Introduction:
The article discusses the use of mechanistic modeling for rAAV enrichment in recombinant adeno-associated virus (rAAV)-based therapies. The authors explain how mechanistic modeling can enhance the development of rAAV processes and predict recovery yield and optimal process conditions. They also discuss the advantages of mechanistic modeling over empirical models and provide recommendations for using this approach.
- The optimal strategy for rAAV enrichment involves two steps: an isocratic elution of empty capsids followed by isocratic elution of full capsids.
- Purity and yield are affected by the pH of the first step, with a pH of 9.0 being optimal.
- Higher purity requirements decrease yield.
- Shorter elution times reduce yields but improve productivity, buffer consumption, and pool concentration.
- Load challenge affects selectivity, and there is a strong correlation between salt concentration and wash length in the first step and pool yield and purity in the second step.
Conclusion:
Mechanistic modeling for rAAV enrichment can effectively predict recovery yield and optimize process conditions. It offers advantages over empirical models and can support process characterization for establishing a commercial process control strategy. However, researchers should carefully consider the underlying assumptions of the model and the quality of the data inputs to ensure accurate results. Additionally, off-the-shelf tools and technologies are available to support modeling efforts in this field.
