Best practices for detection, assessment and management of suspected immune-mediated liver injury caused by immune checkpoint inhibitors during drug development
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICIs) have shown significant efficacy in patients with various malignancies, however, they are associated with a wide range of immune-related toxicities affecting many organs, including the liver. Immune-mediated liver injury caused by checkpoint inhibitors (ILICI) is a distinctive form of drug induced liver injury (DILI), that differs from most DILI types in presumed underlying mechanism, incidence, and response to therapeutic interventions. Despite increased awareness of ILICI and other immune-related adverse effects of ICIs reflected by recent guidelines for their management in post marketing clinical practice, there is lack of uniform best practices to address ILICI risk during drug development. As efforts to develop safer and more effective ICIs for additional indications grow, and as combination therapies including ICIs are increasingly investigated, there is a growing need for consistent practices for ILICI in drug development. This publication summarizes current best practices to optimize the monitoring, diagnosis, assessment, and management of suspected ILICI in clinical trials using ICI as a single agent and in combination with other ICIs or other oncological agents. It is one of several publications developed by the IQ DILI Initiative in collaboration with DILI experts from academia and regulatory agencies. Recommended best practices are outlined pertaining to hepatic inclusion and exclusion criteria, monitoring of liver tests, ILICI detection, approach to a suspected ILICI signal, causality assessment, hepatic discontinuation rules and additional medical treatment.
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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 | 0.035 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".