Phase 1b study of selinexor, a first in class selective inhibitor of nuclear export (SINE) compound, in combination with doxorubicin in patients (pts) with locally advanced or metastatic soft tissue sarcoma (STS).
Bibliographic record
Abstract
11562 Background: Selinexor is a first-in-class SINE compound with single-agent activity in STS. We undertook this study to determine the safety, tolerability and efficacy of selinexor when combined with doxorubicin in pts with locally advanced or metastatic STS. Methods: This phase 1b study was conducted using a modified toxicity probability index (mTPI) design. Patients with locally advanced or metastatic STS received selinexor at two dose levels (60 or 80mg weekly PO) plus doxorubicin (75mg/m2 IV q21 days, max 6 cycles). Pts with stable disease or better (per RECIST 1.1 criteria) after 6 cycles of combination treatment received selinexor monotherapy until disease progression or unacceptable toxicity. Disease assessments were made with standard imaging after every 2 cycles. Limited pharmacokinetic (PK) data was collected for the first 3 pts at each dose level. Results: 13 pts (11F/2M, ECOG 0/1: 5/8, median age 63 years [range 51-73]) were enrolled. Disease subtypes included leiomyosarcoma (n = 6), undifferentiated pleomorphic sarcoma (n = 2; UPS), liposarcoma (n = 2) and other sarcomas (n = 3). Three pts at 60mg selinexor and 10 pts at 80mg selinexor have been treated. The most common G3 drug related adverse events (AEs) were hematological, including neutropenia n = 6 (46%), anemia n = 3 (23%). There were two dose-limiting toxicities (febrile neutropenia and unresolved fatigue lasting more than 7 days), both at the 80mg dose level. Of the 13 evaluable pts (median follow-up of 15 weeks [range 5-31]), partial response was seen in 3 pts (23%, n = 1 for UPS, malignant peripheral nerve sheath tumor and myxofibrosarcoma) and stable disease was seen in 7 (54%). PK analysis of selinexor did not demonstrate changes compared to single agent profile. Conclusions: Our initial data demonstrate that the combination of selinexor and doxorubicin appears to be tolerable and safe. There was no exposure changes oberserved for selinexor in this combination regimen. Updated toxicity, safety and efficacy data will be presented at the meeting. Clinical trial information: NCT03042819.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".