Summary of Study ST001476
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001000. The data can be accessed directly via it's Project DOI: 10.21228/M8F982 This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
| Study ID | ST001476 |
| Study Title | Design of Experiments for Maximizing T cell endpoints |
| Study Summary | Purified T cells from a single healthy donor were expanded in vitro with either magnetic beads or degradable micro-scaffold (DMS) particles. Magnetic bead cultures were expanded according to manufacturer’s instructions, while DMS cultures were expanded with varying DMS particle concentration, IL2 concentration, and antigen surface density on the particles, according to a design of experiments. Media of each culture of was sampled repeatedly over the course of a 14 day period. Quantities and characteristics of activated T cells were assessed at the end of the culture period, and media was analyzed by 1H-NMR. Analysis of spectra resulted in a set of 20 features that was used in predictive modeling of T cell endpoints, along with culture parameters described above and cytokine data. A second validation experiment was performed with different values for culture parameters, using the same culture, sampling, and NMR analysis methods. |
| Institute | University of Georgia |
| Department | Complex Carbohydrate Research Center |
| Laboratory | Edison |
| Last Name | Colonna |
| First Name | Max |
| Address | 315 Riverbend Rd |
| maxwellbaca@uga.edu | |
| Phone | 7065420257 |
| Submit Date | 2020-08-12 |
| Num Groups | 2 |
| Total Subjects | 1 |
| Raw Data Available | Yes |
| Analysis Type Detail | NMR |
| Release Date | 2020-09-10 |
| Release Version | 1 |
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Project:
| Project ID: | PR001000 |
| Project DOI: | doi: 10.21228/M8F982 |
| Project Title: | Predicting T Cell Quality During Manufacturing Through an AI-based Integrative Multi-omics Analytical Platform |
| Project Summary: | Large-scale, reproducible manufacturing of therapeutic cells with consistent high quality is vital for translation to clinically effective and widely accessible cell-therapies for patients. However, the biological and logistical complexity of manufacturing a living product, including challenges associated with their inherent variability as well as uncertainties of process parameters, currently make it difficult to achieve predictable cell-product quality. Using a degradable microscaffold-based T cell manufacturing process as an example, we developed an Artificial Intelligence (AI)-driven experimental-computational platform to identify a multivariate set of critical process parameters (CPPs) and critical quality attributes (CQAs) from heterogeneous, high dimensional, time-dependent multi-omics data, measurable during early stages of manufacturing and that are predictive of end-of-manufacturing product quality. Sequential, Design-of-Experiment (DOE)-based studies, coupled with a set of agnostic machine-learning framework, was used to extract multiple feature combinations from Day 4 to 6 media assessment, that were highly predictive (R2>90%) of the end-product phenotypes, specifically the total live CD4+ and CD8+ naïve and central memory T cells (CD63L+CCR7+ cells), and the CD4+/CD8+ T cell ratio. This generalizable workflow and computational platform could be broadly applied to any cell-therapy manufacturing process to identify multivariate early CQAs and CPPs that are predictable of final product quality. |
| Institute: | University of Georgia;Georgia Institute of Technology;University of Puerto Rico Mayaguez |
| Last Name: | Colonna |
| First Name: | Maxwell |
| Address: | 315 Riverbend Rd, Athens, Georgia, 30602, USA |
| Email: | maxwellbaca@uga.edu |
| Phone: | 7065420257 |
| Funding Source: | NSF EEC-1648035 |
| Publications: | Predicting T Cell Quality During Manufacturing Through an AI-based Integrative Multi-omics Analytical Platform |
| Contributors: | Valerie Odeh-Couvertier, Nathan J. Dwarshuis, Maxwell B. Colonna, Bruce L. Levine, Arthur S. Edison, Theresa Kotanchek, Krishnendu Roy, and Wandaliz Torres-Garcia |
