Application of Machine Learning in Nanotoxicology: A Critical Review and Perspective.

The massive production and application of nanomaterials (NMs) have raised concerns about the potential adverse effects of NMs on human health and the environment. Evaluating the adverse effects of NMs by laboratory methods is expensive, time-consuming, and often fails to keep pace with the invention of new materials. Therefore, in silico methods that utilize machine learning techniques to predict the toxicity potentials of NMs are a promising alternative approach if regulatory confidence in them can be enhanced. Previous reviews and regulatory OECD guidance documents have discussed in detail how to build an in silico predictive model for NMs. Nevertheless, there is still room for improvement in addressing the ways to enhance the model representativeness and performance from different angles, such as data set curation, descriptor selection, task type (classification/regression), algorithm choice, and model evaluation (internal and external validation, applicability domain, and mechanistic interpretation, which is key to ensuring stakeholder confidence). This review explores how to build better predictive models; the current state of the art is analyzed via a statistical evaluation of literature, while the challenges faced and future perspectives are summarized. Moreover, a recommended workflow and best practices are provided to help in developing more predictive, reliable, and interpretable models that can assist risk assessment as well as safe-by-design development of NMs.

Leveraging human genetic and adverse outcome pathway (AOP) data to inform susceptibility in human health risk assessment.

Estimation of susceptibility differences in human health risk assessment (HHRA) has been challenged by a lack of available susceptibility and variability data after exposure to a specific environmental chemical or pharmaceutical. With the increasingly large number of available data sources that contain polymorphism and other genetic data, human genetic variability that informs susceptibility can be better incorporated into HHRA. A recent policy, the 2016 The Frank R. Lautenberg Chemical Safety for the twenty-first Century Act, requires the US Environmental Protection Agency to evaluate new and existing toxic chemicals with explicit consideration of susceptible populations of all types (life stage, exposure, genetic, etc.). We propose using the adverse outcome pathway (AOP) construct to organize, identify, and characterize human genetic susceptibility in HHRA. We explore how publicly available human genetic datasets can be used to gain mechanistic understanding of molecular events and characterize human susceptibility for an adverse outcome. We present a computational method that implements publicly available human genetic data to prioritize AOPs with potential for human genetic variability. We describe the application of this approach across multiple described AOPs for health outcomes of interest, and by focusing on a single molecular initiating event. This contributes to a long-term goal to improve estimates of human susceptibility for use in HHRA for single and multiple chemicals.

AOP-DB: A database resource for the exploration of Adverse Outcome Pathways through integrated association networks.

The Adverse Outcome Pathway (AOP) framework describes the progression of a toxicity pathway from molecular perturbation to population-level outcome in a series of measurable, mechanistic responses. The controlled, computer-readable vocabulary that defines an AOP has the ability to, automatically and on a large scale, integrate AOP knowledge with publically available sources of biological high-throughput data and its derived associations. To support the discovery and development of putative (existing) and potential AOPs, we introduce the AOP-DB, an exploratory database resource that aggregates association relationships between genes and their related chemicals, diseases, pathways, species orthology information, ontologies, and gene interactions. These associations are mined from publically available annotation databases and are integrated with the AOP information centralized in the AOP-Wiki, allowing for the automatic characterization of both putative and potential AOPs in the context of multiple areas of biological information, referred to here as "biological entities". The AOP-DB acts as a hypothesis-generation tool for the expansion of putative AOPs, as well as the characterization of potential AOPs, through the creation of association networks across these biological entities. Finally, the AOP-DB provides a useful interface between the AOP framework and existing chemical screening and prioritization efforts by the US Environmental Protection Agency.

Convergent adaptation of human lactase persistence in Africa and Europe.

A SNP in the gene encoding lactase (LCT) (C/T-13910) is associated with the ability to digest milk as adults (lactase persistence) in Europeans, but the genetic basis of lactase persistence in Africans was previously unknown. We conducted a genotype-phenotype association study in 470 Tanzanians, Kenyans and Sudanese and identified three SNPs (G/C-14010, T/G-13915 and C/G-13907) that are associated with lactase persistence and that have derived alleles that significantly enhance transcription from the LCT promoter in vitro. These SNPs originated on different haplotype backgrounds from the European C/T-13910 SNP and from each other. Genotyping across a 3-Mb region demonstrated haplotype homozygosity extending >2.0 Mb on chromosomes carrying C-14010, consistent with a selective sweep over the past approximately 7,000 years. These data provide a marked example of convergent evolution due to strong selective pressure resulting from shared cultural traits-animal domestication and adult milk consumption.

