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.
Nascent advanced therapies, including regenerative medicine and cell and gene therapies, rely on the production of cells in bioreactors that are highly heterogeneous in both space and time. Unfortunately, advanced therapies have failed to reach a wide patient population due to unreliable manufacturing processes that result in batch variability and cost prohibitive production. This can be attributed largely to a void in existing process analytical technologies (PATs) capable of characterizing the secreted critical quality attribute (CQA) biomolecules that correlate with the final product quality. The Dynamic Sampling Platform (DSP) is a PAT for cell bioreactor monitoring that can be coupled to a suite of sensor techniques to provide real‐time feedback on spatial and temporal CQA content in situ. In this study, DSP is coupled with electrospray ionization mass spectrometry and direct‐from‐culture sampling to obtain measures of CQA content in bulk media and the cell microenvironment throughout the entire cell culture process (≈3 weeks). Post hoc analysis of this real‐time data reveals that sampling from the microenvironment enables cell state monitoring (e.g., confluence, differentiation). These results demonstrate that an effective PAT should incorporate both spatial and temporal resolution to serve as an effective input for feedback control in biomanufacturing.
more » « less- Award ID(s):
- 1648035
- NSF-PAR ID:
- 10453150
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Biotechnology Journal
- Volume:
- 16
- Issue:
- 3
- ISSN:
- 1860-6768
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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