Radiolabeling and Preliminary In Vivo Evaluation of the Candidate CCR2 Targeting PET Radioligand [11C]AZD2423
Bibliographic record
Abstract
Background: AZD2423 is a high-affinity and selective negative allosteric modulator of the chemokine receptor type 2 (CCR2). This receptor plays important roles in the extravasation and transmigration of monocytes under inflammatory conditions. The aims of the current positron emission tomography (PET) study were as follows: (i) to develop an efficient synthetic method for labeling AZD2423 with carbon-11 (11C, t1/2 = 20.4 min) and (ii) to evaluate its potential to visualize CCR2 binding in the non-human primate (NHP) brain. Methods: [11C]AZD2423 was synthesized using a novel two-step, two-pot [11C]carbon monoxide carbonylation procedure. PET imaging studies in NHPs (n = 2) were conducted to assess its brain penetration and in vivo distribution. Results: Radiolabeling of [11C]AZD2423 was accomplished with good yield (7.4 ± 0.6%, n = 4) and high radiochemical purity (>99%) using [11C]carbon monoxide. Preliminary PET imaging in NHPs revealed low [11C]AZD2423 brain exposure under both baseline and pretreatment conditions (SUVpeak = 0.4, n = 2). However, high concentrations of radioactivity were observed in organs outside the brain at baseline, e.g., the thyroid gland (SUVpeak = 3.3, n = 2), parotid gland (SUVpeak = 3.4, n = 2), and submandibular gland (SUVpeak = 4.4, n = 2). This radioactivity was markedly reduced following pretreatment with AZD2423 (3.0 mg/kg), indicating specific binding of [11C]AZD2423 to CCR2 in vivo. The presence of specific CCR2 binding was further validated using two-tissue compartment modeling, which demonstrated a 59–63% reduction in the total volume of distribution values in the analyzed peripheral tissues. Conclusions: Altogether, [11C]AZD2423 shows potential as a PET radioligand for the in vivo visualization of CCR2 expression in tissues outside the brain and may also serve as a lead compound for the further development of a CCR2 PET radioligand suitable for brain imaging.
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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 | 0.000 | 0.000 |
| 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".