Toward Nano-Specific In Silico NAMs: How to Adjust Nano-QSAR to the Recent Advancements of Nanotoxicology?

Nanoparticle-Mediated Embryotoxicity: Mechanisms of Chemical Toxicity and Implications for Biological Development

Nanoparticles, defined by their nanoscale dimensions and unique physicochemical properties, are widely utilized in healthcare, electronics, environmental sciences, and consumer products. However, increasing evidence of their potential embryotoxic effects during pregnancy underscores the need for a molecular-level understanding of their interactions during embryonic development. Nanoparticles such as titanium dioxide, silver, cerium oxide, copper oxide, and quantum dots can cross the placental barrier and interfere with crucial developmental processes. At the molecular level, they disrupt signaling pathways like Wnt and Hedgehog, induce oxidative stress and inflammation, and cause genotoxic effects, all critical during sensitive phases, such as organogenesis. Furthermore, these nanoparticles interact directly with cellular components, including DNA, proteins, and lipids, impairing cellular function and viability. Innovative strategies to mitigate nanoparticle toxicity, such as surface modifications and incorporation of biocompatible coatings, are discussed as potential solutions to reduce adverse molecular interactions. Various laboratory animal models used to investigate nanoparticle-induced embryotoxicity are evaluated for their efficacy and limitations, providing insights into their applicability for understanding these effects. This Account examines the molecular mechanisms by which nanoparticles compromise embryonic development and emphasizes the importance of designing safer nanoparticles to minimize maternal-fetal exposure risks, particularly in biomedical applications.

The role of machine learning in predicting titanium dioxide nanoparticles induced pulmonary pathology using transcriptomic biomarkers

This study explores the application of machine learning (ML) in identifying transcriptomic changes associated with pulmonary pathologies induced by titanium dioxide nanoparticles (TiO2-NPs). Such an approach significantly contributes to understanding the underlying mode-of-action of TiO2-NP inhalation and follows the European Chemicals Agency's recommendations on applying Novel Approach Methodologies designed for reducing animal studies. The lung gene expression profiles from mice exposed via single intratracheal instillations to TiO2-NPs with varying physicochemical properties on day 1, and day 28 post-exposure were analyzed to develop computational models for predicting the lung pathologies of rutile TiO2-NPs. More than 600 random forest models were generated and rigorously validated, leading to the identification of 17 high-quality models with an average accuracy of 0.95. These models link nanoparticle-deposited surface area, charge, and post-exposure sampling time with dysregulation in key genes, including serum amyloid Saa1 (59.7-fold increase), Saa3 (253.7-fold increase), and the cytokine Ccl2 (3.4-fold increase). These genes are strongly associated with lung inflammation and fibrosis, key pathological responses to nanomaterial exposure. The study highlights critical nanoparticle features that drive transcriptomic changes. Hierarchical clustering confirmed the mechanistic links between nanoparticle properties and transcriptomic changes. This study demonstrates ML's potential to integrate omics data for nanosafety, offering a robust framework for early detection of adverse effects. The models enable the prediction of gene expression changes based on nanoparticle features, aiding in potential Safe and Sustainable-by-design of nanomaterials.

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.

A roadmap towards safe and sustainable by design nanotechnology: Implementation for nano-silver-based antimicrobial textile coatings production by ASINA project

This report demonstrates a case study within the ASINA project, aimed at instantiating a roadmap with quantitative metrics for Safe(r) and (more) Sustainable by Design (SSbD) options. We begin with a description of ASINA's methodology across the product lifecycle, outlining the quantitative elements within: Physical-Chemical Features (PCFs), Key Decision Factors (KDFs), and Key Performance Indicators (KPIs). Subsequently, we delve in a proposed decision support tool for implementing the SSbD objectives across various dimensions-functionality, cost, environment, and human health safety-within a broader European context. We then provide an overview of the technical processes involved, including design rationales, experimental procedures, and tools/models developed within ASINA in delivering nano-silver-based antimicrobial textile coatings. The result is pragmatic, actionable metrics intended to be estimated and assessed in future SSbD applications and to be adopted in a common SSbD roadmap aligned with the EU's Green Deal objectives. The methodological approach is transparently and thoroughly described to inform similar projects through the integration of KPIs into SSbD and foster data-driven decision-making. Specific results and project data are beyond this work's scope, which is to demonstrate the ASINA roadmap and thus foster SSbD-oriented innovation in nanotechnology.

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.

Harmonization Risks and Rewards: Nano-QSAR for Agricultural Nanomaterials

This comprehensive review explores the emerging landscape of Nano-QSAR (quantitative structure-activity relationship) for assessing the risk and potency of nanomaterials in agricultural settings. The paper begins with an introduction to Nano-QSAR, providing background and rationale, and explicitly states the hypotheses guiding the review. The study navigates through various dimensions of nanomaterial applications in agriculture, encompassing their diverse properties, types, and associated challenges. Delving into the principles of QSAR in nanotoxicology, this article elucidates its application in evaluating the safety of nanomaterials, while addressing the unique limitations posed by these materials. The narrative then transitions to the progression of Nano-QSAR in the context of agricultural nanomaterials, exemplified by insightful case studies that highlight both the strengths and the limitations inherent in this methodology. Emerging prospects and hurdles tied to Nano-QSAR in agriculture are rigorously examined, casting light on important pathways forward, existing constraints, and avenues for research enhancement. Culminating in a synthesis of key insights, the review underscores the significance of Nano-QSAR in shaping the future of nanoenabled agriculture. It provides strategic guidance to steer forthcoming research endeavors in this dynamic field.