Liver safety assessment in clinical trials of new agents for chronic hepatitis B
Bibliographic record
Abstract
Investigational agents that reduce or eliminate covalently closed circular DNA (cccDNA) or enhance host immunity against hepatitis B virus (HBV)-infected hepatocytes are intended to induce a durable off-treatment clearance of hepatitis B surface antigen (HBsAg) (referred to as functional cure). The aim of this paper was to highlight challenges in interpreting liver safety data in clinical trials of these agents when given alone or in combination regimens. The incidence, grading and management of spontaneous serum ALT flares in untreated chronic HBV patients are reviewed along with a summary of serum ALT flares observed during the registration trials for peginterferon and nucleos(t)ide reverse transcriptase inhibitors. Recommendations regarding the detection, management and interpretation of liver safety biomarker data in future clinical trials as well as suggested inclusion and exclusion criteria for phase 1/2 vs phase 3 studies are provided. Criteria to help classify liver safety signals as being due to the intended therapeutic response, emergence of drug-resistant HBV virions, or idiosyncratic drug-induced liver injury are provided along with a review of the role of an expert hepatic adjudication panel in assessing a compound's hepatotoxicity profile. Finally, an algorithmic approach to the differential diagnosis and recommended medical evaluation and management of individual clinical trial patients that develop a liver safety signal is provided along with the rationale to collect and test research blood samples for future mechanistic studies.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".