Developing a SERM: Stringent Preclinical Selection Criteria Leading to an Acceptable Candidate (WAY‐140424) for Clinical Evaluation
Bibliographic record
Abstract
Estrogens are represented by a diverse group of compounds. Within this large family of molecules are tissue-selective estrogens that have been classified as selective estrogen receptor modulators (SERMs). These compounds are characterized by the fact that they exhibit both estrogen agonist and antagonist activity dependent upon the gene promoter and target tissue being examined. SERMs have been intensively studied over the past decade, especially since one, raloxifene, has been approved for the prevention and treatment of postmenopausal osteoporosis. While not a replacement for hormone replacement therapy (HRT), raloxifene can be an alternative to it and other treatments for osteoporosis. The ideal SERM would provide the positive benefits associated with HRT without the uterine and breast stimulation. Raloxifene does achieve some of the benefits of HRT, specifically on the skeleton and lipid metabolism with no apparent uterine effects, and a potential decreased risk of developing breast cancer associated with raloxifene therapy. However, there are a number of parameters that can be improved. A number of SERMs have been evaluated only to fail in development due to, for the most part, uterine safety issues. In order to develop an improved SERM, a stringent screening process was designed to select compounds that did not stimulate the uterus or breast. At the same time, these new compounds would have a positive impact on the skeleton and lipid metabolism with the additional improvement (over raloxifene) of a neutral effect on hot flashes. Under these strict conditions, WAY-140424 was developed and, to date, the preclinical pharmacology data have accurately predicted the clinical response demonstrated in phase I and II trials.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".