Predicting T-cell quality during manufacturing through an artificial intelligence-based integrative multiomics analytical platform.
Bioeng Transl Med
; 7(2): e10282, 2022 May.
Article
em En
| MEDLINE
| ID: mdl-35600660
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 of end-of-manufacturing product quality. Sequential, design-of-experiment-based studies, coupled with an agnostic machine-learning framework, were used to extract feature combinations from early in-culture media assessment that were highly predictive of the end-product CD4/CD8 ratio and total live CD4+ and CD8+ naïve and central memory T cells (CD63L+CCR7+). Our results demonstrate a broadly applicable platform tool to predict end-product quality and composition from early time point in-process measurements during therapeutic cell manufacturing.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Bioeng Transl Med
Ano de publicação:
2022
Tipo de documento:
Article