Novel Diagnostic and Therapeutic Strategies in Juvenile Autoimmune Hepatitis - Sciveres, Marco; Nastasio, Silvia; Maggiore, Giuseppe (Frontiers in Pediatrics, 2019)
Juvenile autoimmune hepatitis (JAIH) is a rare, chronic, inflammatory disease of the liver characterized by a complex interaction between genetic, immunological and environmental factors leading to loss of immunotolerance to hepatic antigens. It affects both children and adolescents, most commonly females, and its clinical manifestations are quite variable. JAIH is progressive in nature and if left untreated may lead to cirrhosis and terminal liver failure. Although JAIH was first described almost 50 years ago, there have been few significant advances in the clinical management of these patients, both in terms of available diagnostic tools and therapeutic options. Aminotransferase activity, class G immunoglobulins and autoantibodies are the biomarkers used to diagnose AIH and monitor treatment response alongside clinical and histological findings. Despite their utility and cost-effectiveness, these biomarkers are neither an accurate expression of AIH pathogenic mechanism nor a precise measure of treatment response. Current standard of care is mainly based on the administration of steroids and azathioprine. This combination of drugs has been proven effective in inducing remission of disease in the majority of patients dramatically improving their survival; however, it not only fails to restore tolerance to hepatic autoantigens, but it also does not halt disease progression in some patients, it is often needed life-long and finally, it has deleterious side-effects. The ideal therapy should be enough selective to contrast immune-mediated live damage while preserving or potentiating the ability to develop permanent tolerance versus pathogenic autoantigens. By reviewing the state of the art literature, this article highlights novel diagnostic and therapeutic strategies for managing pediatric AIH with a special focus on new strategies of immunotherapy. These promising tools could improve the diagnostic algorithm, more accurately predict disease prognosis, and provide targeted, individualized treatment.