Liposomal Lapatinib in Combination with Low-Dose Photodynamic Therapy for the Treatment of Glioma
Bibliographic record
Abstract
Background: Malignant gliomas are highly invasive and extremely difficult to treat tumours with poor prognosis and outcomes. Photodynamic therapy (PDT), mediated by Gleolan®, has been studied previously with partial success in treating these tumours and extending lifetime. We aim to determine whether combining PDT using ALA-protoporphyrin IX (PpIX) with a liposomal formulation of the clinical epidermal growth factor receptor (EGFR) inhibitor, lapatinib, would increase the anti-tumour PDT efficacy. Methods: Lapatinib was given in vitro and in vivo 24 h prior to PDT and for 3–5 days following PDT to elicit whether the combination provided any benefits to PDT therapy. Live-cell imaging, in vitro PDT, and in vivo studies were performed to elucidate the effect lapatinib had on PDT for a variety of glioma cell lines and as well as GSC-30 neurospheres in vivo. Results: PDT combined with lapatinib led to a significant increase in PpIX accumulation, and reductions in the LD50 of PpIX mediated PDT in two EGFR-driven cell lines, U87 and U87vIII, tested (p < 0.05). PDT + lapatinib elicited stronger MRI-quantified glioma responses following PDT for two human glioma-derived tumours (U87 and GSC-30) in vivo (p < 0.05). Furthermore, PDT leads to enhanced survival in rats following treatment with lapatinib compared to lapatinib alone and PDT alone (p < 0.05). Conclusions: As lapatinib is approved for other oncological indications, a realization of its potential combination with PDT and in fluorescence-guided resection could be readily tested clinically. Furthermore, as its use would only be in acute settings, long-term resistance should not pose an issue as compared to its use as monotherapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".