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

Doktorova, Tatyana Y.; Oki, Noffisat O.; Mohorič, Tomaž; Exner, Thomas E.; Hardy, Barry

doi:10.1016/j.yrtph.2020.104652

Cited by 6 publications

(3 citation statements)

References 118 publications

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“…Following a previously described approach [ 9 ], frequent item set mining methods were applied to high-throughput screening, gene expression, in vivo studies, and disease data present in ToxCast and the CTD. ToxCast provides high-throughput toxicity screening in vitro assay data, and active/inactivity calls on one target gene were collected.…”

Section: Methodsmentioning

confidence: 99%

“…For some complex diseases such as DILI, biological networks are an appropriate way of capturing events and relationships as they cover the entire spectrum of relationships, which can also be non-linear. In evidence collection for biological network development, use of historical data and novel computational approaches for data analysis and assembly have proven very useful for hypothesis generation [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…Several studies have used association rule mining to generate computationally predicted AOPs, mostly at the molecular level ( Bell et al, 2016 ; Oki et al, 2016 ). Doktorova et al have further developed a filtering approach for refining the molecular AOP-like networks using gene expression data from TG-Gates database ( Igarashi et al, 2015 ) and manual curation to arrive at a putative AOP-network ending in the AO non-genotoxic induced hepatocellular carcinoma ( Doktorova et al, 2020 ). The AOP-helpFinder tool uses text mining to find potential connections between key events ( Jornod et al, 2021 ), while the computational pipeline developed by Jin et al uses chemical-specific toxicogenomic data, pathway enrichment analysis and biomarker selection to develop putative cpAOPs ( Jin et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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

Ramšak

Modic

et al. 2022

Front. Toxicol.

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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.

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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

Shi

Han

Xiao

et al. 2023

Food and Chemical Toxicology

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

Cited by 6 publications

References 118 publications

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

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

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

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

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