(M1030-02-09) Connecting ADA-SCID Manufacturing to Outcomes: A Case Study in Predictive Analytics for Product Understanding

Purpose: Success in autologous gene therapy manufacturing for ADA-SCID depends on multiple factors, including mobilized CD34+ cell yield and quality, process robustness, formulation strategy, patient conditioning, and clinical outcomes (Kohn et al., 2021). Allogeneic stem cell transplantation remains an option but poses risks such as graft-versus-host disease, with survival rates of 66% (matched unrelated) and 43% (haploidentical). Historical events like the 2020 Strimvelis T-ALL case further emphasize the need for long-term safety monitoring (Ferrua et al., 2019). This case study compares two published autologous workflows: one using a third-generation self-inactivating lentiviral vector (EFS-ADA) and the other using a first-generation gammaretroviral vector (MND-ADA). A feedback-driven analytics approach connects material variability, process parameters, product quality, and clinical outcomes to derive process understanding and support Biologics License Applications.

Methods: Workflow 1, based on lentiviral gene therapy with expansion (Kohn et al., 2021), follows a structured 11-day manufacturing process. Before Day 0, patients discontinue enzyme replacement therapy and receive busulfan conditioning at 90 mg/m², with AUC monitoring by liquid chromatography–mass spectrometry (LC-MS) targeting 60–80 mg·h/L. Bone marrow is harvested on Day 0, and CD34-positive cells are purified on Day 1. From Days 2–4, cells are pre-stimulated for 24–48 hours and transduced with an EFS-ADA lentiviral vector at a multiplicity of infection (MOI) of 50–100, targeting 1–3 vector copies per cell. Following transduction, cells are cultured and expanded over six days (Days 5–10) to reach a final yield of 5–10 × 10⁶ cells/kg. On Day 11, cells are washed, resuspended, optionally cryopreserved, and subjected to lot release assays including ADA activity (≥50 nmol/h/mg by ELISA), vector copy number (1–3 by qPCR), CD34 positivity ( >90%) by flow cytometry), sterility (BacT/ALERT with conditional release), mycoplasma testing (PCR negative), and endotoxin testing ( < 0.5 EU/mL). The final drug product is infused on Day 11. Workflow 2, based on gammaretroviral gene therapy without expansion (Aiuti et al., 2009), follows a condensed 4-day process. Bone marrow harvest and CD34-positive purification occur on Days 0 and 1. Cells are pre-stimulated for 24 hours and transduced on Days 2–3 with the MND-ADA vector, targeting 0.5–2 copies per cell. No expansion is performed. Formulation and lot release testing occur on Day 4, followed by infusion the same day. Diagrams 1 and 2 outline both workflows, highlighting in-process analytics such as transduction efficiency, ADA activity, and vector copy number, as well as clinical monitoring. Operational details are summarized in Table 1, and clinical outcomes, including survival and immune reconstitution, are presented in Table 2. Multi-method analytics and PK/PD data were used to correlate manufacturing quality with clinical outcomes (Masiuk et al., 2024)

Results: Feedback from pharmacokinetic and pharmacodynamic (PK/PD) data reduced manufacturing variability by 10–15% in Workflow 1 by systematically leveraging analytical controls (Gaspar et al., 2011). Tracking incoming material characteristics, such as CD34+/CD38– stemness and myeloid dysplasia burden, alongside in-process metrics (vector copy number, ADA activity) and clinical endpoints (e.g., T-ALL phenotyping) enabled a feedback-driven process understanding. Table 3 summarizes key learnings. Poor CD34+ proliferation and the presence of suppressive CD34– myeloid cells limited stimulation efficiency, impacting final cell yields and dosing. Low transduction efficiency reduced vector copy number and ADA potency. Clinical failures, such as failed engraftment in Workflow 1 and T-ALL development in Workflow 2, highlighted critical vulnerabilities in both manufacturing platforms. These insights underline the importance of robust analytics to manage inherent material variability and ensure long-term clinical success.

Conclusion: A feedback-driven analytics framework enables predictive manufacturing control by systematically connecting incoming material quality, in-process analytics, and post-infusion clinical outcomes. In ADA-SCID gene therapy, understanding the impact of intrinsic bone marrow variability—including proliferation deficits, stemness reduction, and myeloid dysplasia burden—was critical to optimizing transduction efficiency, product quality, and patient outcomes. Feedback loops leveraging PK/PD data reduced batch variability by 10–15% in Workflow 1. These findings demonstrate how analytical rigor throughout manufacturing can support process robustness, regulatory filings, and safer clinical translation in rare disease gene therapies.

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Acknowledgements: The author acknowledges clinical trial investigators and patients contributing data.

Funding: No external funding received. Disclosures: No conflicts of interest.

Table 1: Operational Details of Vein-to-Vein ADA-SCID Manufacturing Workflows

Manufacturing Data for ADA-SCID Autologous Gene Therapy Workflows and Clinical Outcomes

Learnings from Analytical Feedback Thread in ADA-SCID Gene Therapy Workflows