Emepepimut-S for non-small cell lung cancer
Bibliographic record
Abstract
INTRODUCTION: Immunotherapy as a possible therapeutic option for cancer has been of great importance due to the innovative development of vaccines. Various molecules have been tested and emepepimut-S (Biomira Liposomal Peptide 25 (BLP 25)) has emerged as an option, particularly in lung cancer. AREAS COVERED: A PubMed literature and ClinicalTrials.gov search was conducted using the terms: emepepimut, BLP25, NSCLC, cancer immunotherapy, cancer vaccine and MUC1. This review covers how emepepimut-S acts against the mucin 1 (MUC1) tumor-associated antigen producing a cellular immune response against the cells that express MUC1 and the most important clinical data available that led to the ongoing Phase III trial. EXPERT OPINION: The results obtained in the Phase I/II trials are promising, showing a favorable toxicity with a benefit in survival in NSCLC patients. As future trials develop, demonstration of the long-term survival benefit, understanding of the various mechanisms of immune response initiated by the drug and the selection of patients that will highly benefit from the immunotherapy will be elucidated. The safety and extension in survival makes emepepimut-S a very interesting drug and could, therefore, offer a possibility of treatment and maintenance, particularly for good performance status patients with locally advanced NSCLC.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".