Impact of estrogen population pharmacokinetics on a QSP model of mammary stem cell differentiation into myoepithelial cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<abstract> <p>Stem cell differentiation cascades are critical components of healthy tissue maintenance. Dysregulation in these systems can lead to serious diseases, including cancer. Myoepithelial mammary cells are produced from differentiated mammary stem cells in processes regulated, in part, by estrogen signalling and concentrations. To quantify and predict the production of mammary myoepithelial cell production by estrogen, we developed a mechanistic, quantitative systems pharmacology (QSP) model that includes the explicit characterization of free and unbound estrogen concentrations in circulation. Linking this model to a previously developed population pharmacokinetics model for ethinyl estradiol, a synthetic form of estrogen included in oral contraceptives, we predicted the effects of estrogen on myoepithelial cell development. Interestingly, pharmacokinetic intraindividual variability alone did not significantly impact on our modelos predictions, suggesting that combinations of physiological and pharmacokinetic variability drive heterogeneity in mechanistic QSP models. Our model is one component of an improved understanding of mammary myoepithelial cell production and development, and our results support the call for mechanistically constructed systems models for disease and pharmaceutical modelling.</p> </abstract>
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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 it