Supramolecular Chemistry Enables Radiolabeling of Proteins under Mild Conditions
Bibliographic record
Abstract
Monoclonal antibodies have proven to be highly effective for in vivo targeting in nuclear medicine applications. However, typical radiolabeling methods, for PET/SPECT imaging and targeted radionuclide therapy, often require conditions that may affect the structural integrity of these fragile biomolecules. In this study, we introduce a novel methodology, based on supramolecular chemistry, for efficient radiolabeling of antibodies under mild conditions. Specifically, we report the synthesis of DOTA-cucurbit[7]uril (DOTA-CB[7]), a precursor that can be readily radiolabeled with a variety of radionuclides ( 111 In, 68 Ga, 89 Zr, 177 Lu, 225 Ac). [ 111 In]In-DOTA-CB[7] was conjugated to an anti-HER2 antibody (trastuzumab) site─specifically grafted with adamantan-1-amine groups. The supramolecular conjugation, driven by the exceptionally strong affinity between CB[7] and adamantan-1-amine enabled rapid and efficient formation of a stable antibody–radiometal complex under mild conditions. The resulting radioimmunoconjugate demonstrated high in vitro and in vivo stability and effective targeting of HER2+ tumors, as evidenced by SPECT/CT imaging in mice xenografted with HER2+ SKOV-3 cells. These findings highlight the potential of DOTA-CB[7] for supramolecular radiolabeling, offering a simple and versatile alternative to traditional covalent approaches, particularly for applications requiring compatibility with heat-sensitive biomolecules.
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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 | 0.001 |
| 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".