Drug-induced liver injury (DILI) is the major reason for the discontinuation of new drug development and the withdrawal of drugs from the market. Hence, the evaluation systems which predict the onset of DILI in the pre-clinical stage are needed. To date, many researchers have conducted the mechanism of DILI, but the DILI prediction is poor because of the complexity of DILI. In this regard, based on the information obtained from basic research and clinical case, several pharmaceutical companies have been developed DILI prediction methods with high sensitivity and specificity by combining multiple targets. Another reason for low predictability is derived from the conventional culture method which causes a rapid decrease in hepatocyte function. To overcome these problems, the construction of a high-level in vitro evaluation system has been developed and applied to DILI evaluation. On the other hand, these in vitro evaluation methods require a lot of labor and cost so, in silico prediction methods have also been constructed in recent years. Based on this point, this article reviews the trends in DILI prediction systems in the non-clinical stage.
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http://dx.doi.org/10.1254/fpj.20049 | DOI Listing |
Drug Des Devel Ther
January 2025
Department of Pharmacy, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, 410004, People's Republic of China.
Purpose: Drug-induced liver injury (DILI) is one of the most common and serious adverse drug reactions related to first-line anti-tuberculosis drugs in pediatric tuberculosis patients. This study aims to develop an automatic machine learning (AutoML) model for predicting the risk of anti-tuberculosis drug-induced liver injury (ATB-DILI) in children.
Methods: A retrospective study was performed on the clinical data and therapeutic drug monitoring (TDM) results of children initially treated for tuberculosis at the affiliated Changsha Central Hospital of University of South China.
J Chem Inf Model
January 2025
Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China.
Drug-induced liver injury (DILI) is a major challenge in drug development, often leading to clinical trial failures and market withdrawals due to liver toxicity. This study presents StackDILI, a computational framework designed to accelerate toxicity assessment by predicting DILI risk. StackDILI integrates multiple molecular descriptors to extract structural and physicochemical features, including the constitution, pharmacophore, MACCS, and E-state descriptors.
View Article and Find Full Text PDFComput Biol Med
February 2025
Faculty of Computer and AI, Cairo University, Egypt. Electronic address:
Prediction of drug toxicity remains a significant challenge and an essential process in drug discovery. Traditional machine learning algorithms struggle to capture the full scope of molecular structure features, limiting their effectiveness in toxicity prediction. Graph Neural Network offers a promising solution by effectively extracting drug features from their molecular graphs.
View Article and Find Full Text PDFTalanta
April 2025
School of Water Conservancy and Environment, University of Jinan, Jinan, 250022, China. Electronic address:
Drug-induced liver injury (DILI) is a crucial factor that poses a significant threat to human health. DILI process leads to the changes of reactive oxygen species and reactive nitrogen species content in cells, which leads to oxidative and nitrosative stress in cells. However, the high reactivity of hypochlorous acid (HOCl) and peroxynitrite (ONOO⁻), combined with a lack of in situ imaging techniques, has hindered a detailed understanding of their roles in DILI.
View Article and Find Full Text PDFLiver Int
January 2025
Faculty of Medical Sciences, Translational & Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK.
Idiosyncratic hepatotoxicity induced by prescribed drugs has been known since the early 20th century. Identifying risk factors, including genetic factors, that trigger this drug-induced liver injury (DILI) has been an important priority for many years, both to prevent drugs that cause liver injury being licensed and as a potential means of preventing at-risk patients being prescribed causative drugs. Improved methods for genomic analysis, particularly the development of genome-wide association studies, have facilitated the identification of genomic risk factors for DILI, but, to date, there are only two main examples, liver injury caused by amoxicillin-clavulanate (AC) and by flucloxacillin, where genetic risk factors causing the injury have been identified and replicated with understanding of the underlying mechanism.
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