I-123 labeled DAPTA peptide targeting chemokine receptor CCR5 as a potential inflammation imaging agent
Bibliographic record
Abstract
1076 Objectives Chemokines receptors (CR) are involved in the process of inflammatory diseases. Radiolabeled CR antagonists can be used to image inflammation. Our goal is to develop an 123I labeled DAPTA peptide targeting CR CCR5, and to evaluate its potential as an inflammation imaging tracer using an atherosclerosis model. Methods DAPTA was radiolabeled with 123I using iodogen. Cold iodine compound was characterized by mass spectroscopy. Biodistribution was determined in C57BL/6 mice. Cell uptake studies were conducted in: a) primary spleen cells and HEK-293 cells; b) U87-CD4-CCR5 and U87-MG cells. The tracer was injected into: 1) 9 month old ApoE-/- mice with normal diet; 2) 4 month old ApoE-/- mice with high fat Western diet (HFD); 3) 4 month old C57BL/6 mice as controls. The mice were sacrificed at 2 hr p.i.. Aortas were dissected and autoradiography images were collected and compared with en face and Oil Red O images. The lesion uptake (%IDxkg/m2) in autoradiography images were quantified using standards. Results DAPTA was radiolabeled with 123I with high radiochemical purity (> 95%) and specific activity (980 mCi/µmol). The identification of the tracer was confirmed by HPLC and mass spectroscopy of the cold analog. Biodistribution showed high blood uptake, indicating slow clearance of the tracer. In vivo de-iodination resulted in high stomach and thyroid uptake.Cell uptake studies showed: i) the uptake in primary spleen cells was significantly higher than the negative control HEK-293 cells (ratio: 2.3 at 1 hr and 2.5 at 2 hr incubatio); ii) U87-CD4-CCR5 cells had ~ 1.4 fold higher uptake than the control U87-MG cells. Autoradiography showed that the lesion uptake of 4 month ApoE-/- HFD mice (0.561 ± 0.079%) was lower than the uptake of 9 month ApoE-/- mice (0.747 ± 0.073%), and ~1.6 fold higher than the control mice (0.343 ± 0.069%). Conclusions We have developed 123I-DAPTA targeting CCR5. The preliminary in vitro and ex vivo evaluation indicates its potential application as a SPECT tracer for imaging inflammation.
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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) | 0.000 | 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".