Optimization of a Macrocyclic Ghrelin Receptor Agonist (Part II)
Bibliographic record
Abstract
The initial lead optimization efforts on the foregoing macrocyclic ghrelin agonists from the high-throughput screening (HTS) hit, from a diverse library of macrocyclic peptidomimetics encompassing a tripeptide connected head to tail by a non-peptidic tether, to the initial clinical candidate, in terms of potency and pharmacokinetic (PK) properties, were reported previously. This chapter presents a summary of the efforts that culminated in the nomination of TZP-102 as the second clinical candidate from the ghrelin agonist program. Improving CYP 3A4 off-target profile in ulimorelin was one of the key goals in the second round of advanced lead optimization. In the lead optimization beyond ulimorelin that culminated in the discovery of TZP-102, the key features of CYP 3A4 were generally retained, and the main focus was directed to additional optimization through the AA3 side chain (more tolerant in terms of bioactivity structure-activity relationship (SAR)), as well as the less explored tether regions in the lead structure.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".