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 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".