Quo vadis autoimmune hepatitis? - Summary of the 5th international autoimmune hepatitis group research workshop 2024Keypoints - Bastian Engel; David N. Assis; Mamatha Bhat; Jan Clusmann; Joost PH. Drenth; Alessio Gerussi; María-Carlota Londoño; Ye Htun Oo; Ida Schregel; Marcial Sebode; Richard Taubert (JHEP Reports)
Summary: Autoimmune hepatitis (AIH) is a rare chronic liver disease with an increasing incidence in many countries. Chronic autoimmune responses against the liver can cause hepatic and extrahepatic symptoms, decreased quality of life and reduced liver transplant-free survival if inadequately treated. Although standard treatment with corticosteroids and thiopurines improves the life expectancy of patients with AIH, remission rates and tolerability are generally overestimated and the development of alternative first-line and salvage therapies has been disappointingly slow compared to in rheumatological diseases or inflammatory bowel disease. Other gaps include the lack of disease-specific diagnostic markers for AIH. Similarly, the new entity of drug-induced autoimmune-like hepatitis underscores the need to re-evaluate previous diagnostic criteria. The International AIH Group (IAIHG) has initiated a series of research workshops over the last decade to promote the identification of research gaps and subsequently improve the pace of scientific progress by stimulating collaboration between expert centres. This review reports on the results of the 5th Research Workshop, held in Hannover, Germany in June 2024, and summarises the progress made since the 4th Workshop in 2022. Patient representatives from the European Reference Network (ERN) Rare Liver Youth Panel participated in the workshop. The specific objectives of this year's 5th Workshop were: (1) To further improve diagnostics. (2) Initiate clinical trials including knowledge transfer on drugs from extrahepatic immune-mediated diseases, including B cell-depleting CAR T cells. (3) Utilisation of multi-omics approaches to improve the understanding of disease pathogenesis. (4) Application of machine learning-based approaches established in oncology or transplantation medicine to improve diagnosis and outcome prediction in AIH.