Characterization and development of a peptide (p160) with affinity for neuroblastoma cells.
Bibliographic record
Abstract
UNLABELLED: Drug-targeting strategies can increase the efficacy and reduce the side effects and toxicity of conventional chemotherapy or may lead to new radiolabeled molecules useful for diagnosis and therapy. To identify and characterize new carrier molecules, we evaluated a peptide that had been identified by phage display technology. METHODS: The peptide p160 (VPWMEPAYQRFL) was prepared by solid-phase peptide synthesis and radiolabeled with (125)I or (131)I. The radiolabeled peptide and derivatives of it were used to study binding and internalization in vitro and to assess their distribution in tumor-bearing mice. RESULTS: Cell-binding assays on the human neuroblastoma cell line WAC 2 indicated the affinity and specificity of (125)I-labeled p160 toward neuroblastoma cells. Binding of the (125)I-labeled p160 was inhibited up to 95% by the unlabeled peptide. Furthermore, 50% of the total bound activity was internalized into the neuroblastoma cells. Biodistribution studies on nude mice showed a higher tracer accumulation in tumors than in most organs. Perfusion of the animals reduced uptake in all tissues, whereas tumor uptake remained constant. Fluorescence-activated cell-sorting studies with fluorescein isothiocyanate-labeled p160 demonstrated an increased fluorescence signal. Investigation of the binding properties of the fragments p160-8-1, p160-8-2, and p160-8-3 indicated that the sequence EPAYQR might be of significance for the binding of p160. CONCLUSION: These data indicate that the p160 peptide is an attractive candidate for the development of a neuroblastoma-specific vector that can be used for drug targeting or radiopeptide-based diagnosis and 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".