A new 18F-RBF3 radioprosthetic for one step aqueous labeling of peptides and other ligands: meeting the challenge of radiosynthetic ease and preclinical impact i.e. high specific activity and high T:NT ratios
Bibliographic record
Abstract
223 Objectives To validate a new RBF3 for one-step 18F-labeling of complex bioactive tracers that includes: 1) no azeotropic drying, 2) use of Curie-levels of NCA 18F-fluoride, 3) rapidity e.g. 15-20 min, 4) no HPLC purification, 5) use of micrograms of precursor, 6) good-to-excellent tumor uptake, 7) very high T:NT ratios, 8) no bone uptake, 9) general applicability to several complex peptides. Methods We designed a novel zwitterionic organotrifluoroborate that is click conjugated to various peptides.[1,2] For labeling, we have identified QMA resins that enable direct elution of ~1Ci of NCA 18F-fluoride into Results RCYs are routinely >25% (n>70). Standard curve analysis shows SA9s are >3 Ci/μmol. QC radio/UV HPLC traces show purity >98%. Tumor:non-tumor ratios (T:M and T:Bone) are typically >20 and often exceptionally high (>75). T:Blood ratios are >6, and often much higher. Conclusions Bioconjugates with a novel organotrifluoroborate are eaily synthesized and stocked in aliquots of 3 Ci/μmol) and excellent purity. HPLC purification is obviated. The synthetic ease for coupling this new RBF3 to peptides to create stockable precursors, and more importantly, the exceptional radiosynthetic ease and the resulting high contrast images demonstrate that this method will be of potentially great utility to many applications. Research Support Genome BC, CIHR
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".