<i>Update:</i> Peptide Motifs for Insertion of Radiolabeled Biomolecules into Cells and Routing to the Nucleus for Cancer Imaging or Radiotherapeutic Applications
Bibliographic record
Abstract
Intracellular compartments, in particular the cytoplasm or nucleus, have generally been poorly accessible or inaccessible to radiolabeled biomolecules (e.g., monoclonal antibodies [mAbs], peptides, or oligonucleotides [ODNs]). However, recently cell-penetrating peptides (CPPs) and nuclear localizing peptide sequences (NLSs) have been shown to have the capability of inserting biomolecules into cells and transporting them to the cell nucleus. This discovery now presents intriguing new opportunities to design radiopharmaceuticals that could potentially probe, through imaging, the expression of key intracellular or intranuclear regulatory proteins that define the tumor phenotype, predict outcome, or act as sensitive reporters of response or resistance to treatment. CPPs could also more efficiently internalize radiolabeled antisense ODNs or peptide nucleic acids (PNAs) into tumor cells to enhance the sensitivity of imaging gene expression at the mRNA level. Perhaps one of the most exciting new developments to emerge is the use of NLS to route mAbs and peptides conjugated to nanometer-micrometer range Auger-electron-emitting radionuclides (e.g., (111)In) to the nucleus of cancer cells following their receptor-mediated internalization. In the nucleus, these electrons are highly potent in causing lethal DNA strand breaks. In some cases, NLSs are present naturally in peptide growth factors or their receptors, where they function to deliver internalized ligands to the nucleus, or alternatively, they can be introduced synthetically. This update reviews the properties of CPPs and NLS and focuses on their use for inserting radiolabeled biomolecules into cancer cells for imaging or targeted Auger electron radiotherapy of malignancies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.030 |
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".