Nanoparticles for photodiagnosis and photodynamic therapy of malignant diseases
Bibliographic record
Abstract
Photodiagnosis as well as photodynamic therapy (PDT) are based on the use of a photosensitzer (PS) molecule that upon light illumination will either fluoresce or transfer energy to oxygen molecules to create reactive oxygen species that are highly cytotoxic. Since those molecules have a higher affinity towards malignant tissue, they can be used to either indicate the presence of cancer or to destroy it. The main problem with these techniques resides in the relative lack of specific uptake, resulting in false positive diagnosis, excessive tissue destruction and long standing cutaneous sensitization. Incorporation of PS into nanoparticle platforms aims to minimize those side effects, mainly due to the enhanced permeability retention effect and to enhance photodynamic activity due to monomerization of the active compound. Our group is actively investigating photophysical properties of different carrier systems as well as preclinical work to evaluate potential new diagnostic and therapeutic approaches. Liposomal formulations of mTHPC (Foscan®) have been investigated in collaboration with Biolitec AG. The PS was rapidly eliminated from the plasma and maximal therapeutic efficacy was observed when microscopic studies indicated presence of high drug doses in both endothelial cells and tumor cells. Intratumoral drug injections were tested in a model of breast cancer recurrence and revealed unusual features, namely progressive increase of fluorescence that was maximal 24 H after administration. This appeared to be due to a phenomenon of photoinduced fluorescence quenching due to energy transfer between PS molecules within the liposomes. We also developed a new approach for PDT, namely to apply illumination in order to prevent recurrence of bladder cancer following fluorescence guided transurethral resection. In order to avoid excessive photobleaching of the drug, we are currently investigating dendrimerized photosensitizers in collaboration with the University College of London. Those dendrimers appear to have a much higher affinity towards bladder cancer in vivo and to provide a sustained release and uptake of the active component. A third axis of our research is to visualize sentinel lymph nodes with quantum dots. Together with the EPCI (Industrial Physico-Chemical High School, Paris), we have defined the coating that offers the highest lymphotropicity in a newly developed model of metastatic breast cancer. We are currently investigating a new imaging tool to detect thoracic lymph nodes containing NIR emitting quantum dots
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".