Pacritinib is a potent ACVR1 inhibitor with significant anemia benefit in patients with myelofibrosis
Bibliographic record
Abstract
In patients with cytopenic myelofibrosis, treatment with the JAK2/IRAK1 inhibitor pacritinib was associated with anemia benefit in the phase 3 PERSIST-2 study. The impact of pacritinib on transfusion independence (TI) has not been previously described, nor has the mechanism by which pacritinib improves anemia been elucidated. Because it has been previously postulated that inhibition of activin A receptor, type 1 (ACVR1)/activin receptor-like kinase-2 improves anemia in patients with myelofibrosis via suppression of hepcidin production, we assessed the relative inhibitory potency of pacritinib compared with other JAK2 inhibitors against ACVR1. Pacritinib inhibited ACVR1 with greater potency (half-maximal inhibitory concentration [IC50] = 16.7 nM; Cmax:IC50 = 12.7) than momelotinib (IC50 = 52.5 nM; Cmax:IC50 = 3.2), fedratinib (IC50 = 273 nM; Cmax:IC50 = 1.0), or ruxolitinib (IC50 > 1000; Cmax:IC50 < 0.01). Pacritinib's inhibitory activity against ACVR1 was corroborated via inhibition of downstream SMAD signaling in conjunction with marked suppression of hepcidin production. Among patients on PERSIST-2 who were not transfusion independent at baseline based on Gale criteria, a significantly greater proportion achieved TI on pacritinib compared with those treated on best available therapy (37% vs 7%, P = .001), and significantly more had a ≥50% reduction in transfusion burden (49% vs 9%, P < .0001). These data indicate that the anemia benefit of the JAK2/IRAK1 inhibitor pacritinib may be a function of potent ACVR1 inhibition.
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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) | 0.002 | 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".