This article examines AI applications to improve manufacturing efficiency in cell and gene therapy, suggesting impactful strategies.
Cell and gene therapy manufacturing continues to face efficiency and scalability challenges, yet the sector can draw directly on the biopharmaceutical industry’s established experience applying AI and machine learning across biologics production, spanning digital infrastructure, process analytical technology (PAT), cloud-based digital applications, and generative AI. This article examines how those lessons translate to CGT, arguing that manufacturers should prioritize electronic batch records, contextualized metadata, and cloud-based data pipelines before layering on advanced AI, while planning early for GMP validation of AI-driven tools.
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Shyam Panjwani (2026) studied this question.
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