Mechanism of idiosyncratic drug induced liver injury (DILI): unresolved basic issues
Bibliographic record
Abstract
Clinical features of idiosyncratic drug induced liver injury (DILI) are well described in cases that have been assessed for causality using the Roussel Uclaf Causality Assessment Method (RUCAM), but our understanding of the mechanistic steps leading to injury is fragmentary. The difficulties describing mechanistic events can be traced back to the lack of an animal model of experimental idiosyncratic DILI that can mimic the genetic requirements of human idiosyncratic DILI. However, immune tolerance plays a dominant role in the immune response of the liver, and impairment of immune tolerance with immune checkpoint inhibitors increases DILI in both humans and animals. This may provide one method to study the individual steps involved. In general. the human DILI liver is a secret keeper providing little insight into what occurs in the diseased organ. Sufficient evidence exists that most idiosyncratic cases are mediated by the adaptive immune system, which depends on stimulation of the innate immune system, but the triggering factors are unknown. It is attractive to hypothesize that the gut microbiome plays a role; however, it is very difficult to study. Similarly, exosomes are likely to play an important role in communication between hepatic cells and the immune system, but there is a lack of data on blood exosomes in affected patients. Reactive metabolites are likely to play an important role. This is supported by the current analysis, which revealed an association between metabolism by cytochrome P450 and drugs most commonly involved in causing idiosyncratic DILI with causality verified by RUCAM. Circumstantial evidence suggests that reactive oxygen species (ROS) generated by cytochrome P450 could be responsible for the initial steps of injury, but details are unknown. In conclusion, most of the mechanistic steps leading to idiosyncratic DILI remain unclear.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".