Clinical Factors Associated with Cytokine Release Syndrome and Dosing Recommendations for Restarting Elranatamab Following an Interruption
Bibliographic record
Abstract
BACKGROUND In the MagnetisMM-3 (NCT04649359) study, elranatamab (ELRA) administered at a dose of 76 mg once-weekly (QW) with 2 step-up priming doses of 12 mg on day 1 and 32 mg on day 4 had a manageable safety profile, with 56.3% of patients developing cytokine release syndrome (CRS) and 98.8% of events occurring with the first 3 doses. Here, we report clinical factors associated with CRS and dosing recommendations for restarting treatment after dose interruptions in patients with relapsed or refractory multiple myeloma (RRMM) undergoing treatment with ELRA. METHODS Eligible patients received ELRA subcutaneously in step-up priming doses of 12 and 32 mg on days 1 and 4 of cycle 1, respectively, followed by 76 mg of ELRA QW. CRS events were graded according to the criteria of the American Society for Transplantation and Cellular Therapy and managed according to published guidelines. Multivariable analyses were performed to identify clinical factors associated with CRS. Baseline factors (extramedullary disease by BICR, prior antibody-drug conjugate, prior CAR-T, number of prior lines of therapy [≤5, >5], sex, and age [continuous variable]) were included in the model for CRS after the initial dose, and only post-baseline factors (time to previous CRS and time to initial tocilizumab use) were included in the model for CRS after the second dose. A previously described population pharmacokinetic (PK) model was used to simulate different interruption scenarios and determine how long it takes for ELRA exposure to drop below the threshold associated with CRS events (Hibma J, et al. Poster presented at PAGE 31 2023 [abstract 10648]). The data used to develop this model was collected from the following open-label elranatamab trials: MagnetisMM-1 (NCT03269136; phase 1), MagnetisMM-2 (NCT04798586; phase 1), MagnetisMM-3 (NCT04649359; phase 2), and MagnetisMM-9 (NCT05014412; phase 1/2). Of 183 patients who were treated in MagnetisMM-3 with the 2 step-up priming dosing, 106 patients (57.9%) experienced CRS, 43.7% grade 1, 13.7% grade 2, and 0.5% grade 3. Tocilizumab was used to manage CRS. Of the 130 CRS events, 37 (28.5%; 20 grade 1 and 17 grade ≥2) were treated with tocilizumab, and 93 (71.5%; 82 grade 1 and 11 grade ≥2) were not. After the initial elranatamab dose, 79/183 patients had CRS. The presence of extramedullary disease was predictive of a decreased risk of CRS after the initial dose. Of the 180 who received a second elranatamab dose, 35 had CRS afterwards. In a multivariable model also adjusting for CRS after the initial dose, prior tocilizumab use was predictive of a decreased risk of CRS after the second dose. After receiving doses of 76 mg of ELRA, 16 instances of dose delays lasting >6 to ≤12 weeks were observed. In most of these instances (11/16; 68.8%), patients did not repeat the 32-mg step-up dose. In 5 instances, patients restarted treatment with 32 mg of ELRA. No patients experienced CRS after retreatment. For the 4 instances of dose delays lasting >12 weeks after a 76-mg dose, patients repeated the 32-mg step-up dose in 2 instances, and patients received >32 mg ELRA after the interruption in the other 2 instances, with none experiencing CRS after retreatment. With dose delays of 6-12 weeks, ELRA exposure decreases below the C max after a 32-mg dose but not less than that for the 12-mg dose. With dose delays >12 weeks, ELRA exposure decreases below the C max after a 12-mg dose. To minimize the risk of CRS events after dose interruptions, clinical and PK data and modeling were used to develop recommendations for restarting therapy (Figure). CONCLUSIONS Extramedullary disease and use of tocilizumab were associated with a decreased risk of CRS. Based on clinical and PK data and modeling, it is recommended that patients receiving 76 mg ELRA who then experience dose delays lasting 6-12 or >12 weeks should restart treatment at doses of 32 or 12 mg, respectively.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".