The G protein-coupled receptors: Pharmacogenetics and Disease
Bibliographic record
Abstract
Genetic variation in G-protein coupled receptors (GPCRs) is associated with a wide spectrum of disease phenotypes and predispositions that are of special significance because they are the targets of therapeutic agents. Each variant provides an opportunity to understand receptor function that complements a plethora of available in vitro data elucidating the pharmacology of the GPCRs. For example, discrete portions of the proximal tail of the dopamine D1 receptor have been discovered, in vitro, that may be involved in desensitization, recycling and trafficking. Similar in vitro strategies have been used to elucidate naturally occurring GPCR mutations. Inactive, over-active or constitutively active receptors have been identified by changes in ligand binding, G-protein coupling, receptor desensitization and receptor recycling. Selected examples reviewed include those disorders resulting from mutations in rhodopsin, thyrotropin, luteinizing hormone, vasopressin and angiotensin receptors. By comparison, the recurrent pharmacogenetic variants are more likely to result in an altered predisposition to complex disease in the population. These common variants may affect receptor sequence without intrinsic phenotype change or spontaneous induction of disease and yet result in significant alteration in drug efficacy. These pharmacogenetic phenomena will be reviewed with respect to a limited sampling of GPCR systems including the orexin/hypocretin system, the beta2 adrenergic receptors, the cysteinyl leukotriene receptors and the calcium-sensing receptor. These developments will be discussed with respect to strategies for drug discovery that take into account the potential for the development of drugs targeted at mutated and wild-type proteins.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".