REGULATION OF BETA-ADRENOCEPTORS ACTIVITYUSING SYNTHETIC LIGHT-REGULATED MOLECULES
Bibliographic record
Abstract
Beta-adrenoceptors (ß-AR) are prototypical G proteincoupled receptors and important pharmacological targets for many diseases. Indeed, a number of approved drugs target these receptors due to their key role on many physiological functions. Among other examples, we encounter ß1-AR antagonists (ß- Blockers), which constitute the first-line therapy for the treatment of heart diseases, and ß2-AR agonists, which act as bronchodilators for the treatment of breathing pathologies. Considering the relevance of these receptors, achieving a reversible and localised control of their activity would provide a powerful tool, both for its research applications and its clinical potential. In this context, photopharmacology arises as a potent approach. Photopharmacology is an emerging field based on the use of synthetic light-regulated molecules to allow reversible spatiotemporal control of target receptors in native tissues. These ligands have the potential to provide a precise and controllable therapeutic action with increased efficacy and reduced side effects. Moreover, the fine regulation on demand of the receptor activation state is of great interest for their study in non-modified cells, tissues and organisms. The present project provides the first proof of concept for beta-adrenoceptor photopharmacology. We first designed and synthesised libraries of lightregulated compounds in order to regulate ß-AR activity with spatiotemporal precision. Subsequent testing highlighted the successful development of compounds with promising pharmacological properties which can be reversibly and irreversibly controlled by light. The discovered molecules enable a fine control of ß-AR in their native environment that will certainly open the door to innovative research procedures and may inspire future personalized therapies targeting these receptors.
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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 | 0.001 |
| 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".