Synthesis of azabicycloalkanone amino acid and azapeptide mimics and their application as modulators of the prostaglandin F2α receptor for delaying preterm birth
Bibliographic record
Abstract
Premature birth (<37 weeks gestation) is the major cause of perinatal mortality and morbidity and has been steadily increasing worldwide. Towards the rational design of more effective therapeutic agents for inhibiting uterine contractions and prolonging gestation (a so-called tocolytic drug), our team has targeted the prostaglandin F2α receptor (FP) employing a peptidomimetic approach designed to provide modulators of this novel target. We identified first a lead peptide (PDC113) (1) based on the sequence of the second extracellular loop of FP on the basis that the loop itself might modulate receptor activation. Systematic study of the structure−activity relationships of 1 generated hypotheses concerning the conformation and side-chains responsible for activity that led to the synthesis of PDC113.31 (2), a potent all d-amino acid peptide, which has successfully completed Phase 1b clinical trials. Employing indolizidinone amino acids, peptide mimics were developed that served to probe the mechanism of FP modulation. For example, PDC113.824 (9) was shown to allosterically regulate FP activity contingent on the presence of prostaglandin F2α by a mechanism implicating biased signalling. Although attempts to understand the turn geometry responsible for the activity of 9 by replacement of its indolizidin-2-one moiety with other azabicycloalkanones failed to produce biologically active analogs, employment of aza-aminoacyl-proline analogs resulted in a series of FP modulators exhibiting distinct effects on different G protein-mediated signalling pathways. Our program has thus contributed novel probes for understanding the chemical biology of FP as well as new therapeutic agents with promise for inhibiting uterine contractions and preventing preterm birth.
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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 | 0.001 |
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".