Identification of Mechanism and Pathway of the Interaction between the African Traditional Medicine, Sutherlandia Frutescens, and the Antiretroviral Protease Inhibitor, Atazanavir, in Human Subjects Using Population Pharmacokinetic (PK) Analysis
Bibliographic record
Abstract
Although the use of the indigenous Southern African plant, Sutherlandia frutescens (SF) for the treatment of HIV/AIDS has previously been described, the risk which it may pose to the safety and efficacy of ARVs and the potential mechanisms which underlie such effects may have clinical significance and relevance. The protease inhibitor (PI), atazanavir (ATV) is a substrate of the efflux transporter, P-gp which modulates absorption in the small intestine, as well as CYP3A4 and CYP3A5enzymes which facilitate metabolism in the small intestine and liver. The objective of this study was to investigate the effect of SF on the pharmacokinetics (PK) of atazanavir (ATV) and to use a population PK analysis to fit and explain plasma concentration vs. time profiles of ATV generated in a previously conducted study in healthy male subjects in order to understand and postulate on the potential mechanism(s) of the drug-drug interaction. The population PK Compartmental Analysis of ATV before and after a two-week regimen of Phyto Nova Sutherlandia SU1 tablets which contain SF plant material indicated that a two compartment model with a dual absorption mechanism best explained the data. The dual absorption mechanism is hypothesized to reflect "passive" (first-order, Ka parameter) and "active" (zero-order, K0 parameter) absorption processes. The model suggested that the mechanism by which SF reduced the overall bioavailability of ATV may be modulated via the inhibition of the "active" absorption process. This study has highlighted the utility of population PK analyses in postulating probable mechanism(s) whereby an ATM or a herbal medicine interacts with an allopathic drug.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".