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Enregistrement W4417001881 · doi:10.1182/blood-2025-100

Safety and efficacy of elranatamab in combination with iberdomide in patients with relapsed or refractory multiple myeloma: Results from the phase 1b MagnetisMM-30 trial

2025· article· en· W4417001881 sur OpenAlexaff
Attaya Suvannasankha, Jonathan L. Kaufman, Ashraf Badros, Michel Pavic, Hock Choong Lai, Muhammad Sheraz Raza, Parth Shah, Patrick J. Mueller, Jorge Acosta‐Reyes, Margaret Hoyle, Erik Vandendries, Alexander M. Lesokhin

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensDr. Everett Chalmers Regional HospitalCentre Hospitalier Universitaire de Sherbrooke
Organismes subventionnairesnon disponible
Mots-clésRefractory (planetary science)RegimenMultiple myelomaPhases of clinical researchBone marrowNeutropeniaBortezomibLenalidomideProteasome inhibitorProgressive disease

Résumé

récupéré en direct d'OpenAlex

Abstract Background Elranatamab (ELRA), a B-cell maturation antigen (BCMA)-CD3 bispecific antibody, induced deep and durable responses with a manageable safety profile in the phase 2 registrational MagnetisMM-3 study (NCT04649359) in patients (pts) with relapsed or refractory multiple myeloma (RRMM) (Prince et al, ASH2024). Among pts with no prior BCMA-directed therapy, the objective response rate (ORR) was 61.0% and 37.4% achieved complete response (CR) or better (median follow-up 33.9 mo estimated by reverse Kaplan-Meier [KM]). Oral iberdomide (IBER) is a novel CELMoD agent that enhances antimyeloma tumoricidal and immunomodulatory activity in pts with RRMM (Lonial et al, Lancet Haematol 2022). MagnetisMM-30 (NCT06215118) is a phase 1b, open-label, multicenter, dose escalation (Part 1) and dose optimization (Part 2) prospective study evaluating the safety, efficacy, and pharmacokinetics (PK) of ELRA + IBER in pts with RRMM. Here we present preliminary data from Part 1 of MagnetisMM-30. Methods Eligible pts were aged ≥18 years with a diagnosis of MM per IMWG criteria, ECOG performance status of 0 or 1, adequate organ and bone marrow function, and disease relapsed or refractory to the last antimyeloma regimen per IMWG criteria. For Part 1, pts received 2 to 4 prior lines of therapy (LOT). All pts must have received ≥2 consecutive cycles of an immunomodulatory drug-containing regimen and ≥2 consecutive cycles of a proteasome inhibitor (PI) or PI-containing regimen. Key exclusion criteria included stem cell transplant ≤12 weeks prior to enrollment; active, uncontrolled infection; prior treatment with BCMA-directed or CD3 redirecting therapy or prior CELMoD agents. Part 1 of MagnetisMM-30 was guided by a Bayesian Optimal Interval Design for dose-escalation. After receiving 2 step-up priming doses of subcutaneous (SC) ELRA (12 mg on day 1 and 32 mg on day 4) and the first full dose of ELRA on day 8 (76 mg), pts received SC ELRA at dose level (DL) 1 or DL2 in 28-day cycles. In DL1, pts received a starting dose of ELRA at 76 mg QW while in DL2, pts received a starting dose of ELRA at 76 mg Q2W. IBER was given daily for 21 days of each cycle (DL-1: 0.75 mg, DL1: 1.0 mg, DL2: 1.3 mg). The primary endpoint of Part 1 was dose-limiting toxicities (DLTs) during the first cycle of treatment. Secondary endpoints included adverse events and laboratory abnormalities, ORR, CR rate, time-to-event outcomes, PK, minimal residual disease negativity rate, and immunogenicity. Results Of the 22 pts in Part 1 (DL1: 13, DL−1: 9), the median age was 68 y (range, 46-83), 10 (45.5%) were male, 4 (18.2%) had extramedullary disease, 9 (40.9%) had high-risk cytogenetics, defined as t(4;14), t(14;16), or del(17p), 1 (4.5%) had R-ISS stage III, and 2 (9.1%) had ≥50% baseline bone marrow plasma cells. Pts had a median of 2.5 prior LOT (range, 2-4), and 11 (50%) had triple-class refractory disease. At data cutoff (June 23, 2025), the median (range) ELRA treatment duration was 3.1 mo (0.5-7.2) overall and 6.1 mo (0.5-7.2) for DL1 and 1.9 mo (1.5-3.3) for DL−1. IBER treatment duration was 2.6 mo (0.1-6.7) overall and 5.7 mo (0.1-6.7) for DL1 and 1.6 mo (0.7-3.0) for DL−1. ELRA + IBER was ongoing in 77.3% (DL1: 8, DL−1: 9) of pts. Four pts had DLTs; 2 in DL1 (1 grade [G]3 anorexia; 1 G4 neutropenia) and 2 in DL−1 (1 G3 febrile neutropenia; 1 G4 neutropenia). Treatment-emergent adverse events (TEAEs) were reported in 100% (G3/4 68.2%) of pts. The most frequent TEAEs (any G ≥45% or G3/4 ≥10%) were cytokine release syndrome (68.2%, all ≤G2), fatigue (63.6%, all ≤G2), neutropenia (59.1%, G3/4 59.1%), diarrhea (45.5%, all ≤G2), anemia (31.8%, G3/4 13.6%), and thrombocytopenia (27.3%, G3/4 13.6%). Infections were reported in 40.9% (G3/4 4.5%) of pts and immune effector cell–associated neurotoxicity syndrome events were reported in 2 pts (9.1%, 1 G1 and 1 G2). At a median follow-up of 6.1 mo (95% CI, 2.8-7.1), estimated by reverse Kaplan-Meier, the unconfirmed ORR was 90.9% (95% CI, 70.8-98.9) in 20/22 pts; 45.5% (10/22) had CR or better and 68.2% (15/22) had very good partial response or better. The confirmed ORR was 77.3% (17/22) with a median time to response of 1.1 mo (range, 0.5-2.4). Conclusions ELRA + IBER demonstrated a favorable safety profile and encouraging efficacy. The study continues enrolling and will explore ELRA + IBER in a larger group of pts with RRMM. Results from a longer follow-up will be presented.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Essai non randomisé · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,011

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,000
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,013
Tête enseignante GPT0,276
Écart entre enseignants0,263 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeEssai non randomisé
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations7
Publié2025
Routes d'admission1
Résumé présentoui

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