Potentiation of Porphysome Photodynamic Therapy via an Unconventional Membrane Fluidization Strategy
Bibliographic record
Abstract
Photodynamic therapy (PDT) is a cancer treatment modality that combines photosensitizers and light to generate reactive oxygen species for targeted tumor destruction. Porphysomes, a multifunctional liposomal-like nanoparticle, are designed to deliver high concentrations of porphyrin photosensitizers for various theranostic applications, including PDT. However, their limited cellular uptake has hindered their full therapeutic potential. While conventional receptor-targeting strategies have been explored to enhance the uptake of porphysomes, this thesis introduces a receptor-independent approach using EDTA-mediated membrane fluidization to improve porphysome and liposomal nanoparticle delivery in cancer cells. Chapters 1 and 2 outline the motivation of this thesis and provide an overview of the current state of the field. Chapter 3 of this thesis evaluates the in vitro and in vivo characteristics of EDTA-porphysomes, demonstrating enhanced cellular uptake, tumor fluorescence activation, and improved intratumoral distribution compared to parental porphysomes. Chapter 4 investigates the mechanism of uptake enhancement and its applicability to other pre-clinical and clinical liposome formulations. The incorporation of EDTA-lipids into the lipid bilayer induces membrane fluidization without affecting membrane integrity, leading to active nanoparticle uptake rather than passive. Chapter 5 assesses the in vivo PDT efficacy of EDTA-porphysomes in immunocompromised tumor-bearing mice and immunocompetent CT-26 colorectal tumor models. EDTA-porphysomes achieved superior PDT efficacy, high tumor ablation rates, and induced a specific anti-tumor immune response following a single PDT treatment without immunostimulants. This led to long-term immune protection, preventing recurrence upon tumor rechallenge 90 days post-treatment. Finally, Chapter 6 explores the future clinical potential of EDTA-porphysomes, including combination with surgery, drug delivery, and PDT-induced anti-tumor immunity for preventing recurrence and metastasis. Overall, this thesis describes the development and validation of EDTA-porphysome as an effective agent for PDT and explores the application of EDTA-mediated membrane fluidization to enhance liposomal drug delivery for improved cancer therapy.
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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".