Design, Synthesis, and Bioactivity Assessment of Modified Vemurafenib Analog
Bibliographic record
Abstract
Background: Metastatic melanoma is a highly aggressive malignancy with poor prognoses and frequent resistance to conventional chemotherapy. Approximately 40% of melanoma cases carry the BRAFV600E mutation, for which vemurafenib, a selective BRAFV600E inhibitor, is approved. Despite initial clinical benefits, vemurafenib often leads to drug resistance and relapse, highlighting the need for improved therapeutic strategies. Objectives, methods: In this study, we designed, synthesized, and characterized five novel vemurafenib analogs—RF-86A, RF-87A, RF-94A, RF-94B, and RF-96B—with the aim of enhancing anti-proliferative and anti-metastatic effects against human melanoma cells. Results: All compounds induced apoptosis in BRAFV600E-mutated A375 cells, with RF-86A displaying the lowest IC50 value among the series, comparable to that of vemurafenib. Moreover, RF-86A exhibited the highest selectivity index, as determined using HEK293T cells as a non-tumorigenic control. Additionally, migration assays and gelatin zymography demonstrated that the analogs, unlike vemurafenib, significantly inhibited matrix metalloproteinases MMP-2 and MMP-9, key enzymes involved in tumor invasion and metastasis. Conclusions: These findings suggest that structural modifications to the vemurafenib scaffold may improve therapeutic efficacy and offer a promising strategy to overcome acquired resistance.
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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".