Jun 18, 2025 | 2025, Journal of Chemical Information and Modeling, Journal publications
Abstract
Effective drug safety assessment, guided by the 3R principle (Replacement, Reduction, Refinement) to minimize animal testing, is critical in early drug development. Drug-induced liver injury (DILI), particularly drug-induced cholestasis (DIC), remains a major challenge. This study introduces a computational method for predicting DIC by integrating PubChem substructure fingerprints with biological data from liver-expressed targets and pathways, alongside nine hepatic transporter inhibition models. To address class imbalance in the public cholestasis data set, we employed undersampling, a technique that constructs a small and robust consensus model by evaluating distinct subsets. The most effective baseline model, which combined PubChem substructure fingerprints, pathway data and hepatic transporter inhibition predictions, achieved a Matthews correlation coefficient (MCC) of 0.29 and a sensitivity of 0.79, as validated through 10-fold cross-validation. Subsequently, target prediction using four publicly available tools was employed to enrich the sparse compound-target interaction matrix. Although this approach showed lower sensitivity compared to experimentally derived targets and pathways, it highlighted the value of incorporating specific systems biology related information. Feature importance analysis identified albumin as a potential target linked to cholestasis within our predictive model, suggesting a connection worth further investigation. By employing an expanded consensus model and applying probability range filtering, the refined method achieved an MCC of 0.38 and a sensitivity of 0.80, thereby enhancing decision-making confidence. This approach advances DIC prediction by integrating biological and chemical descriptors, offering a reliable and explainable model.
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.
Apr 7, 2025 | 2024, Heliyon, Journal publications
Abstract
Various groups of chemicals that we encounter in every-day life are known to disrupt the endocrine system, such as estrogen mimics that can disturb normal cellular development and homeostasis. To understand the effect of estrogen on intracellular protein dynamics and how this relates to cell proliferation, we aimed to develop a quantitative description of transcription factor complexes and their regulation of cell cycle progression in response to estrogenic stimulation. We designed a mathematical model that describes the dynamics of three proteins, GREB1, PR and TFF1, that are transcriptionally activated upon binding of 17β-estradiol (E2) to estrogen receptor alpha (ERα). Calibration of this model to imaging data monitoring the expression dynamics of these proteins in MCF7 cells suggests that transcriptional activation of GREB1 and PR depends on the association of the E2-ERα complex with both GREB1 and PR. We subsequently combined this ER signaling model with a previously published cell cycle model and compared this to quantification of cell cycle durations in MCF7 cells following nuclei tracking based on images segmented with deep neural networks. The resulting model predicts the effect of GREB1 and PR knockdown on cell cycle progression, thus providing mechanistic insight in the molecular interactions between ERα-regulated proteins and their relation to cell cycle progression. Our findings form a valuable basis to further investigate the pharmacodynamics of endocrine disrupting chemicals and their influence on cellular behavior.
Apr 4, 2025 | 2025, Altex, Journal publications
Abstract
Toxicological test methods generate raw data and provide instructions on how to use these to determine a final outcome such as a classification of test compounds as hits or non-hits. The data processing pipeline provided in the test method description is often highly complex. Usually, multiple layers of data, ranging from a machine-generated output to the final hit definition, are considered. Transition between each of these layers often requires several data processing steps. As changes in any of these processing steps can impact the final output of new approach methods (NAMs), the processing pipeline is an essential part of a NAM description and should be included in reporting templates such as the ToxTemp. The same raw data, processed in different ways, may result in different final outcomes that may affect the readiness status and regulatory acceptance of the NAM, as an altered output can affect robustness, performance, and relevance. Data management, processing, and interpretation are therefore important elements of a comprehensive NAM definition. We aim to give an overview of the most important data levels to be considered during the development and application of a NAM. In addition, we illustrate data processing and evaluation steps between these data levels. As NAMs are increasingly standard components of the spectrum of toxicological test methods used for risk assessment, awareness of the significance of data processing steps in NAMs is crucial for building trust, ensuring acceptance, and fostering the reproducibility of NAM outcomes.
Apr 4, 2025 | 2025, Environmental Toxicology and Chemistry, Journal publications
Abstract
Since the release of the U.S. National Academy’s report calling for toxicology to evolve from an observation-based to a mechanism-based science (National Research Council, 2007), scientific advances have shown that mechanistic approaches provide a deeper understanding of hazards associated with chemical exposures. New approach methodologies (NAMs) have emerged to assess the hazards and risks associated with exposure to anthropogenic and/or nonanthropogenic stressors within the context of reduce, refine, and replace (the 3Rs). Replacement refers to achieving a research goal without using animals. Reduction means applying methods that allow an investigator to obtain comparable information and precision using fewer animals. Refinement refers to changes in procedures that decrease or eliminate the animals’ pain, stress, and discomfort both during experimental procedures and in their daily social and physical environments (Russell & Burch, 1959). The development, acceptance and implementation of NAMs has become an international priority for human health and that of wildlife and ecosystems. The global commitment to nonanimal research is driven by societal values on animal welfare and the uncertainty of mammalian model species as reliable human surrogates. In addition, NAM-based information can potentially unite the different branches of toxicology by its relevance in protecting human health, wildlife, and ecosystems, thereby contributing to public safety, ecological resilience, and sustainability.
Apr 4, 2025 | 2024, Cells, Journal publications
Abstract
Cell-based test methods with a phenotypic readout are frequently used for toxicity screening. However, guidance on how to validate the hits and how to integrate this information with other data for purposes of risk assessment is missing. We present here such a procedure and exemplify it with a case study on neural crest cell (NCC)-based developmental toxicity of picoxystrobin. A library of potential environmental toxicants was screened in the UKN2 assay, which simultaneously measures migration and cytotoxicity in NCC. Several strobilurin fungicides, known as inhibitors of the mitochondrial respiratory chain complex III, emerged as specific hits. From these, picoxystrobin was chosen to exemplify a roadmap leading from cell-based testing towards toxicological predictions. Following a stringent confirmatory testing, an adverse outcome pathway was developed to provide a testable toxicity hypothesis. Mechanistic studies showed that the oxygen consumption rate was inhibited at sub-µM picoxystrobin concentrations after a 24 h pre-exposure. Migration was inhibited in the 100 nM range, under assay conditions forcing cells to rely on mitochondria. Biokinetic modeling was used to predict intracellular concentrations. Assuming an oral intake of picoxystrobin, consistent with the acceptable daily intake level, physiologically based kinetic modeling suggested that brain concentrations of 0.1–1 µM may be reached. Using this broad array of hazard and toxicokinetics data, we calculated a margin of exposure ≥ 80 between the lowest in vitro point of departure and the highest predicted tissue concentration. Thus, our study exemplifies a hit follow-up strategy and contributes to paving the way to next-generation risk assessment.