Translating nanoEHS data using EPA NaKnowBase and the resource description framework.

Background: The U.S. Federal Government has supported the generation of extensive amounts of nanomaterials and related nano Environmental Health and Safety (nanoEHS) data, there is a need to make these data available to stakeholders. With recent efforts, a need for improved interoperability, translation, and sustainability of Federal nanoEHS data in the United States has been realized. The NaKnowBase (NKB) is a relational database containing experimental results generated by the EPA Office of Research and Development (ORD) regarding the actions of engineered nanomaterials on environmental and biological systems. Through the interaction of the National Nanotechnology Initiative's Nanotechnology Environmental Health Implications (NEHI) Working Group, and the Database and Informatics Interest Group (DIIG), a U.S. Federal nanoEHS Consortium has been formed. Methods: The primary goal of this consortium is to establish a "common language" for nanoEHS data that aligns with FAIR data standards. A second goal is to overcome nomenclature issues inherent to nanomaterials data, ultimately allowing data sharing and interoperability across the diverse U.S. Federal nanoEHS data compendium, but also in keeping a level of consistency that will allow interoperability with U.S. and European partners. The most recent version of the EPA NaKnowBase (NKB) has been implemented for semantic integration. Computational code has been developed to use each NKB record as input, modify and filter table data, and subsequently output each modified record to a Research Description Framework (RDF). To improve the accuracy and efficiency of this process the EPA has created the OntoSearcher tool. This tool partially automates the ontology mapping process, thereby reducing onerous manual curation. Conclusions: Here we describe the efforts of the US EPA in promoting FAIR data standards for Federal nanoEHS data through semantic integration, as well as in the development of NAMs (computational tools) to facilitate these improvements for nanoEHS data at the Federal partner level.

Mining, refining, and QSAR analysing the nanoinformatics in EPA NaKnowBase.

Concerns about the safety of manufacturing and using engineered nanomaterials (ENMs) have been increasing as the technology continues to expand. Efforts have been underway to investigate the potentially harmful effects of ENMs without carrying out the challenging empirical studies. To make such investigations possible, the US EPA Office of Research and Development (ORD) developed the nanomaterial database NaKnowBase (NKB) containing the detail of hundreds of assays conducted and published by ORD scientists experimentally investigating the environmental health and safety effects of ENMs (nanoEHS). This article describes specifics of the effort to mine, refine, and analyse the NKB. Here we use a quantitative structure activity relationship (QSAR) analysis, using a random forest of decision trees to predict the in vitro cell viability effects that occur upon exposure to ENMs that are similar in composition and structure and implement a set of laboratory conditions. These predictions are confirmed using the Jaqpot cloud platform developed by the National Technical University of Athens, Greece (NTUA) where nanoEHS effects are investigated with scientists working together globally.

Why adverse outcome pathways need to be FAIR.

Adverse outcome pathways (AOPs) provide evidence for demonstrating and assessing causality between measurable toxicological mechanisms and human or environmental adverse effects. AOPs have gained increasing attention over the past decade and are believed to provide the necessary steppingstone for more effective risk assessment of chemicals and materials and moving beyond the need for animal testing. However, as with all types of data and knowledge today, AOPs need to be reusable by machines, i.e., machine-actionable, in order to reach their full impact potential. Machine-actionability is supported by the FAIR principles, which guide findability, accessibility, interoperability, and reusability of data and knowledge. Here, we describe why AOPs need to be FAIR and touch on aspects such as the improved visibility and the increased trust that FAIRification of AOPs provides.

New approach methodologies (NAMs) can detect biological phenomena that occur before they add up to serious problems like cancer, infertility, death, and others. NAMs detect key events (KE) along well-proven and agreed adverse outcome pathways (AOP). If a substance tests positive in a NAM for an upstream KE, this signals an early warning that actual adversity might follow. However, what if the knowledge about these AOPs is a well-kept secret? And what if decision-makers find AOPs too exotic to apply in risk assessment? This is where FAIR comes in! FAIR stands for making information findable, accessible, interoperable and re-useable. It aims to increase availability, usefulness, and trustworthiness of data. Here, we show that by interpreting the FAIR principles beyond a purely technical level, AOPs can ring in a new era of 3Rs applicability ‒ by increasing their visibility and making their creation process more transparent and reproducible.

