A Purpose-Synthesised Anti-Fibrotic Agent Attenuates Experimental Kidney Diseases in the Rat
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Locally-active growth factors have been implicated in the pathogenesis of many diseases in which organ fibrosis is a characteristic feature. In the setting of chronic kidney disease (CKD), two such pro-fibrotic factors, transforming growth factor-β (TGF-β) and platelet-derived growth factor (PDGF) have emerged as lead potential targets for intervention. Given the incomplete organ protection afforded by blocking the actions of TGF-β or PDGF individually, we sought to determine whether an agent that inhibited the actions of both may have broader effects in ameliorating the key structural and functional abnormalities of CKD. EXPERIMENTAL APPROACH: Accordingly, we studied the effects of a recently described, small molecule anti-fibrotic drug, 3-methoxy-4-propargyloxycinnamoyl anthranilate (FT011, Fibrotech Therapeutics, Australia), which should have these effects. KEY RESULTS: In the in vitro setting, FT011 inhibited both TGF-β1 and PDGF-BB induced collagen production as well as PDGF-BB-mediated mesangial proliferation. Consistent with these in vitro actions, when studied in a robust model of non-diabetic kidney disease, the 5/6 nephrectomised rat, FT011 attenuated the decline in GFR, proteinuria and glomerulosclerosis (p<0.05 for all). Similarly, in the streptozotocin-diabetic Ren-2 rat, a model of advanced diabetic nephropathy, FT011 reduced albuminuria, glomerulosclerosis and tubulointerstitial fibrosis. CONCLUSIONS AND IMPLICATIONS: Together these studies suggest that broadly antagonising growth factor actions, including those of TGF-β1 and PDGF-BB, has the potential to protect the kidney from progressive injury in both the diabetic and non-diabetic settings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".