Current Drug Metabolism - Volume 26, Issue 8, 2025

Cirrhosis is a very serious, gradual liver disease characterized by the overproduction of collagen and scarring, which together may result in liver failure or hepatocellular cancer. It typically develops based on fibrosis, which may be reversible when diagnosed in its initial stages. Viral hepatitis, alcohol, non-alcoholic fatty liver disease, and drug-induced hepatotoxicity are some of the etiologies of the disease. It is worth noting that about half of the cases of hepatic damage in patients are known to be caused by drug-induced liver injury. The existence of the pathophysiological complexity and therapeutic reactions of cirrhosis requires experimental models that are reliable. This review gives insight into the different in vivo, ex vivo, and operative animal models employed to cause hepatic injury by application of chemicals, drugs, dietary, and bile duct ligation particulars. The focus is on the mechanistic understanding of the actions of hepatotoxic substances, their involvement in oxidative stress, mitochondrial pathology, and the inflammatory process. Each model is critically discussed in terms of its advantages, limitations, and translational relevance.Additionally, this work highlights emerging alternatives, such as organ-on-a-chip systems, 3D co-cultures, and hepatic bioengineered tissues, as solutions that aim to reduce animal use and increase physiological relevance. The review also discusses biomarkers, histopathological endpoints, and important molecular players in hepatic fibrosis and cirrhosis. The review also examines data from preclinical studies of hepatoprotective substances and modulators of fibrosis, as it facilitates the integration of data from various experimental platforms. The next steps involve personalized pathology based on decellularized liver scaffolding and patient cells, aiming to better determine clinical outcomes and treatment responses.

Licochalcone A (LCA) is an important secondary metabolite in licorice that has attracted extensive attention due to its unique species-specific distribution characteristics and various pharmacodynamic activities, particularly its anti-inflammatory and anti-cancer effects. LCA was originally considered exclusive to Glycyrrhiza inflata Batal. However, further analyses have shown its distribution in different licorice species, extending its known distribution among licorice species and suggesting a broader role in secondary metabolism. Nevertheless, the complex chemical synthesis of LCA presents challenges in regioselectivity control. The oral bioavailability of LCA is limited due to the intestinal first-pass effect, and its metabolic mechanism has not yet been fully elucidated. These issues restrict the therapeutic effects and practical applications of LCA in vivo. In recent years, advancements in optimizing synthetic pathways and developing new delivery systems have significantly improved the efficacy of LCA while also achieving notable breakthroughs in its safety. This review examines the distribution patterns, synthesis methods, in vivo metabolic processes, pharmacological activities, and current application status of LCA, while also exploring future research directions. However, its metabolic mechanisms and prospects for clinical application still require further investigation in the future. A multi-source database search related literature employed “Licochalcone A”as the anchor term, synergized with species taxonomy (Glycyrrhiza), biogeographic patterns, and phytochemical dynamics (biosynthesis/metabolism).

Rare diseases present unique challenges in drug discovery and development, primarily due to small patient populations, limited clinical data, and significant variability in disease mechanisms. The primary objective of this review is to examine the integration of pharmacokinetics (PK) and drug metabolism data into data-driven drug discovery approaches, particularly in the context of rare diseases. By incorporating advanced computational techniques such as Machine Learning (ML) and Artificial Intelligence (AI), researchers can better predict PK parameters, optimize drug candidates, and identify personalized therapeutic strategies. AI integration with genomic and proteomic data reveals previously unidentifiable pathways, fostering collaboration among researchers, clinicians, and pharmaceutical companies. This interdisciplinary approach reduces development timelines and costs while enhancing the precision and effectiveness of therapies for patients with rare diseases. This review highlights the critical role of absorption, distribution, metabolism, and excretion (ADME) in understanding drug behavior in genetically diverse populations, thereby enabling the development of tailored treatments for patients with rare diseases. Additionally, it evaluates the opportunities and limitations of integrating PK/PD (pharmacodynamics) models with multi-omics data to improve drug discovery efficiency. Key examples of enzyme-drug interactions, metabolic pathway analysis, and AI-based PK simulations are discussed to illustrate advancements in predictive accuracy and drug safety. This review concludes by emphasizing the transformative potential of integrating PK and metabolism studies into the broader framework of data-driven drug discovery, ultimately accelerating therapeutic innovation and addressing unmet medical needs in rare diseases.