Best practices for detection, assessment and management of suspected immune-mediated liver injury caused by immune checkpoint inhibitors during drug development
Why this work is in the frame
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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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it