Circular RNAs (circRNAs) are non-coding RNAs characterized by a strictly closed-loop covalent structure. They are abundantly detected in various cells due to their conserved nature. Studies have reported their potential association with chronic liver disease (CLD), including hepatitis, cirrhosis, and hepatocellular carcinoma (HCC), with possible roles as diagnostic and prognostic markers. This study aimed to analyze the potential use of serum-derived hsa_circ_101555 as a diagnostic tool for CLD without HCC, and to compare it with other known non-invasive parameters for liver disease severity and inflammation. Additionally, it aimed to evaluate its expression among non-HCC CLD patients, CLD with HCC cases reported in our published (phase I) study, and healthy controls.

A cross-sectional study (phase II) was conducted involving 30 clinically, laboratory, and radiologically diagnosed Egyptian non-HCC CLD patients and 30 healthy subjects. The serum expression level of hsa_circ_101555 was measured using real-time polymerase chain reaction. The diagnostic accuracy was assessed through receiver operating characteristic curve analysis, calculating the area under the curve to determine sensitivity and specificity. The study also compared hsa_circ_101555 levels with established non-invasive parameters such as the Child-Turcotte-Pugh and model for end-stage liver disease scores, as well as inflammatory markers like the neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio.

hsa_circ_101555 demonstrated high diagnostic accuracy (area under the curve of 0.970) at a cutoff point of 2.088 for differentiating non-HCC CLD patients from healthy controls. Elevated circRNA levels were noted in patients with hepatic encephalopathy and ascites, correlating with advanced liver disease scores (Child-Turcotte-Pugh/model for end-stage liver disease scores). Mean circRNA values were highest in HCC cases, followed by non-HCC CLD patients, and lowest in healthy controls.

Serum-derived hsa_circ_101555 demonstrates high diagnostic accuracy in differentiating non-HCC CLD patients from healthy controls. These findings suggest that hsa_circ_101555 has the potential to serve as a reliable non-invasive biomarker for the early diagnosis of CLD, correlating with disease severity and inflammation markers. Further research with larger sample sizes is warranted to validate its clinical utility and enhance the management of CLD.

Metabolic dysfunction-associated steatohepatitis (MASH) represents a critical step in the progression from simple fatty liver disease to more severe conditions such as cirrhosis and hepatocellular carcinoma, and it remains difficult to treat. Arctigenin (ATG), a monomer of Fructus Arctii, exhibits anti-inflammatory activity. Therefore, we aimed to examine its potential protective role against MASH and explore the underlying mechanisms.

Male C57BL/6 mice were divided into four groups: control, MASH, low-dose ATG (30 mg/kg/day), and high-dose ATG (120 mg/kg/day). MASH was induced through a choline-deficient, L-amino acid-defined high-fat diet for eight weeks, with concurrent preventive ATG administration. Liver injury, lipid metabolism, inflammation, oxidative stress, and fibrosis were assessed. Network pharmacology was employed to identify the potential protective mechanisms of ATG. Key factors were evaluated in vitro to verify the ATG targets.

ATG administration prevented the progression of MASH in a dose-dependent manner. High-dose ATG significantly reduced hepatic macrophage and neutrophil infiltration, serum enzyme levels, and lipid peroxidation, while enhancing antioxidant enzyme activity. Mechanistic network pharmacology identified modulation of the NLR family pyrin domain containing 3 (NLRP3) inflammasome as the central pathway underlying ATG’s bioactivity. Functional analyses in lipopolysaccharide-stimulated RAW264.7 cells confirmed that ATG inhibited NLRP3 expression, pyroptosis-related protein cleavage (hereinafter referred to as GSDMD-N), and pro-inflammatory chemokine production in a concentration-dependent manner. Notably, ATG disrupted NLRP3/GSDMD-N axis activity in macrophages without causing cellular toxicity.

ATG may inhibit the inflammatory cascade primarily by targeting macrophage NLRP3 inflammasomes, thereby preventing the progression of MASH.

Drug discovery is an exceptionally long and costly process, often taking over 10 years and costing billions of dollars. Despite these efforts, more than 90% of drug candidates fail, with most failures occurring during clinical trials due to issues related to efficacy, safety, or poor pharmacokinetics. A major contributor to these failures is biopharmaceutic barriers, including poor solubility, limited permeability, active efflux by transporters such as P-glycoprotein and breast cancer resistance protein, and extensive first-pass metabolism by CYP450 enzymes. These factors severely limit drug absorption and bioavailability, reducing therapeutic efficacy. Although traditional approaches, such as high-throughput absorption, distribution, metabolism, and excretion screening and improved chemical design, have achieved some progress, a major shift is now occurring through the use of in silico modeling, artificial intelligence (AI), and machine learning. These AI-driven tools enhance the prediction accuracy of absorption, distribution, metabolism, and excretion profiles, identify transporter interactions, and even simulate metabolic pathways. Additionally, modern formulation technologies, such as three-dimensional printing, lipid-based nanocarriers, and biodegradable delivery systems, are increasingly being integrated with AI-powered design platforms to personalize and optimize drug delivery. However, these promising advancements also raise regulatory and ethical concerns that must be addressed before widespread adoption. This review examines the major biopharmaceutic barriers responsible for drug development failures and explores how emerging AI-driven strategies and formulation innovations are being used to overcome these limitations. It also discusses current regulatory challenges and ethical considerations associated with adopting these technologies.

With the widespread application of systemic treatments for hepatocellular carcinoma, liver injury caused by molecular targeted drugs and immune checkpoint inhibitors has become a common clinical problem. The Chinese Society of Hepatology, Chinese Medical Association, organized domestic experts to summarize and analyze adverse liver reactions, as well as advances in the diagnosis and treatment related to systemic therapy for liver cancer, both domestically and internationally. Based on this work, we formulated the “Consensus on the Management of Liver Injury Associated with Targeted Drugs and Immune Checkpoint Inhibitors for Hepatocellular Carcinoma”, aiming to provide practical recommendations and decision-making guidance for clinicians in hepatology and related specialties. This guidance focuses on the monitoring, diagnosis, prevention, and treatment of liver injury during targeted and immune checkpoint inhibitor therapy, ultimately helping more liver cancer patients benefit from targeted immunotherapy.