A new strategy for labeling proteins using oil-in-water emulsions. Single step preparation of Tc-99m radiolabeled insulin
Bibliographic record
Abstract
1507 Objectives Radiolabeling peptide hormones and proteins with radiometals like Tc-99m often requires multi-step indirect labeling methods because direct labeling can result in complex mixtures of products requiring extensive chromatographic purification. The objective was to develop a cost effective and efficient strategy to directly label proteins and peptide hormones using oil-in-water emulsions and insulin as the targeting vector. Insulin-derived nuclear probes are potentially valuable tools to evaluate the distribution and metabolism of insulin in vivo including dysregulation associated with diseases like diabetes, hypertension, and breast cancer. Methods Two new insulin derivatives bearing single amino acid chelates (SAACs) linked to the B1 residue of insulin through different PEG chains were prepared. The rhenium analogues were synthesized in parallel and assessed for in vitro binding to MCF-7 breast cancer cells using an I-125 insulin competition assay. The insulin derivatives were labeled with [99mTc(CO)3]+ using an oil-in-water emulsion and the products isolated by centrifugation. Results The rhenium standards were essentially indistinguishable from insulin in the competition assay and gave similar IC50 values of 4.8 nM and 7.4 nM for PEG3-SAAC II-insulin and PEG8-SAAC II-insulin, respectively. The radiolabeling procedure produced a single product in quantitative yield at room temperature in 60 min. These results are far superior to the yields obtained when performing direct solution phase labeling ( Conclusions A new paradigm for labeling targeting vectors with technetium has been developed and used to prepare 99mTc-labeled insulin in a single step. The product appears functionally indistinguishable from native insulin in vitro. The general applicability of the labeling procedure and potential uses of the Tc-insulin probes for the evaluation of insulin biochemistry in vivo will be discussed
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".