Preparation and evaluation of reagents for tagging amino and thiol groups with fluorous stannanes. A convenient method for producing radioiodinated compounds in high effective specific activity
Bibliographic record
Abstract
Abstract Building on the previously reported fluorous labeling strategy (FLS), a new approach for preparing molecular imaging and therapy agents derived from radioiodine in high purity without the need to employ HPLC was developed. A series of novel reagents containing a fluorous arylstannane, including fluorous benzaldehydes (1a/b) and an aryl‐iodoacetamide (2), were prepared so that the FLS could be used to label and purify targeting vectors that contain free amines or thiol groups. The reagents were conjugated to model amines and thiols in generally high yields (79–95%) under mild conditions. The fully characterized products were radiolabeled with Na[125I] in the presence of iodogen and the products were purified using fluorous solid‐phase extraction to yield the desired iodinated products in high yield (>83%) and high effective specific activity (ESA). The work reported creates a convenient and flexible means of preparing targeted molecular imaging and therapy agents derived from radioisotopes of iodine in high ESA. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".