A Modular Supramolecular Peptide Platform Reveals Atomic-Number-Dependent Mechanisms Driving Radioenhancement
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Optimizing radioenhancer design for cancer therapy has been limited by inconsistent metal comparisons and unclear nanoscale mechanisms. High-Z nanoparticles are expected to enhance radiation effects through increased photoelectric absorption and secondary electron production, with the common assumption that radioenhancement efficacy increases uniformly with atomic number. However, this linear relationship may be oversimplified. Here, we introduce a versatile, supramolecular peptide platform enabling direct and standardized comparison of gadolinium (Gd), bismuth (Bi), and hafnium (Hf) as radioenhancers within a single, biologically targeted framework. This system is based on autoassembled peptide heterodimers (E3-K3) incorporating a flexible chelator (DOTAGA) and variable heavy-chain antibody (VHH) domains, ensuring uniform cellular uptake and precise tumor targeting. Systematic in vitro and in vivo analyses across HER2+ breast cancer and disseminated multiple myeloma models demonstrate that radioenhancement efficacy correlates with atomic number but not in a simple linear fashion, with physicochemical properties of each metal determining biological outcomes such as DNA damage induction, reactive oxygen species generation, and clonogenic survival reduction. Specifically, Gd- and Bi-loaded formulations significantly enhanced tumor control under external beam radiotherapy, with Bi exhibiting superior efficacy, while Gd-based constructs facilitated MRI-guided radioligand therapy. Our study elucidates fundamental physical mechanisms governing metal-dependent radioenhancement at the nanoscale but also establishes a broadly applicable theranostic approach with significant translational implications for personalized radiation oncology.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".