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Iberdomide, bortezomib, and dexamethasone (IberVd) in transplant-ineligible (TNE) newly diagnosed multiple myeloma (NDMM): Updated results from the CC-220-MM-001 trial.

2025· article· en· W4410809758 on OpenAlexaff
Darrell White, Brea Lipe, Mercedes Gironella, Rubén Niesvizky, Albert Oriol, Anna Sureda, Manisha Bhutani, Cristina Encinas Rodríguez, Abdullah Khan, Michael Amatangelo, Danny V. Jeyaraju, Kexin Jin, Thomas Solomon, Kevin Hong, Alpesh Amin, Olumoroti Aina, Paulo Maciag, Niels W.C.J. van de Donk, Sagar Lonial

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsQueen Elizabeth II Health Sciences CentreDalhousie University
Fundersnot available
KeywordsMedicineBortezomibMultiple myelomaDexamethasoneInternal medicineOncology

Abstract

fetched live from OpenAlex

7532 Background: Lenalidomide (LEN), bortezomib (BORT), and dexamethasone (DEX) are recommended for NDMM. Iberdomide (IBER), an oral CELMoD agent, has stronger tumoricidal and immune-stimulatory effects than LEN and shows synergy with DEX and BORT in preclinical models. IberVd has shown meaningful efficacy and safety in patients (pts) with TNE NDMM in the ongoing phase 1/2 CC-220-MM-001 trial (NCT02773030). Here we report updated results with longer follow-up from the IberVd dose-expansion cohort. Methods: Eligible pts had untreated NDMM and were TNE or deferred. Oral IBER was given on days (D) 1–14 of each 21-d cycle (C) in C1–8 and on D1–21 of each 28-d cycle in C ≥ 9, with subcutaneous BORT (starting at 1.3 mg/m 2 ) on D1, 4, 8, and 11 in C1–8, plus oral DEX on D1, 2, 4, 5, 8, 9, 11, and 12 in C1–8 and weekly in C ≥ 9 (20 or 10 mg if > 75 y of age in C1–8; 40 or 20 mg if > 75 y in C ≥ 9). Endpoints included efficacy, safety, pharmacokinetics, and minimal residual disease (MRD) assessment by next-generation flow cytometry. Results: As of May 29, 2024, 18 pts had received IberVd (1 pt 1.0 mg; 17 pts 1.6 mg). Median age was 77.5 (57–84) y, 12 (66.7%) pts were male, 17 (94.4%) White, 1 (5.6%) Hispanic/Latino, and 11 (61.1%) had high-risk cytogenetics. Median follow-up was 25 (0.7–29.5) mo. Median treatment duration was 24.9 (0.7–29.5) mo, median number of cycles received was 25 (1–34), and 11 (61.1%) pts remain on treatment; 3 pts discontinued due to withdrawal, 2 to adverse events (AEs), 1 to progressive disease, and 1 to physician decision. One death was reported during follow-up. In the safety population (n = 17), 14 (82.4%) pts had grade (Gr) 3/4 treatment-emergent AEs (TEAEs); primarily infections (47.1%), including pneumonia (17.6%) and COVID-19 (11.8%). The most common hematologic Gr 3/4 TEAE was neutropenia (29.4%); 2 (11.8%) pts had Gr 3–4 peripheral neuropathy. Other Gr 3/4 non-hematologic TEAEs like fatigue and diarrhea were rare. IBER dose interruptions and reductions due to TEAEs occurred in 14 (82.2%) and 10 (58.8%) pts, respectively. Dose reductions were mainly due to peripheral neuropathy (23.5%), neutropenia (11.8%), and thrombocytopenia (11.8%). TEAEs were manageable with dose modifications/interruptions and G-CSF use. In the evaluable pts (n = 16), the overall response rate was 100% with 8 stringent complete responses, 4 complete responses (CRs), 3 very good partial responses, and 1 partial response. Median time to response was 0.7 (0.7–3.9) mo, median duration of response was not reached, and 4 pts deepened response post 1 y treatment. MRD negativity at 10 -5 was reported in 8 (50.0%) pts, and all had ≥ CR. Conclusions: With longer follow-up (13–25 mo), IberVd confirmed durable deep responses, with ≥ CR % rising from 56.3% to 75.0%, and an encouraging safety profile with no new signals in pts with TNE NDMM. These data support IberVd evaluation in the frontline setting. Clinical trial information: NCT02773030 .

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.106
GPT teacher head0.445
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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