Drug toxicity prediction plays a crucial role in the drug research and development process, ensuring clinical drug safety. However, traditional methods are hampered by high cost, low throughput, and uncertainty of cross-species extrapolation, which has become a key bottleneck restricting the efficiency of new drug research and development. The breakthrough development of Artificial Intelligence (AI) technology, especially the application of deep learning and multimodal data fusion strategy, is reshaping the scientific paradigm of drug toxicology assessment. In this review, we focus on the application of AI in the field of drug toxicity prediction and systematically summarize the relevant literature and development status globally in the past years. The application of various toxicity databases in the prediction was elaborated in detail, and the research results and methods for the prediction of different toxicity endpoints were analyzed in depth, including acute toxicity, carcinogenicity, organ-specific toxicity, etc. Furthermore, this paper discusses the application progress of AI technologies (e.g., machine learning and deep learning model) in drug toxicity prediction, analyzes their advantages and challenges, and outlines the future development direction. It aims to provide a comprehensive and in-depth theoretical framework and actionable technical strategies for toxicity prediction in drug development.

The liver damage associated with Polygonum multiflorum Thunb. (P. multiflorum) and its preparations have aroused widespread concern. Opinions on the toxicity mechanisms and targets of P. multiflorum vary, and the toxic components are even more controversial. However, based on the current research results, we believed that any single component in P. multiflorum could not directly lead to liver injury, but may be the synergistic effect of multiple components. In addition, the toxicity mechanism also involved multiple targets. This study aimed to elucidate the mechanism and target of the hepatotoxicity of P. multiflorum. In this study, the manifestations of liver injury triggered by P. multiflorum and the associated metabolic enzymes/transporters in the metabolic pathways of bilirubin and bile acid were investigated to elucidate the mechanism and target of the hepatotoxicity of P. multiflorum and related components. First, the hepatotoxicity and potential effect of P. multiflorum on both metabolic pathways were studied in rats administered P. multiflorum extracts (in 70% ethanol) for 42 days. Then, in vitro cultured hepatocyte spheroids were used to determine the hepatotoxicity of monomer components. This revealed that P. multiflorum could simultaneously block bilirubin(BIL) and bile acid(BA) metabolism pathways, subsequently leading to liver damage. The targets and modes of action include reducing the activity of UGT1A1, the only metabolic enzyme of BIL, downregulating BIL and BA uptake transporters NTCP, OATP1B1, OATP1B3, efflux transporters MRP2, and BSEP, and upregulating efflux transporter MRP3. Furthermore, our data indicated that 2,3,5,4'-tetrahydroxystilbene-2-O-β-glucoside (TSG) and emodin-8-O-β-D-glucoside (EG) are the main toxic components in P. multiflorum. TSG accounts for 3.71% of the total content of P. multiflorum. In addition to markedly downregulating UGT1A1, TSG can upregulate OATP1B1/3 and promote the uptakes of bilirubin and bile acid, producing synergistic toxicity. EG accounts for 0.29% of the total content and demonstrates direct hepatotoxicity and extensive substrate overlap with bilirubin and bile acids. It can affect these two metabolic pathways simultaneously, promoting the accumulation of both bilirubin and bile acid for further toxic effects. Emodin is other major component, accounting for 0.01% of the total content, and its hepatotoxicity mechanisms include direct toxicity and inhibitory effects on bilirubin metabolizing enzymes. However, emodin is mainly distributed in the kidneys, so its hepatotoxicity risk is relatively low. The simultaneous blockade of bilirubin and bile acid metabolic pathways as the critical toxic mechanism of P. multiflorum-induced liver injury, and potential toxic components were TSG and EG.

To understand the genotoxicity induced in the liver by silver nanoparticles (AgNPs) and silver ions, an engineered gold nanorod core/silver shell nanostructure (Au@Ag NR) and humanized hepatocyte HepaRG cells were used in this study. The involvement of oxidative stress and cell cycle arrest in the DNA and chromosome damage induced by 0.4–20 µg mL⁻¹ Au@Ag NR were investigated by comet assay, γ-H2AX assay and micronucleus test. Further, the distribution of Au@Ag NR was analyzed. Our results demonstrated that both Ag⁺ and Au@Ag NR led to DNA cleavage and chromosome damage (clastogenicity) in HepaRG cells and that the Au@Ag NR retained in the nucleus may further release Ag⁺, aggravating the damages, which are mainly caused by cell cycle arrest and ROS formation. The results reveal the correlation between the intracellular accumulation, Ag⁺ ion release and the potential genotoxicity of AgNPs.

Aim: To explore the hepatotoxicity of copper sulfide nanoparticles (CuSNPs) toward hepatocyte spheroids. Materials & methods: Other than the traditional agarose method to generate hepatocyte sphero...

This study integrates network toxicology with molecular docking technology to systematically elucidate the key molecular mechanisms and signaling pathways by which bisphenol A (BPA) induces obesity. By cross-referencing multiple databases—including the Comparative Toxicogenomics Database (CTD), SwissTarget prediction platform, and PharmMapper—potential BPA target genes were identified, yielding a total of 1326 candidate targets. Obesity-related genes were collected from GeneCards and OMIM databases, yielding 4570 disease-associated targets. Among these, 653 overlapping genes were identified as potential mediators linking BPA exposure to obesity. Protein interaction networks were constructed using STRING and Cytoscape, and the MCC algorithm identified five core hub genes: STAT3, MYC, TP53, IL6, and mTOR. Validation using random datasets demonstrated significant upregulation of these genes in the obesity group (p < 0.05), highlighting their potential central role in BPA-induced obesity effects. Functional enrichment analysis via GO and KEGG pathways indicated that BPA may promote obesity by interfering with endocrine signaling, activating lipid metabolism, and stimulating atherosclerosis pathways. Molecular docking analysis using CB-Dock2 confirmed strong binding affinity between BPA and core targets, providing structural evidence for their potential interactions. This study elucidates the potential biological mechanism by which BPA exacerbates obesity through endocrine disruption and metabolic reprogramming, employing a multidimensional approach encompassing cross-target analysis, pathway enrichment, and molecular interactions. It provides an innovative systems toxicology framework and empirical basis for assessing metabolic health risks induced by environmental pollutants.

This study investigated the molecular mechanisms by which phthalates induce depression, utilizing network toxicology and molecular docking techniques. By integrating the TargetNet, GeneCards, and PharmMapper databases, 658 potential target genes of phthalates were identified. Additionally, 5433 depression-related targets were retrieved from the GeneCards and OMIM databases. Comparative analysis revealed 360 common targets implicated in both phthalate action and depression. A Protein-Protein Interaction (PPI) network was constructed using the STRING database. Subsequently, the CytoHubba plugin (employing the MCC algorithm) within Cytoscape was used to screen the network, identifying the top 20 hub genes. These core genes include AKT1, CASP3, TNF, TP53, BCL2, and IL6, among others. Validation on the GEO dataset (GSE23848) revealed that the expression of multiple core genes was significantly upregulated in patients with depression (p < 0.05). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated that phthalates mainly regulate biological processes such as extracellular stimulus response, lipopolysaccharide metabolism, and chemical synaptic transmission. Depression is mediated by the AGE-RAGE signaling pathway (a complication of diabetes), lipids and atherosclerosis, Endocrine resistance, and the PI3K-Akt signaling pathway. Molecular docking confirmed that phthalates have strong binding activity with key targets (CASP3, TNF, TP53, BCL2, IL6). These findings present a novel paradigm for evaluating the health risks posed by environmental pollutants.