Use of a systems model of drug-induced liver injury (DILIsym®) to elucidate the mechanistic differences between acetaminophen and its less-toxic isomer, AMAP, in mice
Bibliographic record
Abstract
Acetaminophen (APAP) has been used as a probe drug to investigate drug-induced liver injury (DILI). In mice, 3'-hydroxyacetanilide (AMAP), a less-toxic isomer of APAP, has also been studied as a negative control. Various mechanisms for the divergence in toxicological response between the two isomers have been proposed. This work utilized a mechanistic, mathematical model of DILI to test the plausibility of four mechanistic hypotheses. Simulation results were compared to an array of measured endpoints in mice treated with APAP or AMAP. The four hypotheses included: (1) quantitative differences in drug metabolism profiles as a result of different affinities for the relevant enzymes; (2) differences in the amount of reactive metabolites produced due to cytochrome P450 (CYP450) inhibition by the AMAP reactive metabolites; (3) differences in the rate of conjugation between the reactive metabolites and proteins; (4) differences in the downstream effects or potencies of the reactive metabolites on vital components within hepatocytes. The simulations did not support hypotheses 3 or 4 as the most likely hypotheses underlying the difference in hepatoxic potential of APAP and AMAP. Rather, the simulations supported hypotheses 1 and 2 (less reactive metabolite produced per mole of AMAP relative to APAP). Within the simulations, the difference in reactive metabolite formation was equally likely to have occurred from differential affinities for the relevant drug metabolism enzymes or from direct CYP450 inhibition by the AMAP reactive metabolite. The demonstrated method of using simulation tools to probe the importance of possible contributors to toxicological observations is generally applicable across species.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".