Optimization of the Caco-2 Permeability Assay to Screen Drug Compounds for Intestinal Absorption and Efflux
Bibliographic record
Abstract
In vitro permeability assays are a valuable tool for scientists during lead compound optimization. As a majority of discovery projects are focused on the development of orally bioavailable drugs, correlation of in vitro permeability data to in vivo absorption results is critical for understanding the structural-physicochemical relationship (SPR) of drugs exhibiting low levels of absorption. For more than a decade, the Caco-2 screening assay has remained a popular, in vitro system to test compounds for both intestinal permeability and efflux liability. Despite advances in artificial membrane technology and in silico modeling systems, drug compounds still benefit from testing in cell-based epithelial monolayer assays for lead optimization. This chapter provides technical information for performing and optimizing the Caco-2 assay. In addition, techniques are discussed for dealing with some of the most pressing issues surrounding in vitro permeability assays (i.e., low aqueous solubility of test compounds and low postassay recovery). Insights are offered to help researchers avoid common pitfalls in the interpretation of in vitro permeability data, which can often lead to the perception of misleading results for correlation to in vivo data.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".