A Decade’s Progress in the Development of Molecular Imaging Agents Targeting the Growth Hormone Secretagogue Receptor
Bibliographic record
Abstract
The growth hormone secretagogue receptor 1a (GHSR), also called the ghrelin receptor, is a G protein-coupled receptor known to play an important metabolic role in the regulation of various physiological processes, including energy expenditure, growth hormone secretion, and cell proliferation. This receptor has been implicated in numerous health issues including obesity, gastrointestinal disorders, type II diabetes, and regulation of body weight in patients with Prader-Willi syndrome, and there has been growing interest in studying its mechanism of behavior to unlock further applications of GHSR-targeted therapeutics. In addition, the GHSR is expressed in various types of cancer including prostate, breast, and testicular cancers, while aberrant expression has been reported in cardiac disease. Targeted molecular imaging of the GHSR could provide insights into its role in biological processes related to these disease states. Over the past decade, imaging probes targeting this receptor have been discovered for the imaging modalities PET, SPECT, and optical imaging. High-affinity analogues of ghrelin, the endogenous ligand for the GHSR, as well as small molecule inhibitors have been developed and evaluated both in vitro and in pre-clinical models. This review provides a comprehensive overview of the molecular imaging agents targeting the GHSR reported to the end of 2019.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".