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Causal, Predictive or Observational? Different Understandings of Key Event Relationships for Adverse Outcome Pathways and their implications on practice

Apr 7, 2025 | 2025, Environmental Toxicology and Pharmacology, Journal publications

Abstract

The Adverse Outcome Pathways (AOPs) framework is pivotal in toxicology, but the, terminology describing Key Event Relationships (KERs) varies within AOP guidelines.This study examined the usage of causal, observational and predictive terms in AOP, documentation and their adaptation in AOP development. A literature search and text, analysis of key AOP guidance documents revealed nuanced usage of these terms, with KERs often described as both causal and predictive. The adaptation of, terminology varies across AOP development stages. Evaluation of KER causality often, relies targeted blocking experiments and weight-of-evidence assessments in the, putative and qualitative stages. Our findings highlight a potential mismatch between,terminology in guidelines and methodologies in practice, particularly in inferring,causality from predictive models. We argue for careful consideration of terms like, causal and essential to facilitate interdisciplinary communication. Furthermore, integrating known causality into quantitative AOP models remains a challenge.

Bioactivity Descriptors for in Vivo Toxicity Prediction: Now and the Future

Jun 12, 2024 | 2024, Expert Opinion on Drug Metabolism & Toxicology, Journal publications

Abstract

Development of a new drug currently takes 10–12 years with costs of around 2 billion EUR. The two main reasons for failures comprise lack of efficacy and unforeseen toxicity. For the latter, a standard process pursued to minimize the risk is the so-called toxicological read across. Briefly, toxicologists query the available literature and databases for compounds, which are structurally similar to their development candidate in order to retrieve information on potential hazards. In addition, computational models might be applied which are either trained for a single protein, such as hERG or P-glycoprotein, or for a respective in vivo endpoint (cholestasis, steatosis, drug-induced liver injury (DILI), ….). In both cases, a proper ‘description’ of the compound of interest is key for the predictive ability of the models. In the following editorial, we will highlight a few general approaches for compound description with a focus on bioactivity-based characterization of compounds.

Identification of the bacterial metabolite aerugine as potential trigger of human dopaminergic neurodegeneration

Feb 7, 2024 | 2023, Environment International, Journal publications

Abstract

The causes of nigrostriatal cell death in idiopathic Parkinson’s disease are unknown, but exposure to toxic chemicals may play some role. We followed up here on suggestions that bacterial secondary metabolites might be selectively cytotoxic to dopaminergic neurons. Extracts from Streptomyces venezuelae were found to kill human dopaminergic neurons (LUHMES cells). Utilizing this model system as a bioassay, we identified a bacterial metabolite known as aerugine (C10H11NO2S; 2-[4-(hydroxymethyl)-4,5-dihydro-1,3-thiazol-2-yl]phenol) and confirmed this finding by chemical re-synthesis. This 2-hydroxyphenyl-thiazoline compound was previously shown to be a product of a wide-spread biosynthetic cluster also found in the human microbiome and in several pathogens. Aerugine triggered half-maximal dopaminergic neurotoxicity at 3-4 µM. It was less toxic for other neurons (10-20 µM), and non-toxic (at <100 µM) for common human cell lines. Neurotoxicity was completely prevented by several iron chelators, by distinct anti-oxidants and by a caspase inhibitor. In the Caenorhabditis elegans model organism, general survival was not affected by aerugine concentrations up to 100 µM. When transgenic worms, expressing green fluorescent protein only in their dopamine neurons, were exposed to aerugine, specific neurodegeneration was observed. The toxicant also exerted functional dopaminergic toxicity in nematodes as determined by the “basal slowing response” assay. Thus, our research has unveiled a bacterial metabolite with a remarkably selective toxicity toward human dopaminergic neurons in vitro and for the dopaminergic nervous system of Caenorhabditis elegans in vivo. These findings suggest that microbe-derived environmental chemicals should be further investigated for their role in the pathogenesis of Parkinson’s disease.

Collaborative SAR Modeling and Prospective In Vitro Validation of Oxidative Stress Activation in Human HepG2 Cells

Feb 7, 2024 | 2023, Journal of Chemical Information and Modeling, Journal publications

Abstract

Oxidative stress is the consequence of an abnormal increase of reactive oxygen species (ROS). ROS are generated mainly during the metabolism in both normal and pathological conditions as well as from exposure to xenobiotics. Xenobiotics can, on the one hand, disrupt molecular machinery involved in redox processes and, on the other hand, reduce the effectiveness of the antioxidant activity. Such dysregulation may lead to oxidative damage when combined with oxidative stress overpassing the cell capacity to detoxify ROS. In this work, a green fluorescent protein (GFP)-tagged nuclear factor erythroid 2-related factor 2 (NRF2)-regulated sulfiredoxin reporter (Srxn1-GFP) was used to measure the antioxidant response of HepG2 cells to a large series of drug and drug-like compounds (2230 compounds). These compounds were then classified as positive or negative depending on cellular response and distributed among different modeling groups to establish structure-activity relationship (SAR) models. A selection of models was used to prospectively predict oxidative stress induced by a new set of compounds subsequently experimentally tested to validate the model predictions. Altogether, this exercise exemplifies the different challenges of developing SAR models of a phenotypic cellular readout, model combination, chemical space selection, and results interpretation.

Identifying multi-scale translational safety biomarkers using a network-based systems approach

Dec 13, 2023 | 2023, iScience, Journal publications

Abstract

Animal testing is the current standard for drug and chemicals safety assessment, but hazards translation to human is uncertain. Human in vitro models can address the species translation but might not replicate in vivo complexity. Herein, we propose a network-based method addressing these translational multiscale problems that derives in vivo liver injury biomarkers applicable to in vitro human early safety screening. We applied weighted correlation network analysis (WGCNA) to a large rat liver transcriptomic dataset to obtain co-regulated gene clusters (modules). We identified modules statistically associated with liver pathologies, including a module enriched for ATF4-regulated genes as associated with the occurrence of hepatocellular single-cell necrosis, and as preserved in human liver in vitro models. Within the module, we identified TRIB3 and MTHFD2 as a novel candidate stress biomarkers, and developed and used BAC-eGFPHepG2 reporters in a compound screening, identifying compounds showing ATF4-dependent stress response and potential early safety signals.