Phototoxicity of B-RAF inhibitors: Exclusively due to UVA radiation and rapidly regressive
Bibliographic record
Abstract
BACKGROUND: New targeted melanoma therapies such as B-RAF inhibitors have shown high and promising clinical benefit but have cutaneous side-effects, including photosensitivity, which is triggered in the UVA radiation spectrum. However, visible spectrum implication has not yet been investigated. We conducted a study to determine whether visible light also contributes to the phototoxicity action spectrum of vemurafenib. The secondary end points were to determine the time to complete regression of the phototoxicity post-vemurafenib discontinuation and whether there was a significant difference between the UVA radiation immediate reactivity cut-offs, in patients treated with vemurafenib vs. those treated with dabrafenib. METHOD: This prospective, observational study included patients with B-RAF mutant metastatic melanoma: 34 patients treated with vemurafenib and 9 with dabrafenib. RESULTS: The visible-light phototest results in patients treated with vemurafenib were all negative before and after 2 months of treatment. The UVA radiation phototests conducted 1 or 2 weeks post-vemurafenib discontinuation in 4 patients showed a normalised UVA-radiation reactivity cut-off. UVA radiation phototests after 2 months of treatment were conducted for all patients. The UVA radiation reactivity cut-off had been lowered for 30 patients (88%) on vemurafenib and 3 patients (33%) on dabrafenib. The median UVA radiation reactivity cut-off was 12 J/cm(2) for the patients on vemurafenib and 20 J/cm(2) for the patients on dabrafenib. CONCLUSION: B-RAF inhibitor phototoxicity is exclusively triggered by UVA radiation and resolves rapidly post-treatment discontinuation. A significant difference between the UVA immediate reactivity cut-offs, vemurafenib vs. dabrafenib, explains the difference in the clinical photosensitivity rates reported in the 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".