Toxicogenomics Data for Chemical Safety Assessment and Development of New Approach Methodologies: An Adverse Outcome Pathway-Based Approach

Developmental toxicity: artificial intelligence-powered assessments

Regulatory agencies require comprehensive toxicity testing for prenatal drug exposure, including new drugs in development, to reduce concerns about developmental toxicity, that is, drug-induced toxicity and adverse effects in pregnant women and fetuses. However, defining developmental toxicity endpoints and optimal analysis of associated public big data remain challenging. Recently, artificial intelligence (AI) approaches have had a critical role in analyzing complex, high-dimensional data, uncovering subtle relationships between chemical exposures and associated developmental risks. Here, we present an overview of major big data resources and data-driven models that focus on predicting various toxicity endpoints. We also highlight emerging, interpretable AI models that integrate multimodal data and domain knowledge to reveal toxic mechanisms underlying complex endpoints, and outline a potential framework that leverages multiple interpretable models to comprehensively evaluate chemical-induced developmental toxicity.

An update on knowledge graphs and their current and potential applications in drug discovery

INTRODUCTION

Knowledge graphs are becoming prominent tools in computational drug discovery. They effectively integrate heterogeneous biomedical data and generate new hypotheses and knowledge.

AREAS COVERED

This article is based on a literature review using Google Scholar and PubMed to retrieve articles on existing knowledge graphs relevant to the drug discovery field. The authors compare the types of entities, relationships, and data sources they encompass. Additionally, the authors provide examples of their use in the drug discovery field and discuss potential strategies for advancing this research area.

EXPERT OPINION

Knowledge graphs are crucial in drug discovery, but their construction leads to challenges in data integration and consistency. Future research should prioritize the standardization of data sources and data modeling. More efforts are needed for the integration in knowledge graphs of diverse data types, such as chemical structures and epigenetic data, to enhance their effectiveness. Additionally, advancements in large language models should be pursued to aid the development of knowledge graphs, provide intuitive querying capabilities for non-expert users, and explain knowledge graphs -derived predictions, thereby making these tools more accessible and their insights more interpretable for a wider audience.

INSIGHT: An integrated framework for safe and sustainable chemical and material assessment

The assessment of chemicals and materials has traditionally been fragmented, with health, environmental, social, and economic impacts evaluated independently. This disjointed approach limits the ability to capture trade-offs and synergies necessary for comprehensive decision-making under the Safe and Sustainable by Design (SSbD) framework. The EU INSIGHT project addresses this challenge by developing a novel computational framework for integrated impact assessment, based on the Impact Outcome Pathway (IOP) approach. Extending the Adverse Outcome Pathway (AOP) concept, IOPs establish mechanistic links between chemical and material properties and their environmental, health, and socio-economic consequences. The project integrates multi-source datasets (including omics, life cycle inventories, and exposure models) into a structured knowledge graph (KG), ensuring FAIR (Findable, Accessible, Interoperable, Reusable) data principles are met. INSIGHT is being developed and validated through four case studies targeting per- and polyfluoroalkyl substances (PFAS), graphene oxide (GO), bio-based synthetic amorphous silica (SAS), and antimicrobial coatings. These studies demonstrate how multi-model simulations, decision-support tools, and artificial intelligence-driven knowledge extraction can enhance the predictability and interpretability of chemical and material impacts. Additionally, INSIGHT incorporates interactive, web-based decision maps to provide stakeholders with accessible, regulatory-compliant risk and sustainability assessments. By bridging mechanistic toxicology, exposure modeling, life cycle assessment, and socio-economic analysis, INSIGHT advances a scalable, transparent, and data-driven approach to SSbD. This project aligns with the European Green Deal and global sustainability goals, promoting safer, more sustainable innovation in chemicals and materials through an integrated, mechanistic, and computationally advanced framework.

