Hepatic fibrosis 2022: Unmet needs and a blueprint for the future

• Published in: Hepatology (Baltimore, Md.) Vol. 75; no. 2; pp. 473 - 488
• Main Authors: Friedman, Scott L., Pinzani, Massimo
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.02.2022
• Subjects: Animal models; Animals; Antifibrotic Agents; Artificial intelligence; Biomedical Research; Biopsy; Cell interactions; Clinical trials; Clinical Trials as Topic; Drug Development; Etiology; Fibrosis; Hepatitis; Hepatology; Humans; Learning algorithms; Liver; Liver - diagnostic imaging; Liver - pathology; Liver Cirrhosis - diagnostic imaging; Liver Cirrhosis - drug therapy; Liver Cirrhosis - etiology; Liver Cirrhosis - pathology; Machine learning; Molecular modelling; Needs Assessment
• ISSN: 0270-9139; 1527-3350
• DOI: 10.1002/hep.32285

Steady progress over four decades toward understanding the pathogenesis and clinical consequences of hepatic fibrosis has led to the expectation of effective antifibrotic drugs, yet none has been approved. Thus, an assessment of the field is timely, to clarify priorities and accelerate progress. Here, we highlight the successes to date but, more importantly, identify gaps and unmet needs, both experimentally and clinically. These include the need to better define cell–cell interactions and etiology‐specific elements of fibrogenesis and their link to disease‐specific drivers of portal hypertension. Success in treating viral hepatitis has revealed the remarkable capacity of the liver to degrade scar in reversing fibrosis, yet we know little of the mechanisms underlying this response. Thus, there is an exigent need to clarify the cellular and molecular mechanisms of fibrosis regression in order for therapeutics to mimic the liver’s endogenous capacity. Better refined and more predictive in vitro and animal models will hasten drug development. From a clinical perspective, current diagnostics are improving but not always biologically plausible or sufficiently accurate to supplant biopsy. More urgently, digital pathology methods that leverage machine learning and artificial intelligence must be validated in order to capture more prognostic information from liver biopsies and better quantify the response to therapies. For more refined treatment of NASH, orthogonal approaches that integrate genetic, clinical, and pathological data sets may yield treatments for specific subphenotypes of the disease. Collectively, these and other advances will strengthen and streamline clinical trials and better link histologic responses to clinical outcomes.