Galectin-3 levels are elevated following nintedanib treatment
Bibliographic record
Abstract
Background and Aims: Idiopathic pulmonary fibrosis (IPF) is a common and severe form of pulmonary fibrosis. Nintedanib, a triple angiokinase inhibitor, is approved for treating IPF. Galectin 3 (Gal-3) activates a variety of profibrotic processes. Currently, the Gal-3 inhibitor TD139 is being tested in phase II clinical trials. Since this treatment is given ‘on top’ of nintedanib, it is important to estimate its effect on Gal-3 levels. Therefore, we evaluated the impact of nintedanib on Gal-3 expression using both in vitro and in vivo models, in addition to serum samples from patients with IPF. Methods: Gal-3 levels were evaluated in IPF and control tissue samples, primary human lung fibroblasts (HLFs) following nintedanib treatment (10–100 nM, quantitative polymerase chain reaction), and in a silica-induced fibrosis mouse model with/without nintedanib (0.021–0.21 mg/kg) by immunohistochemistry. In addition, Gal-3 levels were analyzed in serum samples from 41 patients with interstitial lung disease patients with/without nintedanib treatment by ELISA. Results: Nintedanib addition to HLFs resulted in significant elevations in Gal-3, phospho-signal transducer and activator of transcription 3 (pSTAT3), as well as IL-8 mRNA levels ( p < 0.05). Gal-3 expression was higher in samples from IPF patients compared with non-IPF controls at the protein and mRNA levels ( p < 0.05). In the in vivo mouse model, Gal-3 levels were increased following fibrosis induction and even further increased with the addition of nintedanib, mostly in macrophages ( p < 0.05). Patients receiving nintedanib presented with higher Gal-3 serum levels compared with those who did not receive nintedanib ( p < 0.05). Conclusion: Nintedanib elevates Gal-3 levels in both experimental models, along with patient samples. These findings highlight the possibility of using combined inhibition therapy for patients with IPF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".