Palladium-Mediated <i>S</i>-Arylation of Cysteine Residues with 4-[<sup>18</sup>F]Fluoroiodobenzene ([<sup>18</sup>F]FIB)
Bibliographic record
Abstract
Transition-metal-mediated bioconjugation chemistry has been used extensively to design and synthesize molecular probes to visualize, characterize, and quantify biological processes within intact living organisms at the cellular and subcellular levels. We demonstrate the development and validation of chemoselective [ 18 F]fluoro-arylation chemistry of cysteine residues using Pd-mediated S -arylation chemistry with 4-[ 18 F]fluoroiodobenzene ([ 18 F]FIB) as an aryl electrophile. The novel bioconjugation technique proceeded in excellent radiochemical yields of 73–96% within 15 min under ambient and aqueous reaction mixture conditions, representing a versatile novel tool for decorating peptides and peptidomimetics with short-lived positron emitter 18 F. The chemoselective S -arylation of several peptides and peptidomimetics containing multiple reactive functional groups confirmed the versatility and functional group compatibility. The synthesis and radiolabeling of a novel prostate-specific membrane antigen (PSMA) binding radioligand [ 18 F]6 was accomplished using the novel labeling protocol. The validation of radioligand [ 18 F]6 in a preclinical prostate cancer model with PET resulted in favorable accumulation and retention in PSMA-expressing LNCaP tumors. At the same time, a significantly lower salivary gland uptake was observed compared to clinical PSMA radioligand [ 18 F]PSMA-1007. This finding coincides with ongoing discussions about the molecular basis of the off-target accumulation of PSMA radioligands currently used for clinical imaging and therapy of prostate cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".