Using transcriptomics data and Adverse Outcome Pathway networks to explore endocrine disrupting properties of Cadmium and PCB-126

Omics-technologies such as transcriptomics offer valuable insights into toxicity mechanisms. However, integrating this type of data into regulatory frameworks remains challenging due to uncertainties regarding toxicological relevance and links to adverse outcomes. Furthermore, current assessments of endocrine disruptors (EDs) relevant to human health require substantial amounts of data, and primarily rely on standardized animal studies. Identifying EDs is a high priority in the EU, but so are efforts to replace and reduce animal testing. Alternative methods to investigate EDs are needed, and so are health risk assessment methods that support uptake of novel mechanistic information. This study aims to utilize Adverse Outcome Pathways (AOPs) to integrate transcriptomics data for identifying EDs, by establishing a link between molecular data and adverse outcomes. Cadmium (Cd) and 3,3',4,4',5-pentachlorobiphenyl (PCB126) were used as model compounds due to their observed effects on the endocrine system. An AOP network for the estrogen, androgen, thyroid, and steroidogenesis (EATS)-modalities was constructed. RNA sequencing (RNA-Seq) was conducted on zebrafish (Danio rerio) embryos exposed to Cd or PCB126 for 4 days. RNA-Seq data were then linked to the AOP network via Gene Ontology (GO) terms. Enrichment Maps in Cytoscape and the QIAGEN Ingenuity Pathway Analysis (IPA) software were also used to identify potential ED properties and to support the assessment. Potentially EATS-related GO Biological Process (BP) terms were identified for both compounds. A lack of accurate standardized terms in KEs of the AOP network hindered a data-driven mapping approach. Instead, manual mapping of GO BP terms onto the AOP network revealed more connections, underscoring the need for harmonizing AOP development for regulatory use. Both the Enrichment Maps and the IPA results further supported potentially EATS-related effects of both compounds. While AOP networks show promise in integrating RNA-Seq data, several challenges remain.

CompSafeNano project: NanoInformatics approaches for safe-by-design nanomaterials

The CompSafeNano project, a Research and Innovation Staff Exchange (RISE) project funded under the European Union's Horizon 2020 program, aims to advance the safety and innovation potential of nanomaterials (NMs) by integrating cutting-edge nanoinformatics, computational modelling, and predictive toxicology to enable design of safer NMs at the earliest stage of materials development. The project leverages Safe-by-Design (SbD) principles to ensure the development of inherently safer NMs, enhancing both regulatory compliance and international collaboration. By building on established nanoinformatics frameworks, such as those developed in the H2020-funded projects NanoSolveIT and NanoCommons, CompSafeNano addresses critical challenges in nanosafety through development and integration of innovative methodologies, including advanced in vitro models, in silico approaches including machine learning (ML) and artificial intelligence (AI)-driven predictive models and 1st-principles computational modelling of NMs properties, interactions and effects on living systems. Significant progress has been made in generating atomistic and quantum-mechanical descriptors for various NMs, evaluating their interactions with biological systems (from small molecules or metabolites, to proteins, cells, organisms, animals, humans and ecosystems), and in developing predictive models for NMs risk assessment. The CompSafeNano project has also focused on implementing and further standardising data reporting templates and enhancing data management practices, ensuring adherence to the FAIR (Findable, Accessible, Interoperable, Reusable) data principles. Despite challenges, such as limited regulatory acceptance of New Approach Methodologies (NAMs) currently, which has implications for predictive nanosafety assessment, CompSafeNano has successfully developed tools and models that are integral to the safety evaluation of NMs, and that enable the extensive datasets on NMs safety to be utilised for the re-design of NMs that are inherently safer, including through prediction of the acquired biomolecule coronas which provide the biological or environmental identities to NMs, promoting their sustainable use in diverse applications. Future efforts will concentrate on further refining these models, expanding the NanoPharos Database, and working with regulatory stakeholders thereby fostering the widespread adoption of SbD practices across the nanotechnology sector. CompSafeNano's integrative approach, multidisciplinary collaboration and extensive stakeholder engagement, position the project as a critical driver of innovation in NMs SbD methodologies and in the development and implementation of computational nanosafety.

