Update on models of pulmonary fibrosis therapy for preclinical drug research
Bibliographic record
Abstract
BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a disease with high morbidity and mortality for which current medications are not effective. Therefore, identification of potential therapies is of paramount importance. The preclinical evaluation of novel compounds in animal models represents a critical step in drug development. OBJECTIVE: To describe features and limitations of common animal models of pulmonary fibrosis and discuss relevant preclinical and clinical data on novel potential IPF therapies. METHODS: Review of the existing literature on such models with a special focus on the bleomycin model and its usefulness for the IPF preclinical drug testing. CONCLUSIONS: The model of bleomycin-induced pulmonary fibrosis has the advantages of being well established, reproducible and both time- and cost-efficient. However, it has major limitations as it only mimics some features of human IPF. Most importantly, it is initiated by acute lung injury and is at least partially reversible, which is strikingly different from IPF. The failure in establishing effective IPF therapies despite strong efforts in the last decade is partly attributable to our uncritical trust in the models of lung fibrosis and the false belief that they truly reflect what is going on in human disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".