The Discovery of Novel TAAR1 Ligands through the use of a Homology Model and <i>in silico</i> Screening
Bibliographic record
Abstract
Trace amines (TA) are by‐products of the synthesis of classical neurotransmitters within the brain. TA's exert their effect by activating a class of GPCRs including, t race a mine a ssociated r eceptor 1 (TAAR1) where TAAR1 signalling has been shown to be a negative regulator of dopamine transmission. In this study, we aimed to identify novel selective TAAR1 compounds for TAAR1 for in vivo testing. To this end, a TAAR1 homology model was generated and an in silico screen of 3 million commercially available compounds was conducted. The top 42 compounds, based on predicted affinities, were ordered and screened for TAAR1 activity using a cAMP BRET biosensor. From the 42 compounds tested, a mix of antagonists (seven compounds) and agonists (nine compounds) were found, yielding an initial hit rate of 35%. Of the nine identified agonists, three compounds (8, 16, and 25) displayed high potency for TAAR1 with EC 50 values of 18, 1.0, and 52 µM respectively. In order to identify compounds with improved affinity for TAAR1, five analogs of compounds 8 and 16 were tested. Of the five analogs of compound 8 tested, only one compound (8b) displayed agonist activity with no improvements on efficacy or potency compared to compound 8. Two of the five compound 16 analogs (16a and 16b) had a left shift of ~10 fold in their EC 50 for activation of cAMP signalling by TAAR. Furthermore the analog 16b had higher efficacy for TAAR1 activation than compound 16. In conclusion we have discovered three unique novel partial TAAR1 agonists with new chemical backbones where compound 16 and its analogs had the most optimal pharmacological profile. The in vivo effects as well as identification of higher affinity analogs of these compounds will be undertaken in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".