Peptide-Based Pharmacomodulation of a Cancer-Targeted Optical Imaging and Photodynamic Therapy Agent
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Bibliographic record
Abstract
We designed and synthesized a folate receptor-targeted, water-soluble, and pharmacomodulated photodynamic therapy (PDT) agent that selectively detects and destroys the targeted cancer cells while sparing normal tissue. This was achieved by minimizing the normal organ uptake (e.g., liver and spleen) and by discriminating between tumors with different levels of folate receptor (FR) expression. This construct (Pyro-peptide-Folate, PPF) is composed of three components: (1) pyropheophorbide a (Pyro) as an imaging and therapeutic agent, (2) peptide sequence as a stable linker and modulator improving the delivery efficiency, and (3) Folate as a homing molecule targeting FR-expressing cancer cells. We observed an enhanced accumulation of PPF in KB cancer cells (FR+) compared to HT 1080 cancer cells (FR-), resulting in a more effective post-PDT killing of KB cells over HT 1080 or normal CHO cells. The accumulation of PPF in KB cells can be up to 70% inhibited by an excess of free folic acid. The effect of Folate on preferential accumulation of PPF in KB tumors (KB vs HT 1080 tumors 2.5:1) was also confirmed in vivo. In contrast to that, no significant difference between the KB and HT 1080 tumor was observed in case of the untargeted probe (Pyro-peptide, PP), eliminating the potential influence of Pyro's own nonspecific affinity to cancer cells. More importantly, we found that incorporating a short peptide sequence considerably improved the delivery efficiency of the probe--a process we attributed to a possible peptide-based pharmacomodulation--as was demonstrated by a 50-fold reduction in PPF accumulation in liver and spleen when compared to a peptide-lacking probe (Pyro-K-Folate, PKF). This approach could potentially be generalized to improve the delivery efficiency of other targeted molecular imaging and photodynamic therapy agents.
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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) | 0.000 | 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 it