NNI Nanoinformatics Conference 2023: Movement Toward a Common Infrastructure for Federal nanoEHS Data Computational Toxicology: Short Communication.

The National Nanotechnology Initiative organized a Nanoinformatics Conference in the 2023 Biden-Harris Administration's Year of Open Science, which included interested U.S. and EU stakeholders, and preceded the U.S.-EU COR meeting on November 15th, 2023 in Washington, D.C. Progress in the development of a common nanoinformatics infrastructure in the European Union and United States were discussed. Development of contributing, individual database projects, and their strengths and weaknesses, were highlighted. Recommendations and next steps for a U.S. nanoEHS common infrastructure were discussed in light of the pending update of the National Nanotechnology Initiative (NNI)'s Environmental, Health and Safety Research Strategy, and U.S. efforts to curate and house nano Environmental Health and Safety (nanoEHS) data from U.S. federal stakeholder groups. Improved data standards, for reporting and storage have been identified as areas where concerted efforts could most benefit initially. Areas that were not addressed at the conference, but that are critical to progress of the U.S. federal consortium effort are the evaluation of data formats according to use and sustainability measures; modeler and end user, including risk-assessor and regulator perspectives; a need for a community forum or shared data location that is not hosted by any individual U.S. federal agency, and is accessible to the public; as well as emerging needs for integration with new data types such as micro and nano plastics, and interoperability with other data and meta-data, such as adverse outcome pathway information. Future progress will depend on continued interaction of the U.S. and EU CORs, stakeholders and partners in the continued development goals for shared or interoperable infrastructure for nanoEHS.

Integrating mechanistic and polymorphism data to characterize human genetic susceptibility for environmental chemical risk assessment in the 21st century.

Response to environmental chemicals can vary widely among individuals and between population groups. In human health risk assessment, data on susceptibility can be utilized by deriving risk levels based on a study of a susceptible population and/or an uncertainty factor may be applied to account for the lack of information about susceptibility. Defining genetic susceptibility in response to environmental chemicals across human populations is an area of interest in the NAS' new paradigm of toxicity pathway-based risk assessment. Data from high-throughput/high content (HT/HC), including -omics (e.g., genomics, transcriptomics, proteomics, metabolomics) technologies, have been integral to the identification and characterization of drug target and disease loci, and have been successfully utilized to inform the mechanism of action for numerous environmental chemicals. Large-scale population genotyping studies may help to characterize levels of variability across human populations at identified target loci implicated in response to environmental chemicals. By combining mechanistic data for a given environmental chemical with next generation sequencing data that provides human population variation information, one can begin to characterize differential susceptibility due to genetic variability to environmental chemicals within and across genetically heterogeneous human populations. The integration of such data sources will be informative to human health risk assessment.

Exploring genetic influences on adverse outcome pathways using heuristic simulation and graph data science.

Adverse outcome pathways provide a powerful tool for understanding the biological signaling cascades that lead to disease outcomes following toxicity. The framework outlines downstream responses known as key events, culminating in a clinically significant adverse outcome as a final result of the toxic exposure. Here we use the AOP framework combined with artificial intelligence methods to gain novel insights into genetic mechanisms that underlie toxicity-mediated adverse health outcomes. Specifically, we focus on liver cancer as a case study with diverse underlying mechanisms that are clinically significant. Our approach uses two complementary AI techniques: Generative modeling via automated machine learning and genetic algorithms, and graph machine learning. We used data from the US Environmental Protection Agency's Adverse Outcome Pathway Database (AOP-DB; aopdb.epa.gov) and the UK Biobank's genetic data repository. We use the AOP-DB to extract disease-specific AOPs and build graph neural networks used in our final analyses. We use the UK Biobank to retrieve real-world genotype and phenotype data, where genotypes are based on single nucleotide polymorphism data extracted from the AOP-DB, and phenotypes are case/control cohorts for the disease of interest (liver cancer) corresponding to those adverse outcome pathways. We also use propensity score matching to appropriately sample based on important covariates (demographics, comorbidities, and social deprivation indices) and to balance the case and control populations in our machine language training/testing datasets. Finally, we describe a novel putative risk factor for LC that depends on genetic variation in both the aryl-hydrocarbon receptor (AHR) and ATP binding cassette subfamily B member 11 (ABCB11) genes.

