Lysophosphatidic Acid Receptor 1 (LPA<sub>1</sub>) Antagonists as Potential Migrastatics for Triple Negative Breast Cancer
Bibliographic record
Abstract
Abstract Metastasis is responsible for about 90 % of cancer deaths. Anti‐metastatic drugs, termed as migrastatics, offer a distinctive therapeutic approach to address cancer migration and invasion. However, therapeutic exploitation of metastasis‐specific targets remains limited, and the effective prevention and suppression of metastatic cancer continue to be elusive. Lysophosphatidic acid receptor 1 (LPA 1 ) is activated by an endogenous lipid molecule LPA, leading to a diverse array of cellular activities. Previous studies have shown that the LPA/LPA 1 axis supports the progression of metastasis for many types of cancer. In this study, we report the synthesis and biological evaluation of fluorine‐containing triazole derivatives as potent LPA 1 antagonists, offering potential as migrastatic drugs for triple negative breast cancer (TNBC). In particular, compound 12 f , the most potent and highly selective in this series with an IC 50 value of 16.0 nM in the cAMP assay and 18.4 nM in the calcium mobilization assay, inhibited cell survival, migration, and invasion in the TNBC cell line. Interestingly, the compound did not induce apoptosis in TNBC cells and demonstrated no cytotoxic effects. These results highlight the potential of LPA 1 as a migrastatic target. Consequently, the LPA 1 antagonists developed in this study hold promise as potential migrastatic candidates for TNBC.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".