Development of an In Vivo Model of Idiopathic Pulmonary Fibrosis Using Human Lung Xenografts Implanted on the Chorioallantoic Membrane of Chick Embryos
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis (IPF) is a chronic and slowly progressive disease characterized by lung tissue stiffening. This disease leads organ failure and death, with a median survival of 3 to 5 years following diagnosis. Around 3 million people are affected worldwide, and the incidence and prevalence of this disease seems to be increasing over the years. There is currently no curative pharmacological treatment for IPF. Furthermore, current preclinical models fail to replicate the human pathophysiology and thus are poor predictors of the success or failure of clinical trials for drugs in development for this disease. There is therefore an urgent need to develop new animal models in order to improve the reliability of preclinical studies and thus facilitate the development of new drugs for IPF. In this regard, we have developed a novel in vivo model using tissue from IPF patients who have undergone lung transplantation, as well as cells derived from these tissues. To do so, lung fragments or IPF fibroblast suspensions were implanted on the chorioallantoic membrane (CAM) of chick embryos. These xenografts were cultivated for 7 days on the CAM, with or without antifibrotic treatments. Our results showed that these xenografts are viable and proliferate on the CAM, and that the tissue xenografts retain their IPF phenotype. For their part, the implanted fibroblasts appear to mimic fibroblastic foci. Daily topical treatments with nintedanib, a molecule currently approved for the treatment of IPF, led to a significant decrease in volume, expression of fibrosis-associated genes (ACTA2, COL1A1 and CTGF), and collagen levels in xenografts. Significant modulations were also observed with PBI-4050, a drug candidate in development for IPF, as well as with compounds that target the immune microenvironment, GLPG1205 and fenofibric acid. Altogether, these results demonstrate the versatility of the CAM-IPF model to test the efficacy of different therapeutic strategies relevant to IPF. Furthermore, they suggest that this novel model has the potential to become a valuable translational tool to determine the potential of drug candidates in development for IPF.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".