A review of the mechanism of action, safety, and efficacy of selinexor in multiple myeloma
Bibliographic record
Abstract
In recent years, the armamentarium of routinely available treatments for relapsed and/or refractory multiple myeloma (RRMM) in Canada has dramatically expanded, but treatment gaps still exist. In early relapse (1-3 prior lines), monoclonal antibody (mAb) combinations on a backbone of lenalidomide or bortezomib (e.g. DRd, DVd) have been the mainstay, with combinations building on second generation backbones such as pomalidomide and carfilzomib (e.g. PCd, PVd, Kd) largely reserved for later relapse (after 2 prior lines). However, the increasing use of multi-class drug combinations in the frontline (e.g. DRd, RVd) and a shift towards ongoing therapy until progression, renders patients heavily drug-exposed and refractory at time of early relapse, needful of treatments with novel mechanisms of action. Selinexor is poised to fill an unmet need with a unique, non-overlapping mechanism of action to other available agents. XPOVIO® (selinexor) is indicated in combination with bortezomib and dexamethasone for the treatment of adult patients with multiple myeloma who have received at least one prior therapy. SVd received Health Canada approval May 31, 2022. This review will present data on selinexor’s mechanism of action, efficacy in combination with dexamethasone and bortezomib (Sd, SVd), dosing and scheduling, as well as the management of its common and distinct toxicities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".