biostudies-literatureUnknown7(2)Odeh-Couvertier VYLarge-scale, reproducible manufacturing of therapeutic cells with consistently high quality is vital for translation to clinically effective and widely accessible cell therapies. However, the biological and logistical complexity of manufacturing a living product, including challenges associated with their inherent variability and uncertainties of process parameters, currently make it difficult to achieve predictable cell-product quality. Using a degradable microscaffold-based T-cell process, we developed an artificial intelligence (AI)-driven experimental-computational platform to identify a set of critical process parameters and critical quality attributes from heterogeneous, high-dimensional, time-dependent multiomics data, measurable during early stages of manufacturing and predictive oBioengineering & translational medicinee10282https://www.ebi.ac.uk/biostudies/studies/S-EPMC9115702biostudies-literaturePredicting T-cell quality during manufacturing through an artificial intelligence-based integrative multiomics analytical platform.PMC9115702Roy KColonna MBTorres-Garcia WOdeh-Couvertier VYLevine BLDwarshuis NJEdison ASKotanchek TfalsePredicting T-cell quality during manufacturing through an artificial intelligence-based integrative multiomics analytical platform.Large-scale, reproducible manufacturing of therapeutic cells with consistently high quality is vital for translation to clinically effective and widely accessible cell therapies. However, the biological and logistical complexity of manufacturing a living product, including challenges associated with their inherent variability and uncertainties of process parameters, currently make it difficult to achieve predictable cell-product quality. Using a degradable microscaffold-based T-cell process, we developed an artificial intelligence (AI)-driven experimental-computational platform to identify a set of critical process parameters and critical quality attributes from heterogeneous, high-dimensional, time-dependent multiomics data, measurable during early stages of manufacturing and predictive o2022-01-01T00:00:00Z2022 May2025-04-18T13:16:37.853Z2024-11-06T08:32:28.89ZS-EPMC91157023560066010.1002/btm2.10282