Do In Vitro Assays Predict Drug Candidate Idiosyncratic Drug-Induced Liver Injury Risk?
Bibliographic record
Abstract
In vitro assays are commonly used during drug discovery to try to decrease the risk of idiosyncratic drug-induced liver injury (iDILI). But how effective are they at predicting risk? One of the most widely used methods evaluates cell cytotoxicity. Cytotoxicity assays that used cell lines that are very different from normal hepatocytes, and high concentrations of drug, were not very accurate at predicting idiosyncratic drug reaction risk. Even cytotoxicity assays that use more biologically normal cells resulted in many false-positive and false-negative results. Assays that quantify reactive metabolite formation, mitochondrial injury, and bile salt export pump (BSEP) inhibition have also been described. Although evidence suggests that reactive metabolite formation and BSEP inhibition can play a role in the mechanism of iDILI, these assays are not very accurate at predicting risk. In contrast, inhibition of the mitochondrial electron transport chain appears not to play an important role in the mechanism of iDILI, although other types of mitochondrial injury may do so. It is likely that there are many additional mechanisms by which drugs can cause iDILI. However, simply measuring more parameters is unlikely to provide better predictive assays unless those parameters are actually involved in the mechanism of iDILI. Hence, a better mechanistic understanding of iDILI is required; however, mechanistic studies of iDILI are very difficult. There is substantive evidence that most iDILI is immune mediated; therefore, the most accurate assays may involve those that determine immune responses to drugs. New methods to manipulate immune tolerance may greatly facilitate development of more suitable methods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.003 | 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".