Targeted delivery of galunisertib using machine perfusion reduces fibrogenesis in an integrated <i>ex vivo</i> renal transplant and fibrogenesis model
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Fibrosis in kidney allografts is a major post-transplant complication that contributes to graft failure. Lately, multiple potent inhibitors of fibrosis-related pathways have been developed such as galunisertib, an inhibitor of the transforming growth factor-beta (TGF-β/TGFβ1) signalling pathway. This drug, however, poses risks for adverse effects when administered systemically. Therefore, we devised a new repurposing strategy in which galunisertib is administered ex vivo. We combined machine perfusion and tissue slices to explore the antifibrotic effects of galunisertib in renal grafts. EXPERIMENTAL APPROACH: Porcine kidneys were subjected to 30 min of warm ischaemia, 24 h of oxygenated hypothermic machine perfusion and 6 h of normothermic machine perfusion with various treatments (i.e. untreated control, TGFβ1, galunisertib or TGFβ1 + galunisertib; n = 8 kidneys per group). To determine whether effects persisted upon ceasing treatment, kidney slices were prepared from respective kidneys and incubated for 48 h. KEY RESULTS: Galunisertib treatment improved general viability without negatively affecting renal function or elevating levels of injury markers or by-products of oxidative stress during perfusion. Galunisertib also reduced inflammation and, more importantly, reduced the onset of fibrosis after 48 h of incubation. CONCLUSIONS AND IMPLICATIONS: Our findings demonstrate the value of using machine perfusion for administering antifibrotic drugs such as galunisertib, proving it to be an effective example of repurposing.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".