Anti-cancer liposomal chemophototherapy using bilayer-localized photosensitizer and cabazitaxel
Bibliographic record
Abstract
Photodynamic therapy (PDT) is a non-invasive tumor ablation modality that can be enhanced in combination with concurrent chemotherapy. Previously, we demonstrated that liposomes containing a bilayer-anchored photosensitizer (porphyrin-phospholipid; PoP) can be loaded with drugs in their aqueous core to improve drug delivery and tumor ablation upon target tissue irradiation with red-light. In the present work, we demonstrate that this concept can be extended to drugs loaded within the hydrophobic bilayer of liposomes. Cabazitaxel (CTX) is a potent second generation taxane anti-cancer drug that was loaded in the bilayer of liposomes also containing 0.1 molar% PoP, generating CTX-loaded PoP liposomes (CTX-PoP-Lip). CTX-PoP-Lip showed unilamellar vesicle morphology, and exhibited integrity in storage and serum, while maintaining drug stability under laser irradiation. In vitro cell killing evaluation showed that red-light laser irradiation induced cytotoxicity in cells incubated with CTX-PoP-Lip, compared to control treatments. In vivo pharmacokinetic analysis revealed that following intravenous administration to mice, CTX and PoP exhibited somewhat altered circulation profiles, suggesting that the CTX may have exchanged with serum factors in blood. Nevertheless, when a single treatment of CTX-PoP-Lip with laser irradiation was administered to mice bearing human MIA Paca-2 tumors, tumors were effectively ablated whereas the equivalent chemotherapy and PDT monotherapies were ineffective. These results demonstrate the versatility of liposome delivery systems for achieving tumor ablation with chemophototherapy.
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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".