Isatuximab in combination with cemiplimab in patients with relapsed/refractory multiple myeloma: A phase 1/2 study
Bibliographic record
Abstract
BACKGROUND: Given the incurable nature of multiple myeloma (MM), efforts are made to improve the efficacy of anti-CD38 monoclonal antibodies via combinations with other potentially synergistic therapies. This Phase 1/2 trial (NCT03194867) was designed to determine whether cemiplimab (anti-PD-1) enhances the anti-myeloma activity of isatuximab (anti-CD38) in patients with relapsed and refractory multiple myeloma (RRMM), to confirm the feasibility of the combination, determine its efficacy, and further evaluate its safety. METHODS: Patients received isatuximab 10 mg/kg once weekly for 4 weeks followed by every 2 weeks (Isa), or isatuximab 10 mg/kg plus cemiplimab 250 mg every 2 (Isa + CemiQ2W) or every 4 weeks (Isa + CemiQ4W). RESULTS: Overall, 106 patients with RRMM treated with a median of 4 prior lines were included; 25.5% had high-risk cytogenetics, 63.2% were refractory to proteasome inhibitors and immunomodulatory agents, 26.4% were previously exposed to daratumumab, and 84.0% were refractory to their last treatment line. There were no major changes in the safety or pharmacokinetic profile of isatuximab with the addition of cemiplimab. As assessed by investigators, four patients (11.8%) in the Isa arm, nine patients (25.0%) in the Isa + CemiQ2W arm, and eight patients (22.2%) in the Isa + CemiQ4W arm were responders. Though response rates were numerically higher in cemiplimab-containing arms, differences were not statistically significant and did not translate to improved progression-free or overall survival after a median follow-up of 9.99 months. CONCLUSION: Our results suggest a marginal benefit by adding cemiplimab to isatuximab, despite demonstration of target engagement, without additional observed safety issues.
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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".