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Mapping out strategies to further develop human-relevant, new approach methodology (NAM)-based developmental neurotoxicity (DNT) testing

Apr 4, 2025 | 2025, Altex, Journal publications

Abstract

On occasion of the DNT5 meeting in Konstanz, Germany (April-2024), participants brainstormed on future challenges concerning a regulatory implementation of the developmental neurotoxicity (DNT) in vitro test battery (DNT-IVB). The five discussion topics below outline some of the key issues, opportunities and research directions for the next several years: (1) How to contextualize DNT hazard with information on potential maternal toxicity or other toxicity domains (non-DNT)? Several approaches on how to use cytotoxicity data from NAMs were discussed. (2) What opportunities exist for an immediate or near-future application of the DNT-IVB, e.g. as a prioritisation step or add-on to other information? Initial examples are already emerging; the data can be used even if the battery is not converted to a defined approach. (3) How to establish data interpretation procedures for multi-dimensional endpoints that reduce dimensionality and are suitable for classification? A decision framework  is required on how to use the DNT-IVB in a regulatory context. Machine-learning (AI-approaches) may provide novel classification models. (4) How can a battery of molecular initiating events (MIEs) be smartly linked to the DNT-IVB? At what tier of an overall strategy would MIEs be evaluated, and how would one optimally balance cost vs information yield. (5) What is the way forward to scientific validation of DNT NAMs and the DNT-IVB? A large set of animal data would be required for conventional approaches, while mechanistic information may establish relevance in other ways.

Biology-inspired dynamic microphysiological system approaches to revolutionize basic research, healthcare and animal welfare

Apr 4, 2025 | 2025, Altex, Journal publications

Abstract

The regular t4 workshops on biology-inspired microphysiological systems (MPS) have become a reliable benchmark for assessing fundamental scientific, industrial, and regulatory trends in the MPS field. The 2023 workshop participants concluded that MPS technology as used in academia has matured significantly, as evidenced by the steadily increasing number of high-quality research publications, but that broad industrial adoption of MPS has been slow. Academic research using MPS is primarily aimed at accurately recapitulating human biology in MPS-based organ models to enable breakthrough discoveries. Examples of these developments are summarized in the report. In addition, we focus on key challenges identified during the previous workshop. Bridging gaps between academia, regulators, and industry is addressed. We also comment on overcoming barriers to trust and acceptance of MPS-derived data – the latter being particularly important in a regulatory environment. The status of implementation of the recommendations detailed in the 2020 report has been reviewed. It was concluded that communication between stakeholders has improved significantly, while the recommendations related to regulatory acceptance still need to be implemented. Participants noted that the remaining challenges for increased translation of these technologies into industrial use and regulatory decision-making will require further efforts on well-defined context of use qualifications, together with increased standardization. This will make MPS data more reliable and ultimately make these novel tools more economically sustainable. The long-term roadmap from the 2015 workshop was critically reviewed and updated. Recommendations for the next period and an outlook conclude the report.

Assessment of pulmonary fibrosis using weighted gene co-expression network analysis

Nov 19, 2024 | 2024, Frontiers in Toxicology, Journal publications

Abstract

For many industrial chemicals toxicological data is sparse regarding several regulatory endpoints, so there is a high and often unmet demand for NAMs that allow for screening and prioritization of these chemicals. In this proof of concept case study we propose multi-gene biomarkers of compounds’ ability to induce lung fibrosis and demonstrate their application in vitro. For deriving these biomarkers we used weighted gene co-expression network analysis to reanalyze a study where the time-dependent pulmonary gene-expression in mice treated with bleomycin had been documented. We identified eight modules of 58 to 273 genes each which were particularly activated during the different phases (inflammatory; acute and late fibrotic) of the developing fibrosis. The modules’ relation to lung fibrosis was substantiated by comparison to known markers of lung fibrosis from DisGenet. Finally, we show the modules’ application as biomarkers of chemical inducers of lung fibrosis based on an in vitro study of four diketones. Clear differences could be found between the lung fibrosis inducing diketones and other compounds with regard to their tendency to induce dose-dependent increases of module activation as determined using a previously proposed differential activation score and the fraction of differentially expressed genes in the modules. Accordingly, this study highlights the potential use of composite biomarkers mechanistic screening for compound-induced lung fibrosis.

Computational Strategies for Assessing Adverse Outcome Pathways: Hepatic Steatosis as a Case Study

Nov 19, 2024 | 2024, International Journal of Molecular Sciences, Journal publications

Abstract

The evolving landscape of chemical risk assessment is increasingly focused on developing tiered, mechanistically driven approaches that avoid the use of animal experiments. In this context, adverse outcome pathways have gained importance for evaluating various types of chemical-induced toxicity. Using hepatic steatosis as a case study, this review explores the use of diverse computational techniques, such as structure–activity relationship models, quantitative structure–activity relationship models, read-across methods, omics data analysis, and structure-based approaches to fill data gaps within adverse outcome pathway networks. Emphasizing the regulatory acceptance of each technique, we examine how these methodologies can be integrated to provide a comprehensive understanding of chemical toxicity. This review highlights the transformative impact of in silico techniques in toxicology, proposing guidelines for their application in evidence gathering for developing and filling data gaps in adverse outcome pathway networks. These guidelines can be applied to other cases, advancing the field of toxicological risk assessment.

MolCompass: multi-tool for the navigation in chemical space and visual validation of QSAR/QSPR models

Nov 19, 2024 | 2024, Journal of Cheminformatics, Journal publications

Abstract

The exponential growth of data is challenging for humans because their ability to analyze data is limited. Especially in chemistry, there is a demand for tools that can visualize molecular datasets in a convenient graphical way. We propose a new, ready-to-use, multi-tool, and open-source framework for visualizing and navigating chemical space. This framework adheres to the low-code/no-code (LCNC) paradigm, providing a KNIME node, a web-based tool, and a Python package, making it accessible to a broad cheminformatics community. The core technique of the MolCompass framework employs a pre-trained parametric t-SNE model. We demonstrate how this framework can be adapted for the visualisation of chemical space and visual validation of binary classification QSAR/QSPR models, revealing their weaknesses and identifying model cliffs. All parts of the framework are publicly available on GitHub, providing accessibility to the broad scientific community.

PathwayNexus: a tool for interactive metabolic data analysis

Nov 19, 2024 | 2024, Bioinformatics, Journal publications

Abstract

Motivation

High-throughput omics methods increasingly result in large datasets including metabolomics data, which are often difficult to analyse.

Results

To help researchers to handle and analyse those datasets by mapping and investigating metabolomics data of multiple sampling conditions (e.g. different time points or treatments) in the context of pathways, PathwayNexus has been developed, which presents the mapping results in a matrix format, allowing users to easily observe the relations between the compounds and the pathways. It also offers functionalities like ranking, sorting, clustering, pathway views, and further analytical tools. Its primary objective is to condense large sets of pathways into smaller, more relevant subsets that align with the specific interests of the user.