluated 6 process parameters using a 24-run Space Filling Design generated by the JMP Statistical Software. Space-Filling Designs (SFDs) are a class of experimental designs created with the objective of covering the entire design space as completely as possible; this in turn allows more accurate modelling of complex response surface behavior typically found in bioprocesses. SFDs are very flexible in structure, do not require any prior assumptions about response surface shape and typically require fewer runs for larger numbers of factors than the commonly used response surface designs. The commonly used classical and optimal response surface designs primarily distribute the design points near the boundary of the experimental region such that little or no data is collected in the interior. As a result, the experimenters have no direct knowledge of the kinetic behavior inside the design region. Without data describing the interior kinetic behavior it is not possible to build accurate response surface models. In this study, we will describe the value of SFDs and how to establish critical process parameters (CPP) and operating ranges for optimum and robust AAV production in suspended HEK293 cells in Ambr® 15 Microbioreactor System.

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