Photodynamic molecular beacon as an activatable photosensitizer based on protease-controlled singlet oxygen quenching and activation
Bibliographic record
Abstract
Molecular beacons are FRET-based target-activatable probes. They offer control of fluorescence emission in response to specific cancer targets, thus are useful tools for in vivo cancer imaging. Photodynamic therapy (PDT) is a cell-killing process by light activation of a photosensitizer (PS) in the presence of oxygen. The key cytotoxic agent is singlet oxygen ((1)O(2)). By combining these two principles (FRET and PDT), we have introduced a concept of photodynamic molecular beacons (PMB) for controlling the PS's ability to generate (1)O(2) and, ultimately, for controlling its PDT activity. The PMB comprises a disease-specific linker, a PS, and a (1)O(2) quencher, so that the PS's photoactivity is silenced until the linker interacts with a target molecule, such as a tumor-associated protease. Here, we report the full implementation of this concept by synthesizing a matrix metalloproteinase-7 (MMP7)-triggered PMB and achieving not only MMP7-triggered production of (1)O(2) in solution but also MMP7-mediated photodynamic cytotoxicity in cancer cells. Preliminary in vivo studies also reveal the MMP7-activated PDT efficacy of this PMB. This study validates the core principle of the PMB concept that selective PDT-induced cell death can be achieved by exerting precise control of the PS's ability to produce (1)O(2) by responding to specific cancer-associated biomarkers. Thus, PDT selectivity will no longer depend solely on how selectively the PS can be delivered to cancer cells. Rather, it will depend on how selective a biomarker is to cancer cells, and how selective the interaction of PMB is to this biomarker.
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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 | 0.001 |
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".