A phase 1b dose-escalation study of carfilzomib in combination with thalidomide and dexamethasone in patients with relapsed/refractory systemic immunoglobulin light chain amyloidosis
Bibliographic record
Abstract
Proteasome inhibitors are the backbone of AL amyloidosis treatment – bortezomib being most widely used. Carfilzomib is a proteasome inhibitor licenced to treat multiple myeloma; autonomic and peripheral neuropathy are uncommon toxicities with carfilzomib. There is limited data on the use of carfilzomib in AL amyloidosis. Here, we report the results of a phase Ib dose-escalation study of Carfilzomib-Thalidomide-Dexamethasone (KTD) in relapsed/refractory AL amyloidosis. The trial registered 11 patients from 6 UK centres from September 2017 to January 2019; 10 patients received at least one dose of trial treatment. 80 adverse events were reported from 10 patients in the 1st three cycles. One patient experienced dose-limiting toxicity (acute kidney injury) at a dose of 45 mg/m2, and another patient had a SAR (fever). Five patients experienced an AE ≥ grade 3. There were no haematologic, infectious, or cardiac AE ≥ grade 3. The overall haematological response rate (ORR) at the end of three cycles of treatment was 60%. Carfilzomib 45 mg/m2 weekly can be safely given with thalidomide and dexamethasone. The efficacy and tolerability profile appears comparable to other agents in relapsed AL amyloidosis. These data provide a framework for further studies of carfilzomib combinations in AL amyloidosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".