Antifibrotic Drug Nintedanib Inhibits CSF1R to Promote IL-4–associated Tissue Repair Macrophages
Bibliographic record
Abstract
Abstract Profibrotic and prohomeostatic macrophage phenotypes remain ill-defined, both in vivo and in vitro, impeding the successful development of drugs that reprogram macrophages as an attractive therapeutic approach to manage fibrotic disease. The goal of this study was to reveal profibrotic and prohomeostatic macrophage phenotypes that could guide the design of new therapeutic approaches targeting macrophages to treat fibrotic disease. This study used nintedanib, a broad kinase inhibitor approved for idiopathic pulmonary fibrosis, to dissect lung macrophage phenotypes during fibrosis-linked inflammation by combining in vivo and in vitro bulk and single-cell RNA-sequencing approaches. In the bleomycin model, nintedanib drove the expression of IL-4/IL-13–associated genes important for tissue regeneration and repair at early and late time points in lung macrophages. These findings were replicated in vitro in mouse primary bone marrow–derived macrophages exposed to IL-4/IL-13 and nintedanib. In addition, nintedanib promoted the expression of IL-4/IL-13 pathway genes in human macrophages in vitro. The molecular mechanism was connected to inhibition of the colony stimulating factor 1 (CSF1) receptor in both human and mouse macrophages. Moreover, nintedanib counterbalanced the effects of TNF on IL-4/IL-13 in macrophages to promote expression of IL-4/IL-13–regulated tissue repair genes in fibrotic contexts in vivo and in vitro. This study demonstrates that one of nintedanib's antifibrotic mechanisms is to increase IL-4 signaling in macrophages through inhibition of the CSF1 receptor, resulting in the promotion of tissue repair phenotypes.
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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".