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Record W4405050034 · doi:10.1182/blood-2024-198804

Efficacy and Safety of Teclistamab in Relapsed or Refractory Multiple Myeloma: A Systematic Review and Meta-Analysis

2024· review· en· W4405050034 on OpenAlexaboutno aff
Zaheer Qureshi, Abdur Jamil, Faryal Altaf, Rimsha Siddique, Muhammad Faisal, Sorab Gupta, Paul Berard

Bibliographic record

VenueBlood · 2024
Typereview
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMultiple myelomaInternal medicineRefractory (planetary science)LenalidomideOncologyMeta-analysisPomalidomideBiology

Abstract

fetched live from OpenAlex

Introduction: Multiple myeloma (MM) is a clonal abnormal proliferation of plasma cells resulting in elevated plasma immunoglobulin with end-organ damage with anemia, osteolytic bone lesions, renal failure, and/or hypercalcemia. The FDA approved bispecific antibody Teclistamab, which engages T cells via CD3 ligand and myeloma cells via BCMA binding domain, in October 2022. It has shown promise due to its efficacy and manageable safety profile. This meta-analysis aims to synthesize data on the effectiveness and safety of Teclistamab in treating relapsed or refractory multiple myeloma (RRMM). Methods: The review adhered to PRISMA guidelines, utilizing databases such as PubMed, Web of Science, EMBASE, and Google Scholar to find studies evaluating teclistamab's efficacy and safety. Eligibility criteria included studies involving adult RRMM patients treated with teclistamab, focusing on outcomes like overall response rate (ORR), adverse events, complete response (CR), and excellent partial response (VGPR). On the other hand, safety analysis involved pooling all adverse events of grade 3 or above (grade ≥3 adverse events), including grade ≥3 cytokine release syndrome (CRS) and neurotoxic events. Furthermore, subgroup analyses were conducted to identify factors associated with ORR. Two reviewers independently performed data extraction, and the quality of the included studies was assessed using the Newcastle Ottawa Scale. Results: Five studies with 661 RRMM patients were included in the analysis. The pooled results showed that teclistamab led to an ORR of 62.8% (95% Confidence Interval (CI): 58.6 - 66.8), a ≥VGPR of 52.1% (95% CI: 46.8 - 57.3), and a ≥CR of 29.5% (95% CI: 21.9 - 38.4). When the ORR was assessed in different subgroups, we found that patients with extramedullary disease (EMD) had considerably lower ORR than those without EMD (45% vs. 71%, p<0.0001). Additionally, the ORR was significantly lower in patients with prior BCMA-directed therapy (OR: 2.24, p = 0.002) and those with stage III disease (OR: 3.69, p = 0.0001). However, the subgroup analyses showed no considerable difference in the ORR between patients with high or standard-risk cytogenetics (OR: 1.05 p = 0.82) and those with penta-drug or triple-class-refractory disease (OR: 0.97 p = 0.89). Regarding the safety of teclistamab, the pooled results showed that the incidence of grade ≥3 adverse events was high (90.7%). However, grade ≥3 CRS and neurotoxic events were low (1.5% and 2.2%, respectively). Discussion/Conclusion: RRMM patients treated with teclistamab display good response rates. However, this good response was based on studies with short follow-up duration. Therefore, more research in long-term follow-up studies is required to establish the durability of these responses. In addition, the subgroup analyses suggested that before BCMA-directed therapy, the presence of EMD and stage III disease were associated with poor response rates. However, the response rates for patients with prior BCMA-related therapy are relatively high, meaning teclistamab can also be a valuable therapeutic option for these patients. Teclistamab also displayed low incidences of CRS and neurotoxicity. Therefore, with appropriate measures, resources, and infrastructure, it can be safely administered to 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 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)0.010
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.023
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0160.025
Bibliometrics0.0060.008
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.100
GPT teacher head0.386
Teacher spread0.287 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations1
Published2024
Admission routes1
Has abstractyes

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