Digital batch records and AI process control to halve cell therapy batch failures

Autologous manufacturing has out-of-specification and failure rates far above those of conventional biologics, driven by variable starting material and largely open-loop processes. Inline sensors (metabolites, cell counts, imaging, cytokines), electronic batch records and machine-learning models predicting final yield and potency from early process data allow adaptive feeding, harvest timing and early re-manufacture decisions. The proposal is a shared, anonymised dataset of manufacturing runs across manufacturers and an open model benchmark, with regulatory guidance on adaptive control within a validated design space.