Application of Caco-2 Cell Line in Herb-Drug Interaction Studies: Current Approaches and Challenges
Bibliographic record
Abstract
The Caco-2 model is employed in pre-clinical investigations to predict the likely gastrointestinal permeability of drugs because it expresses cytochrome P450 enzymes, transporters, microvilli and enterocytes of identical characteristics to the human small intestine. The FDA recommends this model as integral component of the Biopharmaceutics Classification System (BCS). Most dedicated laboratories use the Caco-2 cell line to screen new chemical entities through prediction of its solubility, bioavailability and the possibility of drug-drug or herb-drug interactions in the gut lumen. However, challenges in the inherent characteristics of Caco-2 cell and inter-laboratory protocol variations have resulted to generation of irreproducible data. These limitations affect the extrapolation of data from pre-clinical research to clinical studies involving drug-drug and herb-drug interactions. This review addresses some of these caveats and enumerates the plausible current and future approaches to reduce the anomalies associated with Caco-2 cell line investigations focusing on its application in herb-drug interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".