Design, synthesis and biological evaluation of novel orthosteric-allosteric ligands of the cannabinoid receptor type 2 (CB2R)
Bibliographic record
Abstract
It is well known that G protein–coupled receptors (GPCRs) assume multiple active states. Orthosteric ligands and/or allosteric modulators can preferentially stabilize specific conformations, giving rise to pathway-biased signaling. One of the most promising strategies to expand the repertoire of signaling-selective GPCR activators consists of dualsteric agents, which are hybrid compounds consisting of orthosteric and allosteric pharmacophoric units. This approach proved to be very promising showing several advantages over monovalent targeting strategies, including an increased affinity or selectivity, a bias in signaling pathway activation, reduced off-target activity and therapeutic resistance. Our study focused on the cannabinoid receptor type 2 (CB 2 R), considered a clinically promising target for the control of brain damage in neurodegenerative disorders. Indeed, CB 2 R was found highly expressed in microglial cells, astrocytes, and even in some neuron subpopulations. Here, we describe the design, synthesis, and biological evaluation of two new classes of potential dualsteric (bitopic) CB 2 R ligands. The new compounds were obtained by connecting, through different linkers, the pharmacophoric portion of the CB 2 R positive allosteric modulator (PAM), EC21a , with that of the CB 2 R selective orthosteric agonist LV62 , both developed in our laboratories. A preliminary screening enabled us to identify compound JR64a as the most promising of the series. Indeed, functional examination highlighted a signaling ‘bias’ in favor of G protein activation over βarrestin2 recruitment, combined with high affinity for CB 2 R and the ability to efficiently prevent inflammation in human microglial cells (HMC3) exposed to LPS/TNFα stimulation, thus demonstrating great promise for the treatment of neurodegenerative diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".