Title: Synergic effects of anticancer peptide CIGB-552 and Cisplatin in lung cancer models
Bibliographic record
Abstract
Non-small cell lung cancer constitutes one the most frequent and lethal forms of the disease. The antitumor peptide CIGB-552 is a new targeted anticancer therapy which molecular mechanism is associated with the inhibition of the transcription factor NF-kB, mediated by COMMD1 protein stabilization. However, its pharmacological potential in combination with chemotherapy is unknown. In this study, we examined the antiproliferative capacity of CIGB-552 in combination with chemotherapeutic agents in the non-small cell lung cancer cell line NCI-H460 and we confirmed drug interactions in vivo, in a mouse model of TC-1 lung cancer. We focus our research in the combination of CIGB-552 and the antineoplastic agent Cisplatin (CDDP) in a concomitant treatment. Our results demonstrate a clear synergic effect between 37.5 μM of CIGB-552 and 5 μM of CDDP under concomitant scheme, on proliferation inhibition, cell cycle arrest, apoptosis induction and oxidative stress response. The effect of CIGB-552 (1 mg/kg) and CDDP (0.4 mg/kg) administrated as a combined therapy was demonstrated in vivo in the TC-1 murine model where the combination achieved an effective antitumor response, without any deterioration signs or side effects. These findings demonstrate the efficacy of the concomitant combination of both drugs in preclinical studies and support the use of this therapy in clinical trials.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".