Feasibility of Long-term Proteasome Inhibition in Multiple Myeloma by in-class Transition From Bortezomib to Ixazomib
Bibliographic record
Abstract
BACKGROUND: The ongoing US MM-6 study is investigating in-class transition (iCT) from parenteral bortezomib-based induction to all-oral IRd (ixazomib-lenalidomide-dexamethasone) with the aim of increasing proteasome inhibitor (PI)-based treatment adherence and duration while maintaining patients' health-related quality of life (HRQoL) and improving outcomes. PATIENTS AND METHODS: US community sites are enrolling non-transplant-eligible patients with newly diagnosed multiple myeloma (MM) with no evidence of progressive disease after 3 cycles of bortezomib-based therapy to receive IRd (up to 39 cycles or until progression or toxicity). The patients use mobile or wearable digital devices to collect actigraphy (activity and sleep) data and electronically complete HRQoL, treatment satisfaction and medication adherence questionnaires. The primary endpoint is progression-free survival. The key secondary endpoints include response rates and therapy duration. RESULTS: At the data cutoff, 84 patients had been treated (median age 73 years; 44% aged ≥ 75 years; 49% men; 15% Black or African American; and 10% Hispanic or Latino). Of the 84 patients, 62% were continuing therapy. The mean duration of total PI therapy was 10.1 months and for the IRd regimen was 7.3 months. With an 8-month median follow-up, the 12-month progression-free survival rate was 86% (95% confidence interval, 73%-93%) from both the start of bortezomib-based treatment and the start of IRd. The overall response rate was 62% (complete response, 4%; very good partial response, 25%; partial response, 33%) after bortezomib-based induction and 70% (complete response, 26%; very good partial response, 29%; partial response, 15%) after iCT. The IRd safety profile was consistent with previous clinical trial data, and HRQoL and treatment satisfaction were maintained. CONCLUSION: The patients included in the US MM-6 study are representative of the real-world US MM population. The use of iCT might permit prolonged PI-based therapy with promising efficacy, without impacting patients' HRQoL or treatment satisfaction.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".