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Short Report| Volume 24, ISSUE 6, P590-596, June 2022

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Design of experiments as a decision tool for cell therapy manufacturing

  • Author Footnotes
    # These authors contributed equally to this work.
    Esmond Lee
    Correspondence
    Correspondence: Esmond Lee, Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA.
    Footnotes
    # These authors contributed equally to this work.
    Affiliations
    Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
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  • Author Footnotes
    # These authors contributed equally to this work.
    Devin Shah
    Footnotes
    # These authors contributed equally to this work.
    Affiliations
    Duke University, Durham, NC, 27708, USA
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  • Matthew Porteus
    Affiliations
    Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA

    Division of Hematology, Oncology, Stem Cell Transplantation and Regenerative Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA

    Center for Definitive and Curative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
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  • J. Fraser Wright
    Affiliations
    Division of Hematology, Oncology, Stem Cell Transplantation and Regenerative Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA

    Center for Definitive and Curative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
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  • Rosa Bacchetta
    Correspondence
    Correspondence: Rosa Bacchetta, MD, Division of Hematology, Oncology, Stem Cell Transplantation and Regenerative Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA.
    Affiliations
    Division of Hematology, Oncology, Stem Cell Transplantation and Regenerative Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA

    Center for Definitive and Curative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
    Search for articles by this author
  • Author Footnotes
    # These authors contributed equally to this work.
Published:February 26, 2022DOI:https://doi.org/10.1016/j.jcyt.2022.01.009

      Abstract

      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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