An EPA database on the effects of engineered nanomaterials-NaKnowBase.

The US EPA Office of Research and Development (ORD) has conducted a research program assessing potential risks of emerging materials and technologies, including engineered nanomaterials (ENM). As a component of that program, a nanomaterial knowledge base, termed "NaKnowBase", was developed containing the results of published ORD research relevant to the potential environmental and biological actions of ENM. The experimental data address issues such as ENM release into the environment; fate, transport and transformations in environmental media; exposure to ecological species or humans; and the potential for effects on those species. The database captures information on the physicochemical properties of ENM tested, assays performed and their parameters, and the results obtained. NaKnowBase (NKB) is a relational SQL database, and may be queried either with SQL code or through a user-friendly web interface. Filtered results may be output in spreadsheet format for subsequent user-defined analyses. Potential uses of the data might include input to quantitative structure-activity relationships (QSAR), meta-analyses, or other investigative approaches.

A Community-Driven, Openly Accessible Molecular Pathway Integrating Knowledge on Malignant Pleural Mesothelioma.

Malignant pleural mesothelioma (MPM) is a highly aggressive malignancy mainly triggered by exposure to asbestos and characterized by complex biology. A significant body of knowledge has been generated over the decades by the research community which has improved our understanding of the disease toward prevention, diagnostic opportunities and new treatments. Omics technologies are opening for additional levels of information and hypotheses. Given the growing complexity and technological spread of biological knowledge in MPM, there is an increasing need for an integrating tool that may allow scientists to access the information and analyze data in a simple and interactive way. We envisioned that a platform to capture this widespread and fast-growing body of knowledge in a machine-readable and simple visual format together with tools for automated large-scale data analysis could be an important support for the work of the general scientist in MPM and for the community to share, critically discuss, distribute and eventually advance scientific results. Toward this goal, with the support of experts in the field and informed by existing literature, we have developed the first version of a molecular pathway model of MPM in the biological pathway database WikiPathways. This provides a visual and interactive overview of interactions and connections between the most central genes, proteins and molecular pathways known to be involved or altered in MPM. Currently, 455 unique genes and 247 interactions are included, derived after stringent manual curation of an initial 39 literature references. The pathway model provides a directly employable research tool with links to common databases and repositories for the exploration and the analysis of omics data. The resource is publicly available in the WikiPathways database (Wikipathways : WP5087) and continues to be under development and curation by the community, enabling the scientists in MPM to actively participate in the prioritization of shared biological knowledge.

The AOP-DB RDF: Applying FAIR Principles to the Semantic Integration of AOP Data Using the Research Description Framework.

Computational toxicology is central to the current transformation occurring in toxicology and chemical risk assessment. There is a need for more efficient use of existing data to characterize human toxicological response data for environmental chemicals in the US and Europe. The Adverse Outcome Pathway (AOP) framework helps to organize existing mechanistic information and contributes to what is currently being described as New Approach Methodologies (NAMs). AOP knowledge and data are currently submitted directly by users and stored in the AOP-Wiki (https://aopwiki.org/). Automatic and systematic parsing of AOP-Wiki data is challenging, so we have created the EPA Adverse Outcome Pathway Database. The AOP-DB, developed by the US EPA to assist in the biological and mechanistic characterization of AOP data, provides a broad, systems-level overview of the biological context of AOPs. Here we describe the recent semantic mapping efforts for the AOP-DB, and how this process facilitates the integration of AOP-DB data with other toxicologically relevant datasets through a use case example.

COVID-19 through Adverse Outcome Pathways: Building networks to better understand the disease - 3rd CIAO AOP Design Workshop.

On April 28-29, 2021, 50 scientists from different fields of expertise met for the 3rd online CIAO workshop. The CIAO project "Modelling the Pathogenesis of COVID-19 using the Adverse Outcome Pathway (AOP) framework" aims at building a holistic assembly of the available scientific knowledge on COVID-19 using the AOP framework. An individual AOP depicts the disease progression from the initial contact with the SARS-CoV-2 virus through biological key events (KE) toward an adverse outcome such as respiratory distress, anosmia or multiorgan failure. Assembling the individual AOPs into a network highlights shared KEs as central biological nodes involved in multiple outcomes observed in COVID-19 patients. During the workshop, the KEs and AOPs established so far by the CIAO members were presented and posi­tioned on a timeline of the disease course. Modulating factors influencing the progression and severity of the disease were also addressed as well as factors beyond purely biological phenomena. CIAO relies on an interdisciplinary crowd­sourcing effort, therefore, approaches to expand the CIAO network by widening the crowd and reaching stakeholders were also discussed. To conclude the workshop, it was decided that the AOPs/KEs will be further consolidated, inte­grating virus variants and long COVID when relevant, while an outreach campaign will be launched to broaden the CIAO scientific crowd.

