Tissue selective estrogen complex (TSEC): a review
Bibliographic record
Abstract
OBJECTIVE: This review describes historical development of selective estrogen receptor modulators (SERMs) and their combination with estrogens, termed a tissue selective estrogen complex (TSEC), and considers the potential for future TSEC development. METHODS: This narrative review is based on literature identified on PubMed and the TSEC research and development experience of the authors. RESULTS: SERMs have estrogenic and antiestrogenic effects in various tissues; however, no single agent has achieved an optimal balance of agonist and antagonist effects for the treatment of menopausal symptoms. Clinically, a number of SERMs protect against osteoporosis and breast cancer but can exacerbate vasomotor symptoms. Estrogens alleviate menopausal hot flushes and genitourinary symptoms as well as reduce bone loss, but the addition of a progestogen to menopausal hormone therapy to protect against endometrial cancer increases vaginal bleeding risk, breast tenderness, and potentially breast cancer. The search for an effective menopausal therapy with better tolerability led to the investigation of TSECs. Clinical development of a TSEC consisting of conjugated estrogens/bazedoxifene increased understanding of the importance of a careful consideration of the combination's components and their respective doses to balance safety and efficacy. Bazedoxifene is an estrogen receptor agonist in bone but an antagonist/degrader in the endometrium, which has contributed to its success as a TSEC component. Other oral TSEC combinations studied thus far have not demonstrated similar endometrial safety. CONCLUSIONS: Choice of SERM, selection of doses, and clinical trial data evaluating safety and efficacy are key to ensuring safety and adequate therapeutic effect of TSECs for addressing menopausal symptoms.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".