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Record W2131175249 · doi:10.1517/17460440903186100

Update on models of pulmonary fibrosis therapy for preclinical drug research

2009· article· en· W2131175249 on OpenAlexaff
Sabina A Antoniu, Martin Kolb

Bibliographic record

VenueExpert Opinion on Drug Discovery · 2009
Typearticle
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineIdiopathic pulmonary fibrosisBleomycinPulmonary fibrosisDiseaseDrugIntensive care medicineDrug developmentPreclinical testingFibrosisDrug discoveryLungBioinformaticsPathologyPharmacologyInternal medicineMedical physicsChemotherapy

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.937

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.080
GPT teacher head0.402
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2009
Admission routes1
Has abstractyes

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