Porphyrin-lipid stabilized paclitaxel nanoemulsion for combined photodynamic therapy and chemotherapy
Bibliographic record
Abstract
BACKGROUND: Porphyrin-lipids are versatile building blocks that enable cancer theranostics and have been applied to create several multimodal nanoparticle platforms, including liposome-like porphysome (aqueous-core), porphyrin nanodroplet (liquefied gas-core), and ultrasmall porphyrin lipoproteins. Here, we used porphyrin-lipid to stabilize the water/oil interface to create porphyrin-lipid nanoemulsions with paclitaxel loaded in the oil core (PLNE-PTX), facilitating combination photodynamic therapy (PDT) and chemotherapy in one platform. RESULTS: PTX (3.1 wt%) and porphyrin (18.3 wt%) were loaded efficiently into PLNE-PTX, forming spherical core-shell nanoemulsions with a diameter of 120 nm. PLNE-PTX demonstrated stability in systemic delivery, resulting in high tumor accumulation (~ 5.4 ID %/g) in KB-tumor bearing mice. PLNE-PTX combination therapy inhibited tumor growth (78%) in an additive manner, compared with monotherapy PDT (44%) or chemotherapy (46%) 16 days post-treatment. Furthermore, a fourfold reduced PTX dose (1.8 mg PTX/kg) in PLNE-PTX combination therapy platform demonstrated superior therapeutic efficacy to Taxol at a dose of 7.2 mg PTX/kg, which can reduce side effects. Moreover, the intrinsic fluorescence of PLNE-PTX enabled real-time tracking of nanoparticles to the tumor, which can help inform treatment planning. CONCLUSION: PLNE-PTX combining PDT and chemotherapy in a single platform enables superior anti-tumor effects and holds potential to reduce side effects associated with monotherapy chemotherapy. The inherent imaging modality of PLNE-PTX enables real-time tracking and permits spatial and temporal regulation to improve cancer treatment.
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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".