Repurposing alpelisib, an anti-cancer drug, for the treatment of severe TIE2-mutated venous malformations: preliminary pharmacokinetics and pharmacodynamic data
Bibliographic record
Abstract
Abstract Extensive venous malformations (VM) involving limbs severely impact quality of life, mostly due to chronic pain and functional limitations. Patients can also display coagulopathy with associated risks of life-threatening thromboembolism and bleeding. Current pharmacological VM treatments (e.g. sirolimus) are not universally effective as 10% of patients present intractable debilitating and/or critical disease. Novel therapies are therefore highly needed for treatment-resistant VM. Over 70% of sporadic VM are attributed to activating mutations in the TEK gene, encoding the receptor tyrosine kinase TIE2 expressed by venous endothelial cells. Despite in vitro studies showing the superiority of alpelisib over sirolimus in inhibiting TIE2 signalling pathway and vein remodelling, there are currently no clinical reports of alpelisib use in VM. Our aim was therefore to assess the effect of alpelisib in TIE-2 mutated VM and to assess its pharmacokinetics. Three patients with a VM harboring the TEK L914F mutations were treated with alpelisib in an open-label compassionate use study. All patients experienced significant improvement. Pain was controlled, gait improved, size of the abnormal venous network decreased, and coagulopathy showed dramatic improvement. Drug exposure was highly variable despite similar weight-adjusted doses, suggesting that alpelisib dosing should be individualized to patient’s characteristics and guided by therapeutic drug monitoring to improve clinical response.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".