Vascular-targeted photodynamic therapy with BF2-chelated Tetraaryl-Azadipyrromethene agents: a multi-modality molecular imaging approach to therapeutic assessment
Bibliographic record
Abstract
BACKGROUND: Photodynamic therapy (PDT) is a treatment modality for a range of diseases including cancer. The BF(2)-chelated tetraaryl-azadipyrromethenes (ADPMs) are an emerging class of non-porphyrin PDT agent, which have previously shown excellent photochemical and photophysical properties for therapeutic application. Herein, in vivo efficacy and mechanism of action studies have been completed for the lead agent, ADMP06. METHODS: A multi-modality imaging approach was employed to assess efficacy of treatment, as well as probe the mechanism of action of ADPM06-mediated PDT. RESULTS: Tumour ablation in 71% of animals bearing mammary tumours was achieved after delivery of 2 mg kg(-1) of ADPM06 followed immediately by light irradiation with 150 J cm(-2). The inherent fluorescence of ADPM06 was utilised to monitor organ biodistribution patterns, with fluorescence reaching baseline levels in all organs within 24 h. Mechanism of action studies were carried out using dynamic positron emission tomography and magnetic resonance imaging techniques, which, when taken together, indicated a decrease in tumour vascular perfusion and concomitant reduction in tumour metabolism over time after treatment. CONCLUSION: The encouraging treatment responses in vivo and vascular-targeting mechanism of action continue to indicate therapeutic benefit for this new class of photosensitiser.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".