Long‐Circulating Prostate‐Specific Membrane Antigen‐Targeted NIR Phototheranostic Agent
Bibliographic record
Abstract
Targeted photodynamic therapy (PDT) combined with image-guided surgical resection is a promising strategy for precision cancer treatment. Prostate-specific membrane antigen (PSMA) is an attractive target due to its pronounced overexpression in a variety of tumors, most notably in prostate cancer. Recently, we reported a pyropheophorbide-based PSMA-targeted agent, which exhibited long plasma circulation time and effective tumor accumulation. To further advance PSMA-targeted photodynamic therapy by harvesting tissue-penetrating properties of the NIR light, we developed a bacteriochlorophyll-based PSMA-targeted photosensitizer (BPP), consisting of three building blocks: (1) a PSMA-affinity ligand, (2) a peptide linker to prolong plasma circulation time and (3) a bacteriochlorophyll photosensitizer for NIR fluorescence imaging and photodynamic therapy (Qy absorption maximum at 750 nm). BPP exhibited excellent PSMA-targeting selectivity in both subcutaneous and orthotopic mouse models. The nine D-peptide linker in BPP structure prolonged its plasma circulation time (12.65 h). Favorable pharmacokinetic properties combined with excellent targeting selectivity enabled effective BPP tumor accumulation, which led to effective PDT in a subcutaneous prostate adenocarcinoma mouse model. Overall, bright NIR fluorescence of BPP enables effective image guidance for surgical resection, while the combination of its targeting capabilities and PDT activity allows for potent and precise image-guided photodynamic treatment of PSMA-expressing tumors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".