Differential Biochemical and Cellular Actions of Premarin Estrogens: Distinct Pharmacology of Bazedoxifene-Conjugated Estrogens Combination
Bibliographic record
Abstract
The use of estrogen-based therapies and the selective estrogen receptor (ER) modulator (SERM), raloxifene, which are approved for postmenopausal osteoporosis, is associated with side effects such as uterine/breast hyperproliferation, thromboembolism, and hot flashes. A combination of a new SERM, bazedoxifene (BZA), and Premarin (conjugated estrogens; CE) is under investigation to mitigate the estrogen/SERM side effects with promising results in Phase III clinical trials. To explore the mechanism of BZA/CE action, we investigated the recruitment of cofactor peptides to ERalpha by components of CE and a mixture containing the 10 major components of CE with or without three different SERMs. Here, we demonstrate differential recruitment of cofactor peptides to ERalpha by the individual CE components using a multiplex nuclear receptor-cofactor peptide interaction assay. We show that estrone and equilin are partial agonists in comparison with 17beta-estradiol in recruiting cofactor peptides to ERalpha. Further, CE was more potent than 17beta-estradiol in mediating ERalpha interaction with cofactor peptides. Interestingly, BZA was less potent than other SERMs in antagonizing the CE-mediated cofactor peptide recruitment to ERalpha. Finally, in accordance with these biochemical findings, 17beta-estradiol and CE, as well as SERM/CE combinations, showed differential gene regulation patterns in MCF-7 cells. In addition, BZA showed antagonism of a unique set of CE-regulated genes and did not down-regulate the expression of a number of CE-regulated genes, the expression of which was effectively antagonized by the other two SERMs. These results indicate that SERMs in combination with CE exhibit differential pharmacology, and therefore, combinations of other SERMs and estrogen preparations may not yield the same beneficial effects that are observed in clinic by pairing BZA with CE.
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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".