Steven Kunnen

Steven Kunnen is a senior postdoc at the Leiden Academic Centre for Drug Research (LACDR) of the Leiden University. He has a background in life sciences and holds a doctorate degree in medicine. He co-developed the toxicogenomics based TXG-MAPr tools as application for mechanistic toxicology. He is contributing to several international projects, including RISH-HUNT3R, to further develop the TXG-MAPr tools for mechanistic hazard assessment and linking to AOP networks.

NextGen Basel 1

TXG-MAPr tools: gene co-expression network analysis of toxicogenomic data to provide quantitative mode-of-action assessment and prediction of chemical-induced toxicity

Steven J. Kunnen1, Giulia Callegaro1, Hugo van Kessel1, Lukas S. Wijaya1, Imke Bruns1, James L. Stevens1, Bob van de Water1

Leiden University, Leiden Academic Centre for Drug Research, Leiden, The Netherlands

Next-generation risk assessment (NGRA) of chemicals revolves around the use of mechanistic information without animal experimentation. Toxicogenomics has proven to be a useful method to elucidate the underlying mechanisms of chemical-induced toxicities, especially when looking on pathway or co-regulated gene network level. We have developed the interactive TXG-MAPr tool, by applying weighted gene co-expression network analysis (WGCNA) on in vitro liver and kidney datasets of primary human hepatocytes (PHH), HepG2, and RPTEC/TERT1 cells, as well as in vivo rat liver and kidney data. The TXG-MAPr allows visualization of dose- and time-response data, compound correlation, and functional annotation of gene networks (gene-ontology, pathway, transcription factor enrichment) to derive mechanistic information on mode-of-action. Perturbations of WGCNA gene networks (modules) were quantitatively assessed by module eigengene scores (EGs). These module EGs were associated with pathology phenotypes, providing prognostic information for chemical safety assessment. In addition, new module EGs can be obtained by uploading transcriptomics data into the TXG-MAPr tool, which can be applied to investigate novel mechanisms of toxicities by chemical insults. Benchmark concentrations (BMCs) can be derived from co-expression modules and can be used as transcriptomic point of departure (tPOD) of co-expression networks or associated pathways. Finally, module preservation between test systems could identify networks that are preserved in vitro and are associated to an in vivo pathology. These modules could be mapped to key events in an adverse outcome pathway, which could assist in hazard identification for NGRA purposes. We are currently expanding the TXG-MAPr tool to a broad panel of liver, kidney, lung, and neuronal test systems to extend the application of the TXG-MAPr toolbox for NAM-based NGRA, supported by transcriptomics data. In conclusion, the TXG-MAPr represents an innovative and powerful tool that contributes to NGRA by providing mechanistic understanding of potential adverse chemical reactions, and to determine the tPOD of key events that are associated with cellular adversities.

This project has received funding from the EC Horizon2020 EUToxRisk project (grant number 681002), the EC Horizon2020 RISK-HUNT3R project (grant number 964537; part of the ASPIS cluster), the TXG-MAP project supported by the European Food Safety Authority (EFSA), the EC Horizon Europe PARC project: The European Partnership for the Assessment of Risk from Chemicals (grant number 101057014), Cosmetics Europe the European Chemical Industry Council (CEFIC) as part of the Long Range Science Strategy (LRSS) programme (project AIMT10), the Virtual Human Platform for safety assessment (VHP4SAFETY) project (grant number 1292.19.272) and the EU-EFPIA Innovative Medicines Initiative 2 (IMI2) Joint Undertaking TransQST project (grant number 116030) and eTRANSAFE project (grant number 777365). This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation program and EFPIA.