Saracatinib is an efficacious clinical candidate for fibrodysplasia ossificans progressiva
Bibliographic record
Abstract
Currently, no effective therapies exist for fibrodysplasia ossificans progressiva (FOP), a rare congenital syndrome in which heterotopic bone is formed in soft tissues owing to dysregulated activity of the bone morphogenetic protein (BMP) receptor kinase ALK2 (also known as ACVR1). From a screen of known biologically active compounds, we identified saracatinib as a potent ALK2 kinase inhibitor. In enzymatic and cell-based assays, saracatinib preferentially inhibited ALK2, compared with other receptors of the BMP/TGF-β signaling pathway, and induced dorsalization in zebrafish embryos consistent with BMP antagonism. We further tested the efficacy of saracatinib using an inducible ACVR1Q207D-transgenic mouse line, which provides a model of heterotopic ossification (HO), as well as an inducible ACVR1R206H-knockin mouse, which serves as a genetically and physiologically faithful FOP model. In both models, saracatinib was well tolerated and potently inhibited the development of HO, even when administered transiently following soft tissue injury. Together, these data suggest that saracatinib is an efficacious clinical candidate for repositioning in FOP treatment, offering an accelerated path to clinical proof-of-efficacy studies and potentially significant benefits to individuals with this devastating condition.
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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) | 0.003 | 0.001 |
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".