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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".