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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".