The effect of gender and ABCB1 gene polymorphism on the pharmacokinetics of azithromycin in healthy male and female Pakistani subjects
Bibliographic record
Abstract
In the current study, the possible outcome of gender difference and genotypic polymorphism of the ABCB1 gene encoding P-glycoprotein on the pharmacokinetics of azithromycin has been evaluated. An open-label, comparative pharmacokinetic study was done in healthy Pakistani volunteers (females (n = 8) and males (n = 8)). They were administered a single 500 mg oral dose of azithromycin. Blood samples (≈5 mL) were collected in heparinized tubes and the HPLC/MS/MS method was used to determine azithromycin plasma levels. ABCB1 polymorphism (single nucleotide polymorphisms) at C3435T, G26SST was performed using the RFLP–PCR method. The Student t test was applied to compare pharmacokinetic parameters of azithromycin between male and female human subjects (at 95% CI) using GraphPad Prism-8. A significant difference was observed in pharmacokinetic parameters between males and females, as Cmax in males (230 ± 80.2 ng/mL) was significantly higher than in females (224.9 ± 75.5 ng/mL), while [Formula: see text] was also significantly higher (p < 0.05) in males (2102 ± 200.3 ng·h−1·mL−1) compared to females (1825.7 ± 225.4 ng·h−1·mL–1). There was a significant variation in Cmax and AUC in three ABCB1 genotyping groups as well. Gender difference and ABCB1 gene polymorphisms have a significant impact on the pharmacokinetics of azithromycin, as they contribute to interindividual variability in therapeutic response.
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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 | 0.001 |
| 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) | 0.003 | 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".