3D Organotypic Cultures of Human HepaRG Cells: A Tool for In Vitro Toxicity Studies
Bibliographic record
Abstract
Drug-induced human hepatotoxicity is difficult to predict using the current in vitro systems. In this study, long-term 3D organotypic cultures of the human hepatoma HepaRG cell line were prepared using a high-throughput hanging drop method. The organotypic cultures were maintained for 3 weeks and assessed for (1) liver specific functions, including phase I enzyme and transporter activities, (2) expression of liver-specific proteins, and (3) responses to three drugs (acetaminophen, troglitazone, and rosiglitazone). Our results show that the organotypic cultures maintain high liver-specific functionality during 3 weeks of culture. The immunohistochemistry analyses illustrate that the organotypic cultures express liver-specific markers such as albumin, CYP3A4, CYP2E1, and MRP-2 throughout the cultivation period. Accordingly, the production rates of albumin and glucose, as well as CYP2E1 activity, were significantly higher in the 3D versus the 2D cultures. Toxicity studies show that the organotypic cultures are more sensitive to acetaminophen- and rosiglitazone-induced toxicity but less sensitive to troglitazone-induced toxicity than the 2D cultures. Furthermore, the EC50 value (2.7mM) for acetaminophen on the 3D cultures was similar to in vivo toxicity. In summary, the results from our study suggest that the 3D organotypic HepaRG culture is a promising in vitro tool for more accurate assessment of acute and also possibly for chronic drug-induced hepatotoxicity.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".