Identification of integrin a5b1 inhibition as a potentially disease-modifying approach for the treatment of pulmonary arterial hypertension
Bibliographic record
Abstract
Pulmonary arterial hypertension (PAH) is characterized by pathogenic remodeling of the distal arteries arising from pulmonary arterial smooth muscle cell (PASMC) hyperplasia, pulmonary arterial endothelial cell (PAEC) dysfunction, and increased deposition of extracellular matrix (ECM). Given the central role of the ECM in the restructuring of vasculature, we investigated the potential to therapeutically target a5b1, a fibronectin-binding integrin implicated in cell proliferation and angiogenesis, for PAH. Immunohistochemical analysis of lungs from PAH patients revealed increased fibronectin and a5b1 expression in distal pulmonary arteries. To assess the function of a5b1, we have developed potent, orally bioavailable, small molecule inhibitors (SMi) and monoclonal antibodies (mAb) targeting a5b1. Selective inhibition of a5b1 in cultured PASMC modulated multiple pathways involved in cell cycle regulation at a transcriptional and post-transcriptional level and blocked cellular proliferation. Human precision cut lung slices (PCLS) treated with a5b1 inhibitors decreased expression of pathways involved in ECM deposition and decreased levels of smooth muscle cell markers. Finally, a5b1 inhibition with either a SMi or mAb significantly improved cardiac and vascular function in the rat Sugen/hypoxia PAH model by reducing pulmonary arterial wall thickness, right ventricular hypertrophy, and fibrosis. These data highlight a hitherto unappreciated role for a5b1 in the pathogenesis of PAH and support the potential for a5b1 inhibition as a disease-modifying therapeutic strategy for the management of PAH.
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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".