Adverse outcome pathway for the neurotoxicity of Per- and polyfluoroalkyl substances: A systematic review

Per- and polyfluoroalkyl substances (PFAS) are endocrine disruptors with unambiguous neurotoxic effects. However, due to variability in experimental models, population characteristics, and molecular endpoints, the elucidation of mechanisms underlying PFAS-induced neurotoxicity remains incomplete. In this review, we utilized the adverse outcome pathway (AOP) framework, a comprehensive tool for evaluating toxicity across multiple biological levels (molecular, cellular, tissue and organ, individual, and population), to elucidate the mechanisms of neurotoxicity induced by PFAS. Based on 271 studies, the reactive oxygen species (ROS) generation emerged as the molecular initiating event 1 (MIE1). Subsequent key events (KEs) at the cellular level include oxidative stress, neuroinflammation, apoptosis, altered Ca2+ signal transduction, glutamate and dopamine signaling dyshomeostasis, and reduction of cholinergic and serotonin. These KEs culminate in synaptic dysfunction at organ and tissue levels. Further insights were offered into MIE2 and upstream KEs associated with altered thyroid hormone levels, contributing to synaptic dysfunction and hypomyelination at the organ and tissue levels. The inhibition of Na+/I- symporter (NIS) was identified as the MIE2, initiating a cascade of KEs at the cellular level, including altered thyroid hormone synthesis, thyroid hormone transporters, thyroid hormone metabolism, and binding with thyroid hormone receptors. All KEs ultimately result in adverse outcomes (AOs), including cognition and memory impairment, autism spectrum disorders, attention deficit hyperactivity disorders, and neuromotor development impairment. To our knowledge, this review represents the first comprehensive and systematic AOP analysis delineating the intricate mechanisms responsible for PFAS-induced neurotoxic effects, providing valuable insights for risk assessments and mitigation strategies against PFAS-related health hazards.

Toxicogenomic assessment of in vitro macrophages exposed to profibrotic challenge reveals a sustained transcriptomic immune signature

Immune signalling is a crucial component in the progression of fibrosis. However, approaches for the safety assessment of potentially profibrotic substances, that provide information on mechanistic immune responses, are underdeveloped. This study aimed to develop a novel framework for assessing the immunotoxicity of fibrotic compounds. We exposed macrophages in vitro to multiple sublethal concentrations of the profibrotic agent bleomycin, over multiple timepoints, and generated RNA sequencing data. Using a toxicogenomic approach, we performed dose-dependent analysis to discover genes dysregulated by bleomycin exposure in a dose-responsive manner. A subset of immune genes displayed a sustained dose-dependent and differential expression response to profibrotic challenge. An immunoassay revealed cytokines and proteinases responding to bleomycin exposure that closely correlated to transcriptomic alterations, underscoring the integration between transcriptional immune response and external immune signalling activity. This study not only increases our understanding of the immunological mechanisms of fibrosis, but also offers an innovative framework for the toxicological evaluation of substances with potential fibrogenic effects on macrophage signalling. Our work brings a new immunotoxicogenomic direction for hazard assessment of fibrotic compounds, through the implementation of a time and resource efficient in vitro methodology.

