Effects of Various Selective Estrogen Receptor Modulators with or without Conjugated Estrogens on Mouse Mammary Gland
Bibliographic record
Abstract
Selective estrogen receptor modulators (SERMs) are small molecules that, depending on the end point measured, may either function as estrogen receptor (ER) agonists or antagonize estrogens' agonist activity. A key feature of SERMs is the inhibition of ER agonist action on the uterus and mammary gland, but the degree of antagonism varies among compounds and end points. Bazedoxifene is a SERM that is being clinically evaluated both as a monotherapy for the prevention and treatment of osteoporosis and in combination with conjugated estrogens (CEs) for the treatment of menopausal symptoms and prevention of osteoporosis. The studies reported here compare the relative ER agonist and antagonist effects of three pharmacologically distinct SERMs (bazedoxifene, raloxifene, and lasofoxifene) on the ovariectomized mouse when administered alone or as a tissue-selective estrogen complex, a term used to describe the partnering of a SERM and one or more estrogens. At the minimum dose required to maximally reduce CE-stimulated uterine wet weight increase for each SERM, the degree of inhibition varied among the SERMs, with a rank order of bazedoxifene approximately raloxifene > lasofoxifene, in which only bazedoxifene was statistically similar to vehicle. In the mammary gland, in which amphiregulin mRNA and morphological effects were measured, bazedoxifene generally exhibited less agonist activity and was a more effective antagonist of CE than raloxifene or lasofoxifene. In summary, in an animal model evaluating estrogen-modulated uterine effects and mammary gland development, bazedoxifene exhibited less ER agonist activity than raloxifene or lasofoxifene, and, as a tissue-selective estrogen complex, bazedoxifene/CE demonstrated less mammary gland stimulation than raloxifene/CE and lasofoxifene/CE.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".