Discovery of Ghrelin(1–8) Analogues with Improved Stability and Functional Activity for PET Imaging
Bibliographic record
Abstract
The highest affinity ghrelin-based analogue for fluorine-18 positron emission tomography, [Inp 1,Dpr 3 (6-FN),1Nal 4,Thr 8 ]ghrelin(1–8) amide ( 1 ), has remarkable subnanomolar receptor affinity (IC 50 = 0.11 nM) toward the growth hormone secretagogue receptor 1a (GHSR). However, initial in vivo PET imaging and biodistribution of [ 18 F]1 in mice demonstrated an unfavorable pharmacokinetic profile with rapid clearance and accumulation in liver and intestinal tissue, prompting concerns about the metabolic stability of this probe. The aims of the present study were to examine the proteolytic stability of ghrelin analogue 1 in the presence of blood and liver enzymes, structurally modify the peptide to improve stability without impeding the strong binding affinity, and measure the presently unknown functional activity of ghrelin(1–8) analogues. The in vitro stability and metabolite formation of 1 in human serum and liver S9 fraction revealed a metabolic soft spot between amino acids Leu 5 and Ser 6 in the peptide sequence. A focused library of ghrelin(1–8) analogues was synthesized and evaluated in a structure–activity–stability relationship study to further understand the structural importance of the residues at these positions in the context of stability and receptor affinity. The critical nature of l -stereochemistry at position 5 was identified and substitution of Ser 6 with l -2,3-diaminopropionic acid led to a novel ligand with substantially improved in vitro stability while maintaining subnanomolar GHSR affinity. Despite the highly modified nature of these analogues compared to human ghrelin, ghrelin(1–8) analogues were found to recruit all G protein subtypes (Gα q/11/13/i1/oB ) known to associate with GHSR as well as β-arrestins with low micromolar to nanomolar potencies. The study of these analogues demonstrates the ability to balance desirable ligand properties, including affinity, stability, and potency to produce well-rounded candidate molecules for further in vivo evaluation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".