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S188: TECLISTAMAB IN COMBINATION WITH DARATUMUMAB, A NOVEL, IMMUNOTHERAPY-BASED APPROACH FOR THE TREATMENT OF RELAPSED/REFRACTORY MULTIPLE MYELOMA: UPDATED PHASE 1B RESULTS

2022· article· en· W4283326517 on OpenAlexaff
Paula Rodríguez‐Otero, Anita D’Souza, Donna Reece, N. W. van de Donk, Ajai Chari, Aparna Krishnan, Thomas G. Martin, María‐Victoria Mateos, Daniel Morillo, David D. Hurd, Laura Rosiñol, Ralph Wäsch, Deeksha Vishwamitra, Shu-Chen Lin, Thomas J. Prior, Lien Vandenberk, M.-A. D. Smit, Albert Oriol, Bhagirathbhai Dholaria

Bibliographic record

VenueHemaSphere · 2022
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsDaratumumabMedicineInternal medicineOncologyMultiple myelomaAdverse effectPhases of clinical researchRefractory (planetary science)Clinical trialBortezomib

Abstract

fetched live from OpenAlex

Background: Teclistamab (JNJ-64007957) is a B-cell maturation antigen (BCMA) × CD3 T-cell redirecting bispecific antibody currently under investigation in patients with relapsed/refractory multiple myeloma (RRMM). Daratumumab is a CD38-targeting monoclonal antibody with direct on-tumor and immunomodulatory mechanisms of action. The preliminary results from the phase 1b multicohort TRIMM-2 study showed tolerable safety with no overlapping toxicities, and encouraging efficacy, supporting the combination of teclistamab with daratumumab for the treatment of RRMM. Aims: We report updated results from the TRIMM-2 study with additional patients and longer follow-up. Methods: Eligible patients were ≥18 years of age with a MM diagnosis and previously treated with ≥3 prior lines of therapy (including a proteosome inhibitor [PI] and immunomodulatory drug [IMiD]) or were double-refractory to a PI and IMiD. Patients who had received anti-CD38 therapy ≤90 days prior were excluded. Written informed consent was obtained from all eligible patients. Patients received subcutaneous (SC) daratumumab 1800 mg per approved schedule and teclistamab SC 1.5–3 mg/kg once weekly or every 2 weeks. Primary objectives of the study were to identify the recommended phase 2 dose for the teclistamab and daratumumab combination and to assess safety of the combination. Responses were assessed by IMWG criteria. Adverse events (AEs) were graded per CTCAE v5.0, except for cytokine release syndrome (CRS) and immune effector cell–associated neurotoxicity syndrome (ICANS), which were graded per ASTCT guidelines. Results: At the Jan 13, 2022 data cutoff, the median follow-up was 7.2 months (range 0.1–16.6). Among the safety population (N=46), 52% were females, and the median age was 67 years (range 50–79). Patients received a median of 6 prior lines of therapy (range 2–17); 74% of patients were triple-class exposed; 63% were penta-drug exposed, and 15% were anti-BCMA exposed. Overall, 91% of patients had ≥1 AE of any grade; 78% had grade 3/4 AEs. The most common AE was CRS (61%; all grade 1/2); median time to onset was 2 days and median duration was 2 days. Other AEs included neutropenia (54%; grade 3/4 50%), anemia (46%; grade 3/4 28%), thrombocytopenia (33%; grade 3/4 28%), and diarrhea (33%; grade 3/4 2%). Infections occurred in 29 patients (63%; grade 3/4 28%). One patient had grade 1 ICANS that was fully resolved. Among 37 response-evaluable patients, the overall response rate was 78% (29/37); 27 patients (73%) had very good partial response (VGPR) or better (Table). While the median duration of response was not reached, median time to first response across dosing cohorts was 1.0 month (range 0.9–2.8). Upregulation of CD38+/CD8+ T cells and proinflammatory cytokines was observed after teclistamab dosing in combination with daratumumab, supporting potential synergy of the combination in patients with prior anti-CD38 exposure. Updated results will be presented. Image:Summary/Conclusion: Teclistamab in combination with daratumumab is a novel immunotherapy approach that may yield improved clinical efficacy in heavily pretreated patients with RRMM.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

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

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 head0.036
GPT teacher head0.308
Teacher spread0.272 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
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".

Quick stats

Citations15
Published2022
Admission routes1
Has abstractyes

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