Design of experiments as a decision tool for cell therapy manufacturing

基因组编辑 遗传增强 生物过程 Cas9 计算生物学 感染的多重性 细胞疗法 免疫系统 生物 清脆的 计算机科学 免疫学 细胞 基因 病毒 遗传学 古生物学
作者
Esmond Lee,Devin Shah,Matthew H. Porteus,J. Fraser Wright,Rosa Bacchetta
出处
期刊:Cytotherapy [Elsevier]
卷期号:24 (6): 590-596 被引量:6
标识
DOI:10.1016/j.jcyt.2022.01.009
摘要

Background aims Cell therapies are costlier to manufacture than small molecules and protein therapeutics because they require multiple manipulations and are often produced in an autologous manner. Strategies to lower the cost of goods to produce a cell therapy could make a significant impact on its total cost. Methods Borrowing from the field of bioprocess development, the authors took a design of experiments (DoE)-based approach to understanding the manufacture of a cell therapy product in pre-clinical development, analyzing main cost factors in the production process. The cells used for these studies were autologous CD4+ T lymphocytes gene-edited using CRISPR/Cas9 and recombinant adeno-associated virus (AAV) to restore normal FOXP3 gene expression as a prospective investigational product for patients with immune dysregulation, polyendocrinopathy, enteropathy, X-linked (IPEX) syndrome. Results Using gene editing efficiency as the response variable, an initial screen was conducted for other variables that could influence the editing frequency. The multiplicity of infection (MOI) of AAV and amount of single guide RNA (sgRNA) were the significant factors used for the optimization step to generate a response contour plot. Cost analysis was done for multiple points in the design space to find cost drivers that could be reduced. For the range of values tested (50 000–750 000 vg/cell AAV and 0.8–4 μg sgRNA), editing with the highest MOI and sgRNA yielded the best gene editing frequency. However, cost analysis showed the optimal solution was gene editing at 193 000 vg/cell AAV and 1.78 μg sgRNA. Conclusions The authors used DoE to define key factors affecting the gene editing process for a potential investigational therapeutic, providing a novel and faster data-based approach to understanding factors driving complex biological processes. This approach could be applied in process development and aid in achieving more robust strategies for the manufacture of cellular therapeutics.
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