Prediction of herb-drug interactions involving consumption of furanocoumarin-mixtures and cytochrome P450 1A2-mediated caffeine metabolism inhibition in humans
Bibliographic record
Abstract
Abstract Herb-drug interaction (HDI) has become important due to the increasing popularity of natural product consumption worldwide. HDI is difficult to predict as botanical drugs usually contain complex phytochemical-mixtures which interact with drug metabolism. Currently, there is no pharmacological tool to predict HDI since almost all in vitro-in vivo -extrapolation (IVIVE) Drug-Drug Interaction (DDI) models deal with one inhibitor-drug and one victim-drug. The objectives were to modify IVIVE models of Mayhew et al. (2000) and Wang et al. (2004) for prediction of in vivo interaction between caffeine and furanocoumarin-containing herbs, and to confirm model prediction by comparing the predictive results with experimental data. The models were modified to predict in vivo herb-caffeine interaction using the same set of inhibition constants but different integrated dose/concentration of furanocoumarin mixtures in the liver. Different hepatic inlet inhibitor concentration ([I] H ) surrogates were used for each furanocoumarin. In the Mayhew et al., the [I] H was predicted using the concentration-addition model for chemical-mixtures. In the Wang et al., the [I] H was calculated by adding individual furanocoumarins together. Once [I] H was determined, the models predicted an area-under-curve-ratio (AUCR) of each interaction. The results indicate that both models were able to predict the experimental AUCR of herbal products reasonably well.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".