AI and digital transformation in cell and gene therapy: enabling scalable and predictable biomanufacturing

Cell and gene therapy (CGT) has demonstrated transformative clinical potential; however, challenges in scalability, cost, and process consistency continue to limit widespread adoption. AI and digital transformation are emerging as critical enablers to address these bottlenecks. From predictive vector design to smart manufacturing and real-time quality control, AI-driven approaches are reshaping the CGT lifecycle. This article explores how integrated digital ecosystems, advanced analytics, and automation accelerate CGT industrialization while highlighting key implementation challenges and regulatory considerations.

What you will learn

01

How AI and ML are being applied across the entire CGT value chain, from target discovery and vector design through manufacturing and clinical development

02

Why digital twins, real-time process monitoring, and predictive process control are central to shifting CGT manufacturing from reactive to proactive quality assurance

03

How AI-driven antibody and binder design, generative protein modeling, and target selection algorithms are accelerating CAR-T construct optimization

04

How unified data ecosystems and AI-enabled quality systems support interoperability with CDMOs and reduce documentation and batch-review burden

05

What data availability, cost, regulatory harmonization, and skills-gap challenges continue to limit large-scale AI adoption in CGT

06

Why autonomous 'lights-out' manufacturing, real-time release testing, and closed-loop digital twins represent the long-term trajectory for CGT industrialization

Key interests

AI in cell & gene therapy Digital twins CAR-T construct design Predictive process control Multi-omics integration Generative protein design Manufacturing digitalization Real-time release testing Regulatory readiness

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