ArticleBioengineering & translational medicine2022
Predicting T-cell quality during manufacturing through an artificial intelligence-based integrative multiomics analytical platform.
Article in Bioengineering & translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Harnessing the NKG2D immune surveillance pathway using engineered T-cells for the treatment of human disease.Human vaccines & immunotherapeutics · 2026Review
- TiOBioprocess and biosystems engineering · 2026Article
- Combining Small-Molecular Compounds With CAR T-Cell Therapy: Novel Strategies for Enhanced Cancer Immunotherapy.Cancer innovation · 2026Review
- Are we there yet? The road to faster and more efficient CAR T cell manufacturing.Journal of biological engineering · 2026Review
- Process Systems Engineering in Precision Medicine: Opportunities in Autologous CAR-T Therapy.Engineering in life sciences · 2026Review
- Plying potency assays for immunotherapy of solid tumors.Frontiers in immunology · 2026Review
- Harnessing the Power of CAR-NK Cells for Solid Tumors: Challenges, Innovations, and Future Frontiers in Immunotherapy.Cancer communications (London, England) · 2026Review
- Advances in Cell-Mediated Drug Delivery for Dermatologic Diseases: Mechanisms and Current Applications.Pharmaceutics · 2025Review
- Nanoparticle-based strategy in CAR-T cell immunotherapy: challenges, implications, and perspectives.Molecular cancer · 2025Review
- Differential Phase Contrast Imaging to Predict MSC Immune Function.Advanced healthcare materials · 2025Article
- Redefining quality in cell and gene therapies: Lessons from implementing electronic QMS in academic cGMP facility.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Review
- Advancing CAR T-cell Therapies with Artificial Intelligence: Opportunities and Challenges.Blood cancer discovery · 2025Review
- Bio-functional hydrogel coated membranes to decrease T-cell exhaustion in manufacturing of CAR T-cells.Frontiers in immunology · 2025Article
- A high-density microfluidic bioreactor for the automated manufacturing of CAR T cells.Nature biomedical engineering · 2024Article
- Mechanism of action, potency and efficacy: considerations for cell therapies.Journal of translational medicine · 2024Review
- Robotics-Driven Manufacturing of Cartilaginous Microtissues for Skeletal Tissue Engineering Applications.Stem cells translational medicine · 2024Article
- ReCARving the future: bridging CAR T-cell therapy gaps with synthetic biology, engineering, and economic insights.Frontiers in immunology · 2024Review
- Virtual differential phase-contrast and dark-field imaging of x-ray absorption images via deep learning.Bioengineering & translational medicine · 2023Article
- Development of a Robust Consensus Modeling Approach for Identifying Cellular and Media Metabolites Predictive of Mesenchymal Stromal Cell Potency.Stem cells (Dayton, Ohio) · 2023Article
- Label-free in vitro assays predict the potency of anti-disialoganglioside chimeric antigen receptor T-cell products.Cytotherapy · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.