Predictive analysis of cardiac microtissue manufacturing by monitoring metabolic CQAs

Jun 30, 2022, 1:30 PM
20m
Room: S3 B

Room: S3 B

Speaker

Palecek, Sean (University of Wisconsin - Madison)

Description

Biomanufacturing cells and tissues from human pluripotent stem cells (hPSCs) typically strives to guide differentiation through developmentally relevant pathways in a well-defined, dynamic bioreactor environment. While great strides have been made in differentiating hPSCs to many somatic cell types, robust biomanufacturing remains a roadblock to clinical progress of hPSC-derived cell and tissue therapies. In particular, scaling manufacturing to meet clinical needs, reducing cost, improving cell phenotypes, and improving process robustness are critical challenges. hPSC-derived cardiomyocytes have tremendous potential to restore cardiac function to heart failure patients. However, these cells suffer from poor survival and functional integration in preclinical models of heart disease. We have developed protocols to differentiate hPSCs to endothelial cells and cardiac fibroblasts, and demonstrated that inclusion of these cells during cardiomyocyte biomanufacturing accelerates acquisition of maturation phenotypes such as morphology, sarcomere protein expression, and calcium handling in the cardiomyocytes. Importantly, these heterotypic cell interactions must be provided to cardiac progenitor cells, allowing the cell types to co-differentiate. To reduce costs and improve scale of cardiomyocyte biomanufacturing, we have transitioned 2D cardiomyocyte differentiation to 3D, reducing cost by approximately 85% and permitting manufacturing of greater than one trillion cardiomyocytes in a 300 mL spinner flask bioreactor. To improve biomanufacturing process robustness, we have performed a multi-omic characterization of differentiating cardiomyocytes and utilized unbiased data analytics to identify genes, proteins, and metabolites that when measured before day 5 predict successful vs. failed batches at day 15, determined by the percentage of cells expressing cardiac troponin T. We envision that these multivariate predictive critical quality attributes can be used to more quickly identify failed batches and eventually lead to closed-loop control strategies to improve biomanufacturing process robustness.

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