Effect of Hypoxia on Cytochrome P450 Activity and Expression
Bibliographic record
Abstract
In the last three decades, numerous reports have shown that patients with chronic pulmonary disease and with heart failure with hypoxemia cleared drugs at a lower rate than healthy volunteers. As a consequence decreased clearance, drug toxicity is frequent in these patients. The reduction in drug clearance is due to a decrease in activity of cytochrome P450 isoforms, partly associated to the hypoxemia. With in vivo animal models, acute moderate hypoxia (PaO2 of around 35-50 mm Hg) reduces the clearance of drugs biotransformed by CYP1A1, CYP1A2, CYP2B6, CYP2C9, CYP2C19 and CYP2E1, although hypoxia does not affect the clearance of drugs biotransformed by CYP3A6. Ex vivo and in vitro experiments demonstrate that hypoxia down-regulates CYP1A1, CYP1A2, CYP2B6, CYP2C9 and CYP2C19, decrease preceded by a reduction in activity. On the other hand, acute moderate hypoxia up-regulates CYP3A6. The changes in protein expression are preceded by modifications in the mRNA coding for the proteins. The effect of hypoxia on hepatic cytochrome P450 is carried out by serum mediators, e.g. interferon-gamma, interleukin-1beta, and interleukin-2 are responsible for the decrease in activity and in expression of cytochrome P450 isoforms, and erythropoietin accounts for the increase in CYP3A6. Probably several mechanisms underlie and contribute to the decrease in activity and down-regulation of cytochrome P450 isoforms by hypoxia, e.g. reducing potentiation factors, inducing repressor elements and activating negative regulatory elements. The up-regulation of CYP3A6 implies a PTK- and p42/44MAPK-dependent stabilization/activation, nuclear translocation of HIF-1 and AP-1, binding to CYP3A6 promoter, and transactivation of the gene to induce CYP3A6 expression.
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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 | 0.000 | 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) | 0.002 | 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; 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".