The ability of polycyclic aromatic hydrocarbons to alter physiological factors underlying drug disposition
Bibliographic record
Abstract
As part of everyday life, people are exposed to polycyclic aromatic hydrocarbons (PAHs). Sources of PAHs include cigarette smoke, ingestion of contaminated food and water or specifically charcoal-grilled meat, and occupational exposure (e.g., the coal industry). PAH compounds are well known to have enzyme-inducing effects, especially on the cytochrome P450 (CYP) family of enzymes, including CYP1A. Enhanced clearance of CYP1A-metabolized drugs as a result of PAH exposure is well established. However, there are examples where PAH-containing sources enhanced the clearance or altered the disposition of some non-CYP1A-metabolized drugs. It has been shown that not only do these compounds induce CYP1A isoforms, but they also can alter the expression of other CYPs, such as 1B1/2 and 2E1, certain phase II enzymes, some transport proteins (in animal models and cell lines), levels of plasma proteins (e.g., α1-acid glycoprotein and lipoproteins), and liver mass. Changes in any of these parameters can lead to changes in the biological disposition of a wide variety of drugs by altering either their concentrations in blood or tissues. Identification of patients with elevated enzyme activities or otherwise altered physiological parameters as a consequence of exposure to PAH could serve to lessen the risks and optimize therapeutic benefits of drug therapy. In this article, the pharmacokinetic properties of PAH, the possible mechanisms by which they can alter drug disposition, and specific examples are discussed.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".