Lysophosphatidic acid receptor 1 inhibition: a potential treatment target for pulmonary fibrosis
Bibliographic record
Abstract
Lysophosphatidic acid (LPA)-mediated activation of LPA receptor 1 (LPAR1) contributes to the pathophysiology of fibrotic diseases such as idiopathic pulmonary fibrosis (IPF) and systemic sclerosis (SSc). These diseases are associated with high morbidity and mortality despite current treatment options. The LPA-producing enzyme autotaxin (ATX) and LPAR1 activation contribute to inflammation and mechanisms underlying fibrosis in preclinical fibrotic models. Additionally, elevated levels of LPA have been detected in bronchoalveolar lavage fluid from patients with IPF and in serum from patients with SSc. Thus, ATX and LPAR1 have gained considerable interest as pharmaceutical targets to combat fibrotic disease and inhibitors of these targets have been investigated in clinical trials for IPF and SSc. The goals of this review are to summarise the current literature on ATX and LPAR1 signalling in pulmonary fibrosis and to help differentiate the novel inhibitors in development. The mechanisms of action of ATX and LPAR1 inhibitors are described and preclinical studies and clinical trials of these agents are outlined. Because of their contribution to numerous physiologic events underlying fibrotic disease, ATX and LPAR1 inhibition presents a promising therapeutic strategy for IPF, SSc and other fibrotic diseases that may fulfil unmet needs of the current standard of care.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".