Multiparametric High‐Content Assays to Measure Cell Health and Oxidative Damage as a Model for Drug‐Induced Liver Injury
Bibliographic record
Abstract
Drug-induced liver injury is an important cause of non-approval in drug development and the withdrawal of already approved drugs from the market. Screening human hepatic cell lines for toxicity has been used extensively to predict drug-induced liver injury in preclinical drug development. Assessing hepatic-cell health with more diverse markers will increase the value of in vitro assays and help predict the mechanism of toxicity. We describe three live cell-based assays using HepG2 cells to measure cell health parameters indicative of hepatotoxicity. The first assay measures cellular ATP levels using luciferase. The second and third assays are multiparametric high-content screens covering a panel of cell health markers including cell count, mitochondrial membrane potential and structure, nuclear morphology, vacuolar density, and reactive oxygen species and glutathione levels. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Measurement of cellular ATP content Basic Protocol 2: High-content analysis assay to assess cell count, mitochondrial membrane potential and structure, and reactive oxygen species Basic Protocol 3: High-content analysis assay to assess nuclear morphology, vacuoles, and glutathione content Support Protocol 1: Subculturing and maintaining HepG2 cells Support Protocol 2: Plating HepG2 cell line Support Protocol 3: Transferring compounds by pin tool Support Protocol 4: Generating dose-response curves.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".