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 (CB2R), considered a clinically promising target for the control of brain damage in neurodegenerative disorders. Indeed, CB2R 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) CB2R ligands. The new compounds were obtained by connecting, through different linkers, the pharmacophoric portion of the CB2R positive allosteric modulator (PAM), EC21a, with that of the CB2R 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 CB2R 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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".