Dual mode fluorescent PET tracers: Efficient modular synthesis, facile 18F-radiolabelling, in vivo PET imaging and ex vivo fluorescence
Bibliographic record
Abstract
1161 Objectives A fluorescent PET probe that empowers seamless switching between PET and fluorescence imaging is highly desirable. Here we disclose a radiosynthetic strategy that amalgamates a dimeric peptide, a fluorophore, and an organotrifluoroborate prosthetic that affords 1-step aqueous 18F-labelling for the first time. This construct successfully provides fluorescent imaging for binding affinity assay, is labeled at high specific activity, and specifically targets the tumour in PET/optical imaging. Methods Rhodamine-BisRGD-BF3 (cpd 3) was simply synthesized in two steps using a copper-catalyzed click reaction from trisalkyne-BF3 (cpd 1). In terms of kit development, cpd 3 was aliquoted in quantities of 50 nmol for on-demand one-step labeling. The 18F-labeling was performed by 18F-19F isotope exchange. The tracer was obtained without HPLC purification. PET imaging and biodistribution were performed using anesthetized mice bearing U87M glioblastoma tumors. Results The synthesis of the cpd 3 from cpd 1 was achieved in reasonable chemical yield (~36%). 18F-labeled 3 (>200 mCi) was achieved in good radiochemical yield (25%, n=3) with >99% radiochemical purity. The specific activity was measured to be >111 GBq/µmol by a standard curve. 7.5% ID/g) detected at early time points slowly diminished to 5.6% ID/g at 120 mins post injection. Following PET scanning, fluorescent imaging was explored to visualize the tumour uptake at both high and low specific activities. Conclusions Here we successfully develop a new synthetic strategy for the facile and modular construction of dual-mode fluorescent/PET tracers. Given the broad applicability of click conjugations, synthon 1 is amenable to grafting various of peptides, as well as a broad range of fluorophores.
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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 | 0.001 |
| 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".