Pharmaco- and toxicokinetics of selected exogenous and endogenous estrogens: A review of the data and identification of knowledge gaps
Bibliographic record
Abstract
Chemicals with estrogenic activity are derived from many different natural and synthetic processes and products, including endogenous production (e.g., estradiol, conjugated estrogens), drugs (e.g., ethinyl estradiol, conjugated estrogens), plants used as foods (phytoestrogens such as genistein, daidzein, S-equol), and man-made chemicals (xenoestrogens such as bisphenol A). Human exposure to low doses of endogenous estrogens, estrogenic drugs, phytoestrogens, and xenoestrogens has the potential to improve health or disrupt normal endocrine activity, as well as impact the diverse systems with which estrogens interact, including the cardiovascular system, and lipid and carbohydrate metabolism. Mechanisms of action and diversity of adverse and non-adverse effects following human exposure to low doses of estrogen active chemicals (EACs, defined as chemicals which interact with an estrogen receptor [ER]) are poorly understood. This review summarizes our current understanding of the pharmacological action with a focus on pharmacokinetics (PK) and toxicokinetics (TK) of several representative EACs in both physiological and pathological processes. The goal of this review is to assess the current state-of-the-science on: (i) the potential for EACs to interfere with endocrine activity, (ii) factors which contribute to endocrine-related clinical outcomes, and (iii) existing knowledge gaps. While classical PK approaches (compartmental or non-compartmental) can be used to characterize absorption, distribution, metabolism, and elimination of EACs, many of the detailed pharmacological characteristics necessary to understand benefit-risk balance have not yet been clarified. Pharmacological complexities mirror the complexity of determining whether and under what conditions exposure to estrogens in drugs, foods or to xenoestrogenic chemicals are beneficial or harmful to human health.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".