A semi-automated workflow for adverse outcome pathway hypothesis generation: The use case of non-genotoxic induced hepatocellular carcinoma

Hepatic toxicity prediction of bisphenol analogs by machine learning strategy

Toxicological studies have demonstrated the hepatic toxicity of several bisphenol analogs (BPs), a prevalent type of endocrine disruptor. The development of Adverse Outcome Pathway (AOP) has substantially contributed to the rapid risk assessment for human health. However, the lack of in vitro and in vivo data for the emerging BPs has limited the hazard assessment of these synthetic chemicals. Here, we aimed to develop a new strategy to rapidly predict BPs' hepatotoxicity using network analysis coupled with machine learning models. Considering the structural and functional similarities shared by BPs with Bisphenol A (BPA), we first integrated hepatic disease related genes from multiple databases into BPA-Gene-Phenotype-hepatic toxicity network and subjected it to the computational AOP (cAOP). Through cAOP network and conventional machine learning approaches, we scored the hepatotoxicity of 20 emerging BPs and provided new insights into how BPs' structure features contributed to biologic functions with limited experimental data. Additionally, we assessed the interactions between emerging BPs and ESR1 using molecular docking and proposed an AOP framework wherein ESR1 was a molecular initiating event. Overall, our study provides a computational approach to predict the hepatotoxicity of emerging BPs.

Benzo[b]fluoranthene induces male reproductive toxicity and apoptosis via Akt-Mdm2-p53 signaling axis in mouse Leydig cells: Integrating computational toxicology and experimental approaches

This study aims to explore the male reproductive toxicity of Benzo[b]fluoranthene (BbF) and related mechanisms. The results of computational toxicology analysis indicated male reproductive toxicity of BbF was related to apoptosis of Leydig cells and that Akt/p53 pathway might play a key role. In experiments, BbF induced testosterone decline, decreased concentration and motility of sperm and aggravated testicular pathological injury in mice. Besides, BbF led to apoptosis in Leydig cells, and decreased expressions of p-Akt and Bcl2, while improving the expressions of p53, Bax and Cleaved Caspase-3 in vivo and in vitro. Further, compared with BbF group, Akt activator SC79 significantly reduced cell apoptosis rate, improved cell viability, promoted the expressions of p-Akt and p-Mdm2, and reversed the above molecular expressions. Similarly, p53 inhibitor Pifithrin-α also significantly enhanced the cell vitality, alleviated the apoptosis of TM3 cells induced by BbF, and decreased the expressions of Bax and Cleaved Caspase-3, with the up-regulation of Bcl2. To sum up, by inhibiting Akt-Mdm2 signaling, BbF activated the p53-mediated mitochondrial apoptosis pathway, further inducing the apoptosis of Leydig cells, therefore resulting in testosterone decline and male reproductive damage. Besides, this study provided a valid mode integrating computational toxicology and experimental approaches in toxicity testing.

Associating diethylhexyl phthalate to gestational diabetes mellitus via adverse outcome pathways using a network-based approach

Gestational diabetes mellitus (GDM) is a common pregnancy complication that is harmful to both the woman and fetus. Several epidemiological studies have found that exposure to diethylhexyl phthalate (DEHP), an endocrine disruptor ubiquitous in the environment, may be associated with GDM. This study aims to investigate the mechanism between DEHP and GDM using the adverse outcome pathway (AOP) framework, which can integrate information from different sources to elucidate the causal pathways between chemicals and adverse outcomes. We applied a network-based workflow to integrate diverse information to generate computational AOPs and accelerate the AOP development. The interactions among DEHP, genes, phenotypes, and GDM were retrieved from several publicly available databases, including the Comparative Toxicogenomics Database (CTD), Computational Toxicology (CompTox) Chemicals Dashboard, DisGeNET, MalaCards, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). Based on the above interactions, a DEHP-Gene-Phenotype-GDM network consisting of 52 nodes and 227 edges was formed to support AOP construction. The filtered genes and phenotypes were assembled as molecular initiating events (MIEs) and key events (KEs) according to the upstream and downstream relationships, generating a computational AOP (cAOP) network. Based on the Organization for Economic Co-operation and Development handbook of AOPs, a cAOP was assessed and applied to determine the effects of DEHP on GDM. DEHP could increase TNF-α, downregulate the glucose uptake process, and lead to GDM. Overall, this study revealed the utility of computational methods in integrating a variety of datasets, supporting AOP development, and facilitating a better understanding of the underlying mechanism of exposure to chemicals on human health.

From Causal Networks to Adverse Outcome Pathways: A Developmental Neurotoxicity Case Study

The last decade has seen the adverse outcome pathways (AOP) framework become one of the most powerful tools in chemical risk assessment, but the development of new AOPs remains a slow and manually intensive process. Here, we present a faster approach for AOP generation, based on manually curated causal toxicological networks. As a case study, we took a recently published zebrafish developmental neurotoxicity network, which contains causally connected molecular events leading to neuropathologies, and developed two new adverse outcome pathways: Inhibition of Fyna (Src family tyrosine kinase A) leading to increased mortality via decreased eye size (AOP 399 on AOP-Wiki) and GSK3beta (Glycogen synthase kinase 3 beta) inactivation leading to increased mortality via defects in developing inner ear (AOP 410). The approach consists of an automatic separation of the toxicological network into candidate AOPs, filtering the AOPs according to available evidence and length as well as manual development of new AOPs and weight-of-evidence evaluation. The semiautomatic approach described here provides a new opportunity for fast and straightforward AOP development based on large network resources.

Semi-automated approach for generation of biological networks on drug-induced cholestasis, steatosis, hepatitis, and cirrhosis

Drug-induced liver injury (DILI) is one of the leading reasons for discontinuation of a new drug development project. Diverse machine learning or deep learning models have been developed to predict DILI. However, these models have not provided an adequate understanding of the mechanisms leading to DILI. The development of safer drugs requires novel computational approaches that enable the prompt understanding of the mechanism of DILI. In this study, the mechanisms leading to the development of cholestasis, steatosis, hepatitis, and cirrhosis were explored using a semi-automated approach for data gathering and associations. Diverse data from ToxCast, Comparative Toxicogenomic Database (CTD), Reactome, and Open TG-GATEs on reference molecules leading to the development of the respective diseases were extracted. The data were used to create biological networks of the four diseases. As expected, the four networks had several common pathways, and a joint DILI network was assembled. Such biological networks could be used in drug discovery to identify possible molecules of concern as they provide a better understanding of the disease-specific key events. The events can be target-tested to provide indications for potential DILI effects.