A Network Toxicology Approach for Mechanistic Modelling of Nanomaterial Hazard and Adverse Outcomes

Hazard assessment is the first step in evaluating the potential adverse effects of chemicals. Traditionally, toxicological assessment has focused on the exposure, overlooking the impact of the exposed system on the observed toxicity. However, systems toxicology emphasizes how system properties significantly contribute to the observed response. Hence, systems theory states that interactions store more information than individual elements, leading to the adoption of network based models to represent complex systems in many fields of life sciences. Here, they develop a network-based approach to characterize toxicological responses in the context of a biological system, inferring biological system specific networks. They directly link molecular alterations to the adverse outcome pathway (AOP) framework, establishing direct connections between omics data and toxicologically relevant phenotypic events. They apply this framework to a dataset including 31 engineered nanomaterials with different physicochemical properties in two different in vitro and one in vivo models and demonstrate how the biological system is the driving force of the observed response. This work highlights the potential of network-based methods to significantly improve their understanding of toxicological mechanisms from a systems biology perspective and provides relevant considerations and future data-driven approaches for the hazard assessment of nanomaterials and other advanced materials.

Bioinformatics and machine learning to support nanomaterial grouping

Nanomaterials (NMs) offer plenty of novel functionalities. Moreover, their physicochemical properties can be fine-tuned to meet the needs of specific applications, leading to virtually unlimited numbers of NM variants. Hence, efficient hazard and risk assessment strategies building on New Approach Methodologies (NAMs) become indispensable. Indeed, the design, the development and implementation of NAMs has been a major topic in a substantial number of research projects. One of the promising strategies that can help to deal with the high number of NMs variants is grouping and read-across. Based on demonstrated structural and physicochemical similarity, NMs can be grouped and assessed together. Within an established NM group, read-across may be performed to fill in data gaps for data-poor variants using existing data for NMs within the group. Establishing a group requires a sound justification, usually based on a grouping hypothesis that links specific physicochemical properties to well-defined hazard endpoints. However, for NMs these interrelationships are only beginning to be understood. The aim of this review is to demonstrate the power of bioinformatics with a specific focus on Machine Learning (ML) approaches to unravel the NM Modes-of-Action (MoA) and identify the properties that are relevant to specific hazards, in support of grouping strategies. This review emphasizes the following messages: 1) ML supports identification of the most relevant properties contributing to specific hazards; 2) ML supports analysis of large omics datasets and identification of MoA patterns in support of hypothesis formulation in grouping approaches; 3) omics approaches are useful for shifting away from consideration of single endpoints towards a more mechanistic understanding across multiple endpoints gained from one experiment; and 4) approaches from other fields of Artificial Intelligence (AI) like Natural Language Processing or image analysis may support automated extraction and interlinkage of information related to NM toxicity. Here, existing ML models for predicting NM toxicity and for analyzing omics data in support of NM grouping are reviewed. Various challenges related to building robust models in the field of nanotoxicology exist and are also discussed.

Meta-Analysis of Integrated Proteomic and Transcriptomic Data Discerns Structure-Activity Relationship of Carbon Materials with Different Morphologies

The Fiber Pathogenicity Paradigm (FPP) establishes connections between fiber structure, durability, and disease-causing potential observed in materials like asbestos and synthetic fibers. While emerging nanofibers are anticipated to exhibit pathogenic traits according to the FPP, their nanoscale diameter limits rigidity, leading to tangling and loss of fiber characteristics. The absence of validated rigidity measurement methods complicates nanofiber toxicity assessment. By comprehensively analyzing 89 transcriptomics and 37 proteomics studies, this study aims to enhance carbon material toxicity understanding and proposes an alternative strategy to assess morphology-driven toxicity. Carbon materials are categorized as non-fibrous, high aspect ratio with shorter lengths, tangled, and rigid fibers. Mitsui-7 serves as a benchmark for pathogenic fibers. The meta-analysis reveals distinct cellular changes for each category, effectively distinguishing rigid fibers from other carbon materials. Subsequently, a robust random forest model is developed to predict morphology, unveiling the pathogenicity of previously deemed non-pathogenic NM-400 due to its secondary structures. This study fills a crucial gap in nanosafety by linking toxicological effects to material morphology, in particular regarding fibers. It demonstrates the significant impact of morphology on toxicological behavior and the necessity of integrating morphological considerations into regulatory frameworks.

