CIMAvax-EGF: A novel therapeutic vaccine for advanced lung cancer
Bibliographic record
Abstract
The results allowing the Cuban Regulatory Agency (CECMED) to grant the Sanitary Registration to the CIMAvax-EGF cancer vaccine for advanced non-small cell lung cancer (NSCLC) are shown. This was the first registration of a therapeutic vaccine in Cuba and also the first registration of a lung cancer vaccine in the world. Hence, a unique therapeutic vaccine is offered to lung cancer patients, which will increase survival and their quality of life. For this purpose, significant preclinical, clinical, regulatory, productive and negotiation challenges were to be faced. The results obtained in these fields led to 18 scientific papers published in high impact journals and 4 invention objects, generating several patents in Cuba and other countries. In the pre-clinical setting, immunogenicity, safety and anti-tumoral effects were demonstrated in different animal species. The clinical experience began in 1995. Up to now, five phase I-II clinical trials have concluded in Cuba, two phase II have also concluded, one in Cuba and another one in Canada and the UK, and a phase II-III trial with an optimized schedule as well as a phase III trial are currently in progress in Cuba. In the regulatory field, a fast-track registration strategy was designed and performed. It required novel regulatory conceptions to develop this unique product. A scalable, reproducible and controlled productive process was carried out, together with a quality system that ensured full GMP compliance. Funds for product development came from implementing a novel negotiation strategy: negotiation of intangibles.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".