Marije Niemeijer is assistant professor at the Cellular systems and Drug Safety division of the Leiden Academic Centre for Drug Research (LACDR) at Leiden University. She received her Masters degree cum laude in Bio-pharmaceutical Sciences with the specialization Toxicology at Leiden University. Thereafter, she did her PhD at the department of Toxicology of LACDR at Leiden University. Here, she studied the regulation and inter-individual variability of stress response signalling upon chemical exposure by combining siRNA screening, high-content imaging and transcriptomic approaches. As a post-doctoral researcher, she focused on the application of 3D liver models for toxicity testing within the Horizon2020 EU-ToxRisk and PATROLS project. Now as assistant professor, she aims to dissect molecular mechanisms of drug action in both the context of drug efficacy as well as toxicity using advanced in vitro test systems that are fit for this purpose. Through omics approaches, affected genes or networks for different areas within the chemical space or for specific pathologies can be identified and used as reporter genes. To further refine chemical risk assessment, she aims to characterize the inter-individual variability in the activation of these critical gene networks upon chemical exposure.
Human population variability of toxicodynamics driving adverse responses in hepatocytes
Marije Niemeijer1, Natasha Tahir1, Imke Bruns1, Eveline in ‘t Veld5, Lisa van Schijndel5, Diana Pereira5, Jannik Rousel5, Nadia Quignot2, Shuai Fu2, Lea Bredsdorff6, Annika Boye Petersen6, Susanne Hougaard Bennekou6, Witold Wiecek2, Suzanna Huppelschoten1, Peter Bouwman1, Audrey Baze3, Céline Parmentier3, Lysiane Richert3, Richard S. Paules4, Giulia Callegaro1, Martijn Moné1, Sylvia Le Dévédec1, Matthijs Moerland5, Frederic Y. Bois2 and Bob van de Water1*
1Division of Drug Discovery and Safety, LACDR, Leiden University, The Netherlands
2CERTARA, Paris, France
3KaLy-Cell, Plobsheim, France
4Division of the National Toxicology Program, NIEHS, NIH, Research Triangle Park, NC
5Centre for Human Drug Research, The Netherlands
6DTU-Food, The National Food Institute, Denmark
There is a need to improve chemical safety testing and risk assessment by accurately taking into account inter-individual variability in toxicodynamic responses. Currently to account for inter-individual variability, standard uncertainty factors (UFs) are used. However, current understanding of inter-individual variability for toxicodynamics specifically remains limited. Therefore, it is key to map the inter-individual variability in activation of toxicity-related pathways thereby improving drug toxicity screening strategies and accurately define data-driven UFs to account for this variability. Here, in a high-throughput fashion we profiled the transcriptome of a panel of 50 cryo-preserved primary human hepatocytes (PHHs) derived from different individuals exposed for 8 or 24 h to a broad concentration range of specific stress response inducers. The variance in the concentration-dependent stress response activation among individuals could be captured, where the average of benchmark concentrations had a maximum difference of 864, 13, 13 and 259-fold between different hepatocytes for the unfolded protein response, oxidative stress, DNA damage and NF-κB signaling-related genes, respectively. Human population modeling revealed that small panel sizes systematically under-estimated the variance and resulted in low probabilities in estimating the correct variance for the human population. Moreover, estimated toxicodynamic variability factors were up to 2-fold higher than the standard uncertainty factor of 3.16 to account for population variability during risk assessment. Next, the toxicodynamic transcriptomic variability between peripheral blood mononuclear cells derived from a large panel of healthy volunteers for different age groups, sex and ethnicities, upon chemical exposures will be assessed. Variability in weighted co-regulated gene networks will be identified and combined with human population modeling to define data-driven safety factors for specific stress response networks. Overall, by combining high-throughput transcriptome analysis and population modelling, improved understanding of inter-individual variability in stress response activation using PHHs could be established, thereby contributing towards improved prediction of adverse outcome.
Supported by the TD-TRAQ project funded by European Food Safety Authority (OC/EFSA/SCER/2021/03), EU-ToxRisk project funded by the European Union under the Horizon 2020 programme (grant agreement 681002), IMI MIP-DILI project (grant agreement 115336) and Division of the National Toxicology Program at NIEHS, NIH, USA (ZIA ES103318-03).
