PACE4-Based Molecular Targeting of Prostate Cancer Using an Engineered 64Cu-Radiolabeled Peptide Inhibitor
Bibliographic record
Abstract
The potential of PACE4 as a pharmacological target in prostate cancer has been demonstrated as this proprotein convertase is strongly overexpressed in human prostate cancer tissues and its inhibition, using molecular or pharmacological approaches, results in reduced cell proliferation and tumor progression in mouse tumor xenograft models. We developed a PACE4 high-affinity peptide inhibitor, namely, the multi-leucine (ML), and sought to determine whether this peptide could be exploited for the targeting of prostate cancer for diagnostic or molecular imaging purposes. We conjugated a bifunctional chelator 1,4,7-triazacyclononane-1,4,7- triacetic acid (NOTA) to the ML peptide for copper-64 ((64)Cu) labeling and positron emission tomography (PET)- based prostate cancer detection. Enzyme kinetic assays against recombinant PACE4 showed that the NOTA-modified ML peptide displays identical inhibitory properties compared to the unmodified peptide. In vivo biodistribution of the (64)Cu/NOTA-ML peptide evaluated in athymic nude mice bearing xenografts of two human prostate carcinoma cell lines showed a rapid and high uptake in PACE4-expressing LNCaP tumor at an early time point and in PACE4-rich organs. Co-injection of unlabeled peptide confirmed that tumor uptake was target-specific. PACE4-negative tumors displayed no tracer uptake 15 minutes after injection, while the kidneys, demonstrated high uptake due to rapid renal clearance of the peptide. The present study supports the feasibility of using a (64)Cu/NOTA-ML peptide for PACE4-targeted prostate cancer detection and PACE4 status determination by PET imaging but also provides evidence that ML inhibitor-based drugs would readily reach tumor sites under in vivo conditions for pharmacological intervention or targeted radiation therapy.
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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) | 0.000 | 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 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".