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Multi-omic Attributes and Unbiased Computational Modeling for the Prediction of Immunomodulatory Potency of Mesenchymal Stromal Cells

ABSTRACT: Mesenchymal stromal cells (MSCs) are “living medicines” that continue to be evaluated in clinical trials to treat various clinical indications, yet remain unapproved. Because these cell therapies can be harvested from different tissue sources, are manufactured ex vivo, and are composed of highly responsive cells from donors of varying demographics, significant complexities limit the current understanding and advancements to clinical practice. However, we propose a model workflow used to overcome challenges by identifying multi-omic features that can serve as predictive therapeutic outcomes of MSCs. Here, features were identified using unbiased symbolic regression and machine learning models that correlated multi-omic datasets to results from in vitro functional assays based on putative mechanisms of action of MSCs. Together, this study provides a compelling framework for achieving the identification of candidate CQAs specific to MSCs that may help overcome current challenges, advancing MSCs to broad clinical use. This upload contain the metabolomic datasets, which were correlated with quality metrics, such as potency.

ORGANISM(S): Human Homo Sapiens

TISSUE(S): Stem Cells

SUBMITTER: David Gaul

PROVIDER: ST002052 | MetabolomicsWorkbench | Thu Jan 06 00:00:00 GMT 2022

REPOSITORIES: MetabolomicsWorkbench

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