Nanophotosensitizers for cancer therapy: a promising technology?
Bibliographic record
Abstract
Abstract Photodynamic therapy (PDT) has been clinically applied to cure various diseases including cancer. Indeed, photophrin (porfimer sodium, Axcan Pharma, Montreal, Canada), a heterogenous mixture of porphyrins, was the first photosensitizer (PS) approved for the treatment of human bladder cancer in 1993 in Canada. Over the past 10 years the use of PDT in the treatment of benign and malignant lesions has increased dramatically. However, PDT is still considered as an adjuvant strategy due to its limitations, primarily including low tissue penetration by light and inaccurate lesion selectivity by the PSs. To overcome this scenario, new technologies and approaches including nanotechnology have been incorporated into the concept of PS formulations as PS delivery systems, as PSs per se or as energy transducers. The ideal nanophotosensitizer (NPS) for cancer therapy should possess the following characteristics: biocompatibility and biodegradability without toxicity, stability in physiological conditions, tumor specific targeting, strong near infrared absorption for efficient and sufficient light absorbance and large singlet oxygen quantum yield for PDT. To fulfill these requirements, several nanoscale delivery platforms and materials have been developed. In this review we will focus on the state of the art of nanotechnology contributions to the optimization of PDT as a therapeutic alternative to fight against cancer. For this purpose we will start from the basic concepts of PDT, discuss the versatility in terms of NPS formulations and how to tackle the deficiencies of the current therapy. We also give our critical view and suggest recommendations for improving future research on this area.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".