Pharmacology of human trace amine-associated receptors: Therapeutic opportunities and challenges
Bibliographic record
Abstract
The discovery in 2001 of a G protein-coupled receptor family, subsequently termed trace amine-associated receptors (TAAR), triggered a resurgence of interest in so-called trace amines. Initial optimism quickly faded, however, as the TAAR family presented a series of challenges preventing the use of standard medicinal chemistry and pharmacology technologies. Consequently the development of basic tools for probing TAAR and translating findings from model systems to humans has been problematic. Despite these challenges the last 5years have seen considerable advances, in particular with respect to TAAR1, which appears to function as an endogenous rheostat, maintaining central neurotransmission within defined physiological limits, in part through receptor heterodimerization yielding biased signaling outputs. Regulation of the dopaminergic system is particularly well understood and clinical testing of TAAR1 directed ligands for schizophrenia and psychiatric disorders have begun. In addition, pre-clinical animal models have identified TAAR1 as a novel target for drug addiction and metabolic disorders. Growing evidence also suggests a role for TAARs in regulating immune function. This review critically discusses the current state of TAAR research, highlighting recent developments and focussing on human TAARs, their functions, and clinical implications. Current gaps in knowledge are identified, along with the research reagents and translational tools still required for continued advancement of the field. Through this, a picture emerges of an exciting field on the cusp of significant developments, with the potential to identify new therapeutic leads for some of the major unmet medical needs in the areas of neuropsychiatry and metabolic disorders.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".