Development of cannabinoid receptor (CB 2 R) ligands for application in PET studies - where to attach the radiolabel?
Bibliographic record
Abstract
The cannabinoid receptors type 2 (CB2R) are involved in many physiological processes but their expression level in healthy and diseased brain has not been unravelled. With positron emission tomography (PET) it is possible to monitor quantitatively very low amounts of compounds labelled with positron emitting isotopes like 18F in living organisms at high spatial resolution. For application in clinical research, such radiotracers have to show high selectivity and affinity to the target protein. A series of fluorinated N-carbazolyl-oxadiazolyl-propionamides [1] was synthesised and the affinity towards the human CB2R was measured in receptor binding studies. Here, we combine our CB2R receptor model with 3D-QSAR data [2] to support molecular docking studies employing the MOE software (Version 2012.12 Chemical Computing Group Inc. Montreal. http://www.chemcomp.com). The studies revealed that both the primarily investigated compound 2 and the 2-fluoroethyl substituted carbazole derivative 1 (Ki = 3.6 nM) fits well into the binding pocket. Attachment of the fluorine at different positions of the structure does not lead to significantly different poses in accordance with the experimental data. Organ distribution studies on CD1-mice verified our prediction, [3] that [18F]1 and [18F]2 can cross the blood-brain barrier. Figure 1 Compounds 1 and 2 fitted into the binding pocket of the CB2R receptor model.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 0.002 |
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".