Novel chloroquine derivative suppresses melanoma cell growth by DNA damage through increasing ROS levels
Bibliographic record
Abstract
Melanoma is a fatal cancer with a significant feature of resistance to traditional chemotherapeutic drugs and radiotherapy. A mutation in the kinase BRAF is observed in more than 66% of metastatic melanoma cases. Therefore, there is an urgent need to develop new BRAF-mutant melanoma inhibitors. High-dose chloroquine has been reported to have antitumour effects, but it often induces dose-limiting toxicity. In this study, a series of chloroquine derivatives were synthesized, and lj-2-66 had the best activity and was selected for further investigation. Furthermore, the anti-BRAF-mutant melanoma effect and mechanism of this compound were explored. CCK-8 and colony formation assays indicated that lj-2-66 significantly inhibited the proliferation of BRAF-mutant melanoma cells. Flow cytometry revealed that lj-2-66 induced G2/M arrest in melanoma cells and promoted apoptosis. Furthermore, lj-2-66 increased the level of ROS in melanoma cells and induced DNA damage. Interestingly, lj-2-66 also played a similar role in BRAF inhibitor-resistant melanoma cells. In summary, we found a novel chloroquine derivative, lj-2-66, that increased the level of ROS in melanoma cells and induced DNA damage, thus leading to G2/M arrest and apoptosis. These findings indicated that lj-2-66 may become a potential therapeutic drug for melanoma harbouring BRAF mutations.
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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".