Network proximity analysis as a theoretical model for identifying potential novel therapies in primary sclerosing cholangitis.

Primary Sclerosing Cholangitis (PSC) is a progressive cholestatic liver disease with no licensed therapies. Previous Genome Wide Association Studies (GWAS) have identified genes that correlate significantly with PSC, and these were identified by systematic review. Here we use novel Network Proximity Analysis (NPA) methods to identify already licensed candidate drugs that may have an effect on the genetically coded aspects of PSC pathophysiology.Over 2000 agents were identified as significantly linked to genes implicated in PSC by this method. The most significant results include previously researched agents such as metronidazole, as well as biological agents such as basiliximab, abatacept and belatacept. This in silico analysis could potentially serve as a basis for developing novel clinical trials in this rare disease.

How effective are experienced hepatologists at staging fibrosis using non-invasive fibrosis tests in patients with metabolic dysfunction-associated steatotic liver disease?

BACKGROUND: Sequential use of non-invasive fibrosis tests (NITs) to identify patients with advanced hepatic fibrosis is recommended. However, it remains unclear how reliable clinicians are staging liver fibrosis using combinations of NITs. AIM: Our aim was to assess concordance between NIT-based 'clinician fibrosis assessment (CFA)' and histology in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) and compare this with established algorithmic approaches. METHODS: Six experienced hepatologists independently staged 230 MASLD patients for advanced fibrosis (F0-2 vs F3-4) using FIB-4, FIB-4+ELF, FIB-4+ vibration controlled transient elastography (VCTE; Fibroscan™) and FIB-4+ELF+VTCE. Concordance between histology and CFA or algorithmic approaches were assessed. RESULTS: A total of 230 patients were included (median age 54 [22-78] years; 55% female; median FIB-4 1.21 [IQR: 0.78-1.91]; ELF 9.3 [IQR: 8.6-10.2]; VCTE 9.4 [IQR: 6.3-14.3]; 41% F0-1, 22% F2, 21% F3 and 16% F4). Overall, area under the receiver operator curves for histologic F3-4 for the raw tests were 0.84 for FIB-4, 0.86 for ELF and 0.86 for VCTE. Concordance between the hepatologists was good (FIB4, κ = 0.64; FIB-4+ELF, κ = 0.70; FIB-4+VCTE, κ = 0.69; FIB-4+ELF+VCTE, κ = 0.70). Concordance between individual CFA and histology was variable, which was reflected in variability in sensitivity (44%-84%) and specificity (76%-94%). Concordance with histology was better when clinicians used NIT combinations. Purely algorithmic approaches, particularly sequential use of FIB-4 then VCTE, tended to perform better than the CFA. CONCLUSIONS: Adhering to the recommended algorithmic approaches using NITs to stage fibrosis tended to perform more accurately than less-structured clinician NIT-based assessments conducted by experienced hepatologists.