Classic Histamine H1 Receptor Antagonists: A Critical Review of their Metabolic and Pharmacokinetic Fate from a Birds Eye View
Bibliographic record
Abstract
The so-called "classic" histamine H(1) receptor antagonists are highly lipophilic compounds associated with significant biotransformation and tissue distribution. They are categorized according to their chemical structure into ethanolamines, alkylamines, ethylenediamines, piperazines, phenothiazines and piperidines, all of which have characteristic metabolic fates. The former four categories undergo primarily cytochrome P450-mediated oxidative N-desalkylations and deamination whereas the aromatic rings of the latter two undergo P450-mediated oxidative hydroxylation and/or epoxide formation. The common tertiary amino group is susceptible to oxidative metabolism by flavin containing monooxygenases forming N-oxides, and the alicyclic tertiary amines produce small amounts (up to 7%) of N-glucuronides in humans. Species, sex and racial differences in the metabolism and pharmacokinetics of antihistamines are known. Specific P450-isozymes implicated in the metabolism were identified in a few cases, such as CYP2D6 that contributes to the metabolism of promethazine, diphenhydramine and chlorpheniramine. Low circulating plasma concentrations of antihistamines are in part explained by significant first-pass effect and tissue distribution. Antihistaminic effects last up to 6 hours though some compounds exhibit a longer duration of action due to circulating active metabolites. Importantly, diphenhydramine inhibited CYP2D6 leading to a clinically significant drug-drug interaction with metoprolol. Other classic antihistamines were shown to be potent in vitro inhibitors of CYP2D6 and CYP3A4. The prescription-free access to most classic antihistamines can easily lead to their co-administration with other drugs metabolized by the same enzyme system thereby leading to drug accumulation and adverse effects. In depth knowledge of the metabolic pathways of classic antihistamines and the enzymes involved is crucial to prevent the high incidence of drug interactions in humans, which are predictable based on pre-clinical data but unexpected when such data is unavailable.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".