Autoimmune hepatitis: Current and future therapeutic options
Bibliographic record
Abstract
Autoimmune hepatitis (AIH) is a rare immune-mediated liver disease with few major advances in treatment options over the last several decades. Available options are effective in most patients albeit are imprecise in their mechanisms. Novel and more tolerable induction regimens and alternative options for management of patients intolerant or with suboptimal response to traditional therapies including in the post-transplant setting remain an important unmet need. This review aims to summarize recent data on pharmacological options and investigational drugs in development for patients with AIH. Standard therapy using prednisone with or without azathioprine remains the mainstay of therapy and is effective in most patients. Budesonide may be considered for induction in early disease and in those with mild fibrosis, but has not been approved for maintenance therapy. Mycophenolate mofetil (MMF) in combination with steroids might be an alternative first-line therapy, but results from a randomized trial are awaited. MMF as a second-line maintenance agent has moderate efficacy though more frequent adverse events in patients with cirrhosis may be seen. Tacrolimus may be an equally effective second-line option particularly in non-responders, but data remain limited. Management of recurrent AIH post-liver transplantation remains controversial with insufficient data to support long-term steroid use. Moving forward, expanding the scope of therapeutic options to include biologics including B-cell depleting agents may be a promising step. Recent insights in understanding the pathogenesis of AIH could serve as a basis for future therapies, including the elucidation of different immunoregulatory pathways and the potential role of the intestinal microbiome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".