ET-35 * PACRITINIB: A NOVEL JAK/STAT INHIBITOR WITH TRANSLATIONAL RELEVANCE FOR GBM
Bibliographic record
Abstract
Glioblastoma multiforme (GBM) is characterized by an aggressive clinical course, therapeutic resistance, and striking molecular heterogeneity. GBM-derived brain tumor initiating cells (BTICs) closely model this molecular heterogeneity and may have a key role in tumor recurrence and therapeutic resistance. Recent evidence, from our lab and others, indicates that the Janus kinase (JAK)2/signal transducer and activator of transcription (STAT)3 signaling pathway is an important mediator of tumor cell survival, growth, and invasion in a large group of GBMs. Thus, inhibition of the JAK/STAT3 pathway may hold great promise as a therapeutic strategy for GBM. However, to date, drugs targeting activated STAT3 in clinically relevant models have not been successfully transitioned into clinical studies. Here we investigated the efficacy of a novel JAK/STAT inhibitor, Pacritinib, in BTIC lines cultured from GBM patients. Pacritinib is currently in a multicenter, randomized, Phase 3 trial comparing its efficacy and safety with that of best available therapy in patients with primary myelofibrosis. In GBM BTICs, Pacritinib administration resulted in on-target JAK2/STAT3 inhibition at 1-2 µΜ that dramatically reduced cell survival in a large number of lines, regardless of endogenous MGMT promoter methylation or EGFR, PTEN, and TP53 mutational status. Pacritinib was also found to cross the blood-brain barrier in NOD-SCID mice, by liquid chromatography-mass spectrophometry, with no toxicity observed on repeated administration of up to 200mg/kg per day by oral gavage. Previous studies on different JAK/STAT3 inhibitors, by others and us, demonstrated toxicity at doses required to shut-off STAT3 signaling and achieve tumour cell death in vivo. We are currently investigating the actions of Pacritinib in orthotopic BTIC xenograft animal survival studies. Given its previously established human safety profile and demonstrated activity in the clinically relevant BTIC model, Pacritinib may hold considerable promise for clinical translation in GBM.
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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 | 0.001 |
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".