Systemic MEK inhibition enhances the efficacy of 5-aminolevulinic acid-photodynamic therapy
Bibliographic record
Abstract
BACKGROUND: Protoporphyrin IX (PpIX) gets accumulated preferentially in 5-aminolevulinic acid (5-ALA)-treated cancer cells. Photodynamic therapy (PDT) utilises the accumulated PpIX to trigger cell death by light-induced generation of reactive oxygen species (ROS). We previously demonstrated that oncogenic Ras/MEK decreases PpIX accumulation in cancer cells. Here, we investigated whether combined therapy with a MEK inhibitor would improve 5-ALA-PDT efficacy. METHODS: Cancer cells and mice models of cancer were treated with 5-ALA-PDT, MEK inhibitor or both MEK inhibitor and 5-ALA-PDT, and treatment efficacies were evaluated. RESULTS: Ras/MEK negatively regulates the cellular sensitivity to 5-ALA-PDT as cancer cells pre-treated with a MEK inhibitor were killed more efficiently by 5-ALA-PDT. MEK inhibition promoted 5-ALA-PDT-induced ROS generation and programmed cell death. Furthermore, the combination of 5-ALA-PDT and a systemic MEK inhibitor significantly suppressed tumour growth compared with either monotherapy in mouse models of cancer. Remarkably, 44% of mice bearing human colon tumours showed a complete response with the combined treatment. CONCLUSION: We demonstrate a novel strategy to promote 5-ALA-PDT efficacy by targeting a cell signalling pathway regulating its sensitivity. This preclinical study provides a strong basis for utilising MEK inhibitors, which are approved for treating cancers, to enhance 5-ALA-PDT efficacy in the clinic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".