Bibliographic record
Abstract
Abstract Sex differences in drug metabolism are recognized as a major determinant of sex differences in pharmacokinetics and an important contributor to interindividual differences in drug metabolism and, in some cases, drug action. Sex differences in human drug metabolism have also been observed with a variety of drugs. The biochemical basis of sex differences in hepatic drug metabolism was largely unknown until the 1980s when it was discovered that the expression of many drug‐metabolizing enzymes, including several members of the cytochrome P450 (CYP) superfamily, is sex dependent in rat and mouse liver. Well‐studied examples include rat enzymes CYP2C11, which is male specific in its expression, and CYP2C12 (female specific) and CYP2A1 (female predominant). The major gonadal hormones, testosterone and estradiol, regulate the sex‐dependent expression of rat hepatic CYP enzymes indirectly, through their effects on the hypothalamus and its regulation of the sexually dimorphic, ultradian rhythm of pituitary growth hormone (GH) secretion, which ultimately dictates the sex‐dependent patterns of expression of these CYPs and certain other drug‐metabolizing enzymes. In male rats, the intermittent, pulsatile pattern of GH secretion stimulates the expression of male‐specific liver enzymes, whereas the more continuous pattern of GH release in females suppresses the expression male‐specific enzymes while inducing female‐specific enzymes. The current view is that the transcription factors STAT5b and HNF4α coordinately regulate the action of GH and its resultant effect on the sex‐dependent expression of hepatic CYP genes and thus provide a molecular basis for sex differences in drug metabolism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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) | 0.015 | 0.003 |
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".