(667d) Mathematical Modeling of Gene Therapy Manufacturing

Gene therapy has emerged as aa revolutionary treatment to genetic human diseases through the insertion of genetic materials to replace or supplement for genes that do not function adequately¹. Through better understanding of immunogenicity and integration patterns of viral vectors, gene therapy products have attained clinical success which enabled their commercialization on a global scale. In the US alone, there are over 900 applications for clinical studies in this field². Among the viral vectors used in cell and gene therapies, recombinant adeno-associated viruses (rAAVs) are one of the most commonly used platforms due to their low immunogenicity and long-term gene expression after transduction^3,4. However, current manufacturing capacity of rAAV vectors is still inefficient to keep up with increasing demands at both the clinical and commercial stages⁵. As acquiring quantitative knowledge is integral to process design and control in manufacturing⁶, here we will leverage mathematical modeling to elucidate mechanism within the viral production pipeline with the goals of identifying and overcoming bottlenecks that hinder developments.

This presentation reviews the state of the art in mathematical models applicable to rAAV viral vector manufacturing, examines the kinetic parameters in the viral production process from the introduction of exogenous DNA to the cascade of biochemical reactions and cellular transport mechanisms that result in formed capsids, and discusses their contributions to the current knowledge of the overall process dynamics. Then a mathematical model is proposed that builds on the foundation made by published models which model components of the overall viral production process^7–9 while extending the model to include knowledge of the biological mechanisms of rAAV production. This model is used to understand the dynamics from the initial feed materials to the final product of the bioreactor, and used to relate kinetic parameters to experimental data. Finally, we demonstrate how the mathematical modeling of gene therapy manufacturing enables optimization of the bioreactor operations in a systematic manner.

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(2) FDA Continues Strong Support of Innovation in Development of Gene Therapy Products. U.S. Food and Drug Administration. 2020.

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(8) Varga, C. M.; Tedford, N. C.; Thomas, M.; Klibanov, A. M.; Griffith, L. G.; Lauffenburger, D. A. Quantitative Comparison of Polyethylenimine Formulations and Adenoviral Vectors in Terms of Intracellular Gene Delivery Processes. Gene Ther. 2005, 12, 1023–1032.

(9) Zhou, J.; Yockman, J. W.; Kim, S. W.; Kern, S. E. Intracellular Kinetics of Non-Viral Gene Delivery Using Polyethylenimine Carriers. Pharm. Res. 2007, 24 (6), 1079–1087.