A phase IIa study of afuresertib, an oral pan‐AKT inhibitor, in patients with Langerhans cell histiocytosis
Bibliographic record
Abstract
BACKGROUND: Langerhans cell histiocytosis (LCH) is a clonal neoplasm characterized by widely varied clinical presentations, including multisystem involvement and systemic inflammatory symptoms. The AKT pathway is relevant to survival and proliferation of dendritic cells, and is also often upregulated in hematopoietic malignancies. A clinical response in an adult patient with LCH participating in the first-in-human trial of afuresertib prompted this prospective trial. PROCEDURE: The population in the current study included treatment-naïve (n = 7) and recurrent/refractory patients with LCH (n = 10), who received oral afuresertib (125 mg). The majority of patients were treated for > 24 weeks, with four patients receiving treatment for > 48 weeks. RESULTS: Pharmacokinetic analysis showed similar exposures in previously reported patients with other hematologic malignancies. Primary drug-related toxicities included Grade 1/2 nausea, diarrhea, dyspepsia, and vomiting. Grade 3 toxicities included fatigue, diarrhea, and pain (one of each). Another severe adverse event involved soft tissue necrosis. The overall response rate in evaluable subjects was 33% in treatment-naïve patients and 28% in patients with recurrent/refractory disease, which did not meet the predefined Bayesian criteria for efficacy. CONCLUSION: Afuresertib has clinical activity in some patients with newly diagnosed and advanced LCH.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".