{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["7(2)"],"submitter":["Odeh-Couvertier VY"],"pubmed_abstract":["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 o"],"journal":["Bioengineering & translational medicine"],"pagination":["e10282"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9115702"],"repository":["biostudies-literature"],"pubmed_title":["Predicting T-cell quality during manufacturing through an artificial intelligence-based integrative multiomics analytical platform."],"pmcid":["PMC9115702"],"pubmed_authors":["Roy K","Colonna MB","Torres-Garcia W","Odeh-Couvertier VY","Levine BL","Dwarshuis NJ","Edison AS","Kotanchek T"],"additional_accession":[]},"is_claimable":false,"name":"Predicting T-cell quality during manufacturing through an artificial intelligence-based integrative multiomics analytical platform.","description":"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 o","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 May","modification":"2025-04-18T13:16:37.853Z","creation":"2024-11-06T08:32:28.89Z"},"accession":"S-EPMC9115702","cross_references":{"pubmed":["35600660"],"doi":["10.1002/btm2.10282"]}}