Advancing chemical safety assessment through an omics-based characterization of the test system-chemical interaction

Assessing chemical safety is essential to evaluate the potential risks of chemical exposure to human health and the environment. Traditional methods relying on animal testing are being replaced by 3R (reduction, refinement, and replacement) principle-based alternatives, mainly depending on in vitro test methods and the Adverse Outcome Pathway framework. However, these approaches often focus on the properties of the compound, missing the broader chemical-biological interaction perspective. Currently, the lack of comprehensive molecular characterization of the in vitro test system results in limited real-world representation and contextualization of the toxicological effect under study. Leveraging omics data strengthens the understanding of the responses of different biological systems, emphasizing holistic chemical-biological interactions when developing in vitro methods. Here, we discuss the relevance of meticulous test system characterization on two safety assessment relevant scenarios and how omics-based, data-driven approaches can improve the future generation of alternative methods.

Expanding adverse outcome pathways towards one health models for nanosafety

The ever-growing production of nano-enabled products has generated the need for dedicated risk assessment strategies that ensure safety for humans and the environment. Transdisciplinary approaches are needed to support the development of new technologies while respecting environmental limits, as also highlighted by the EU Green Deal Chemicals Strategy for Sustainability and its safe and sustainable by design (SSbD) framework. The One Health concept offers a holistic multiscale approach for the assessment of nanosafety. However, toxicology is not yet capable of explaining the interaction between chemicals and biological systems at the multiscale level and in the context of the One Health framework. Furthermore, there is a disconnect between chemical safety assessment, epidemiology, and other fields of biology that, if unified, would enable the adoption of the One Health model. The development of mechanistic toxicology and the generation of omics data has provided important biological knowledge of the response of individual biological systems to nanomaterials (NMs). On the other hand, epigenetic data have the potential to inform on interspecies mechanisms of adaptation. These data types, however, need to be linked to concepts that support their intuitive interpretation. Adverse Outcome Pathways (AOPs) represent an evolving framework to anchor existing knowledge to chemical risk assessment. In this perspective, we discuss the possibility of integrating multi-level toxicogenomics data, including toxicoepigenetic insights, into the AOP framework. We anticipate that this new direction of toxicogenomics can support the development of One Health models applicable to groups of chemicals and to multiple species in the tree of life.

An ancestral molecular response to nanomaterial particulates

The varied transcriptomic response to nanoparticles has hampered the understanding of the mechanism of action. Here, by performing a meta-analysis of a large collection of transcriptomics data from various engineered nanoparticle exposure studies, we identify common patterns of gene regulation that impact the transcriptomic response. Analysis identifies deregulation of immune functions as a prominent response across different exposure studies. Looking at the promoter regions of these genes, a set of binding sites for zinc finger transcription factors C2H2, involved in cell stress responses, protein misfolding and chromatin remodelling and immunomodulation, is identified. The model can be used to explain the outcomes of mechanism of action and is observed across a range of species indicating this is a conserved part of the innate immune system.

A curated gene and biological system annotation of adverse outcome pathways related to human health

Adverse outcome pathways (AOPs) are emerging as a central framework in modern toxicology and other fields in biomedicine. They serve as an extension of pathway-based concepts by depicting biological mechanisms as causally linked sequences of key events (KEs) from a molecular initiating event (MIE) to an adverse outcome. AOPs guide the use and development of new approach methodologies (NAMs) aimed at reducing animal experimentation. While AOPs model the systemic mechanisms at various levels of biological organisation, toxicogenomics provides the means to study the molecular mechanisms of chemical exposures. Systematic integration of these two concepts would improve the application of AOP-based knowledge while also supporting the interpretation of complex omics data. Hence, we established this link through rigorous curation of molecular annotations for the KEs of human relevant AOPs. We further expanded and consolidated the annotations of the biological context of KEs. These curated annotations pave the way to embed AOPs in molecular data interpretation, facilitating the emergence of new knowledge in biomedicine.