The Effect of β-Carotene Supplementation on the Pharmacokinetics of Nelfinavir and Its Active Metabolite M8 in HIV-1-infected Patients
Bibliographic record
Abstract
β-Carotene supplements are often taken by individuals living with HIV-1. Contradictory results from in vitro studies suggest that β-carotene may inhibit or induce cytochrome P450 enzymes and transporters. The study objective was to investigate the effect of β-carotene on the steady-state pharmacokinetics of nelfinavir and its active metabolite M8 in HIV-1 infected individuals. Twelve hour nelfinavir pharmacokinetic analysis was conducted at baseline and after 28 days of β-carotene supplementation (25,000 IU twice daily). Nelfinavir and M8 concentrations were measured with validated assays. Non-compartmental methods were used to calculate the pharmacokinetic parameters. Geometric mean ratios comparing day 28 to day 1 area under the plasma concentration-time curve (AUC(0-12 h)), maximum (C(max)) and minimum (C(min)) concentrations of nelfinavir and M8 are presented with 90% confidence intervals. Eleven subjects completed the study and were included in the analysis. There were no significant differences in nelfinavir AUC(0-12 h) and C(min) (-10%, +4%) after β-carotene supplementation. The M8 C(min) was increased by 31% while the M8 AUC(0-12 h) and C(max) were unchanged. During the 28 day period, mean CD4+ % and CD4+:CD8+ ratio increased significantly (p < 0.01). β-carotene supplementation increased serum carotene levels but did not cause any clinically significant difference in the nelfinavir and M8 exposure.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".