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Abstract: PUB049

Expanded Protein and Metabolic Secretome Characterization of Renal Cell Therapy Candidate Rilparencel

Session Information

Category: Artificial Intelligence, Digital Health, and Data Science

  • 300 Artificial Intelligence, Digital Health, and Data Science

Authors

  • Rohlfing, Mark A., ProKidney LLC, Morrisville, North Carolina, United States
  • James, Katherine, ProKidney LLC, Morrisville, North Carolina, United States
  • Bruce, Andrew T., ProKidney LLC, Morrisville, North Carolina, United States
  • Justewicz, Dominic Mark, ProKidney LLC, Morrisville, North Carolina, United States
  • Greenawalt, Ashley, ProKidney LLC, Morrisville, North Carolina, United States
Background

Rilparencel is an autologous cell therapy currently in Phase 3 clinical trials for the treatment of CKD patients with T2D. Results of Phase 2 clinical trials suggest that rilparencel treatment promotes stabilization of eGFR. Derived from a cortical kidney biopsy, rilparencel is comprised primarily of renal epithelial cells. Following a manufacturing process, rilparencel is administered via injection into the kidney cortex. Previous studies suggest a potential kidney repair mechanism via paracrine signaling, including reduced pro-inflammatory activity and increased angiogenic activity. We sought to further characterize how the rilparencel secretome may facilitate kidney repair in an expanded cohort of clinical samples.

Methods

Rilparencel product was manufactured from 17 patients enrolled in a Phase 2 investigative clinical trial of T2D with stage 3a-4 CKD (NCT02086574) or donated kidneys (n=1, metabolome; n=4, secretome; National Disease Research Interchange). Cells collected during the manufacturing process (two samples for n=17 lots, and five samples for n=6 subset) were incubated for 24 hours, and conditioned media (CM) was collected. CM was analyzed for metabolites (two samples for n=17 lots) via untargeted metabolomics (LC-MS). Approximately 80 secreted proteins (five samples for n=6 subset) were analyzed (Luminex xMAP, Intelliflex). Analysis was performed utilizing Prism and R Studio. Pathway analysis was performed by Panther Go Enrichment Analysis.

Results

Overall, 2,215 metabolites were measured, and 986 were confidently identified. Principal Component Analysis demonstrated clear clustering of samples by time point for both secreted metabolites (986) and proteins (approximately 80). These data combined demonstrate similar trends in up- or down-regulation of certain pathways, including angiogenesis, inflammation, remodeling, differentiation, and other tissue repair pathways.

Conclusion

Secretome characterization during rilparencel manufacturing exhibits a tissue remodeling and/or wound healing profile. Key secretome markers and metabolites may be useful in further enriching product characterization.

Funding

  • Commercial Support – ProKidney, LLC

Digital Object Identifier (DOI)