Late Clinical-Stage Candidate Rilparencel's Effect on Kidney Function and Biological Pathways in a Type 2 Diabetes and CKD Patient Subset
Bibliographic record
Abstract
Background: Kidney cortical biopsy-derived rilparencel is a 1st-in-class autologous kidney epithelial cell platform being evaluated in late-stage clinical trials for patients with type 2 diabetes (T2D) & chronic kidney disease (CKD). Data from 2 Phase 2 studies suggest that kidney cortical injection of rilparencel may preserve estimated glomerular filtration rate (eGFR). In a proof-of-concept, limited retrospective cohort study we sought to evaluate biological pathways associated with rilparencel’s effect on kidney function. Methods: Banked rilparencel samples from 5 patients with T2D&CKD (NCT02836574) were analyzed with scRNAseq. Kidney cell types were identified with unsupervised clustering & projection on to the KPMP atlas. For each rilparencel cell type, cell-level & pseudo-bulk differentially expressed genes (DEGs) were identified by Wilcoxon Rank Sum test & DESeq2, respectively, based on each patient’s response to rilparencel, i.e. eGFR slope computed across 12 mo post-treatment. Biological pathways were identified with GO. Results: KPMP-anchored molecularly profiled kidney cell types in rilparencel include glomerular parietal epithelial cells (PECs), adaptive proximal tubules (PT), ascending thin limb (ATL) & adaptive thick ascending limb-2 (TAL-2), distal convoluted tubule, connecting tubule & intermediate collecting ducts (DCT/CNT/IMCD). For each cell type, DEGs correlating with renal function were identified. Increased epithelial cell differentiation (PEC), negative regulation of inflammatory response genes (PT), increased expression of cell migration genes within Loop of Henle limbs & decreased cell adhesion (DCT/CNT/IMCD) appear to exhibit improved eGFR slopes. Conclusion: In a limited retrospective cohort of T2D&CKD patients reparative & restorative pathways can be detected with rilparencel’s effect on kidney function. Our approach might serve as a roadmap for unveiling the mechanism of action of cell-based therapies. Funding: Commercial Support - ProKidney
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".