Evaluating the synergistic use of advanced liver models and AI for the prediction of drug-induced liver injury
Bibliographic record
Abstract
INTRODUCTION: Drug-induced liver injury (DILI) is a leading cause of acute liver failure. Hepatotoxicity typically occurs only in a subset of individuals after prolonged exposure and constitutes a major risk factor for the termination of drug development projects. AREAS COVERED: We provide an overview of available human liver models for DILI research and discuss how they have been used to aid in early risk assessments and to mitigate the risk of project closures due to DILI in clinical stages. We summarize the different data that can be provided by such models and illustrate how these diverse data types can be interfaced with machine learning strategies to improve predictions of liver safety liabilities. EXPERT OPINION: Advanced human liver models closely mimic human liver phenotypes and functions for many weeks, allowing for the recapitulation of hepatotoxicity events in vitro. Integration of the biochemical, histological, and toxicogenomic output data from these models with physicochemical compound properties using different machine learning architectures holds promise to enhance preclinical DILI predictions. However, to realize this aim, it is important to benchmark the available liver models on test sets of DILI positive and negative compounds and to carefully annotate and share the resulting data.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".