DREAMM-2: Single-agent belantamab mafodotin (GSK2857916) in patients with relapsed/refractory multiple myeloma (RRMM) and renal impairment.
Bibliographic record
Abstract
8519 Background: Renal impairment, a frequent complication and poor prognostic factor in RRMM, often leads to poor tolerability of standard regimens. We report outcomes in patients with renal impairment receiving single-agent belantamab mafodotin (2.5 or 3.4 mg/kg; B-cell maturation antigen targeting immunoconjugate not renally metabolized) from the DREAMM-2 post-hoc analysis (NCT03525678). Methods: Eligible patients with RRMM had no active renal conditions and adequate renal function (based on albumin/creatinine ratio [<500 mg/g] and eGFR [mL/min/1.73 m2]: normal [≥90], mild impairment [mild, ≥60≤90], moderate impairment [mod, ≥30≤60]). Results: Overall response rates (95% CI) in patients with mild/mod impairment (2.5 mg/kg: 32% [21.4–44.0]; 3.4 mg/kg: 36% [25.6–48.5]) were similar to those in the overall population ( Lancet Oncol.2020). The median duration of response (DoR) was not reached (NR) in 2.5 mg/kg mild/mod subgroup (95% CI estimate: 4.2 months–NR); median DoR was 7.5 months (4.9–NR) in 3.4 mg/kg mild/mod subgroup. Rates of keratopathy and albuminuria were similar regardless of renal function; rates of anemia, pyrexia, and thrombocytopenia were more frequent in patients with impaired renal function (Table). eGFR did not change or changed to normal in most patients. Conclusions: Following treatment with single-agent belantamab mafodotin, patients with mild/mod renal impairment achieved a similar efficacy and safety profile as patients with normal renal function. Funding: GlaxoSmithKline (205678). Drug linker technology licensed from Seattle Genetics; monoclonal antibody produced using POTELLIGENT Technology licensed from BioWa. Clinical trial information: NCT03525678 . [Table: see text]
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".