Transforming a Targeted Porphyrin Theranostic Agent into a PET Imaging Probe for Cancer
Bibliographic record
Abstract
Porphyrin based photosensitizers are useful agents for photodynamic therapy (PDT) and fluorescence imaging of cancer. Porphyrins are also excellent metal chelators forming highly stable metallo-complexes making them efficient delivery vehicles for radioisotopes. Here we investigated the possibility of incorporating (64)Cu into a porphyrin-peptide-folate (PPF) probe developed previously as folate receptor (FR) targeted fluorescent/PDT agent, and evaluated the potential of turning the resulting (64)Cu-PPF into a positron emission tomography (PET) probe for cancer imaging. Noninvasive PET imaging followed by radioassay evaluated the tumor accumulation, pharmacokinetics and biodistribution of (64)Cu-PPF. (64)Cu-PPF uptake in FR-positive tumors was visible on small-animal PET images with high tumor-to-muscle ratio (8.88 ± 3.60) observed after 24 h. Competitive blocking studies confirmed the FR-mediated tracer uptake by the tumor. The ease of efficient (64)Cu-radiolabeling of PPF while retaining its favorable biodistribution, pharmacokinetics and selective tumor uptake, provides a robust strategy to transform tumor-targeted porphyrin-based photosensitizers into PET imaging probes.
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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".