The 2021 update of the EPA's adverse outcome pathway database.

The EPA developed the Adverse Outcome Pathway Database (AOP-DB) to better characterize adverse outcomes of toxicological interest that are relevant to human health and the environment. Here we present the most recent version of the EPA Adverse Outcome Pathway Database (AOP-DB), version 2. AOP-DB v.2 introduces several substantial updates, which include automated data pulls from the AOP-Wiki 2.0, the integration of tissue-gene network data, and human AOP-gene data by population, semantic mapping and SPARQL endpoint creation, in addition to the presentation of the first publicly available AOP-DB web user interface. Potential users of the data may investigate specific molecular targets of an AOP, the relation of those gene/protein targets to other AOPs, cross-species, pathway, or disease-AOP relationships, or frequencies of AOP-related functional variants in particular populations, for example. Version updates described herein help inform new testable hypotheses about the etiology and mechanisms underlying adverse outcomes of environmental and toxicological concern.

African Y chromosome and mtDNA divergence provides insight into the history of click languages.

BACKGROUND: About 30 languages of southern Africa, spoken by Khwe and San, are characterized by a repertoire of click consonants and phonetic accompaniments. The Jumid R:'hoansi (!Kung) San carry multiple deeply coalescing gene lineages. The deep genetic diversity of the San parallels the diversity among the languages they speak. Intriguingly, the language of the Hadzabe of eastern Africa, although not closely related to any other language, shares click consonants and accompaniments with languages of Khwe and San. RESULTS: We present original Y chromosome and mtDNA variation of Hadzabe and other ethnic groups of Tanzania and Y chromosome variation of San and peoples of the central African forests: Biaka, Mbuti, and Lisongo. In the context of comparable published data for other African populations, analyses of each of these independently inherited DNA segments indicate that click-speaking Hadzabe and Jumid R:'hoansi are separated by genetic distance as great or greater than that between any other pair of African populations. Phylogenetic tree topology indicates a basal separation of the ancient ancestors of these click-speaking peoples. That genetic divergence does not appear to be the result of recent gene flow from neighboring groups. CONCLUSIONS: The deep genetic divergence among click-speaking peoples of Africa and mounting linguistic evidence suggest that click consonants date to early in the history of modern humans. At least two explanations remain viable. Clicks may have persisted for tens of thousands of years, independently in multiple populations, as a neutral trait. Alternatively, clicks may have been retained, because they confer an advantage during hunting in certain environments.

Accelerating Adverse Outcome Pathway Development Using Publicly Available Data Sources.

The adverse outcome pathway (AOP) concept links molecular perturbations with organism and population-level outcomes to support high-throughput toxicity (HTT) testing. International efforts are underway to define AOPs and store the information supporting these AOPs in a central knowledge base; however, this process is currently labor-intensive and time-consuming. Publicly available data sources provide a wealth of information that could be used to define computationally predicted AOPs (cpAOPs), which could serve as a basis for creating expert-derived AOPs in a much more efficient way. Computational tools for mining large datasets provide the means for extracting and organizing the information captured in these public data sources. Using cpAOPs as a starting point for expert-derived AOPs should accelerate AOP development. Coupling this with tools to coordinate and facilitate the expert development efforts will increase the number and quality of AOPs produced, which should play a key role in advancing the adoption of HTT testing, thereby reducing the use of animals in toxicity testing and greatly increasing the number of chemicals that can be tested.

Using pathway modules as targets for assay development in xenobiotic screening.

Toxicology and pharmaceutical research is increasingly making use of high throughout-screening (HTS) methods to assess the effects of chemicals on molecular pathways, cells and tissues. Whole-genome microarray analysis provides broad information on the response of biological systems to chemical exposure, but is not practical to use when thousands of chemicals need to be evaluated at multiple doses and time points, as well as across different tissues, species and life-stages. A useful alternative approach is to identify a focused set of genes that can give a coarse picture of systems-level responses and that can be scaled to the evaluation of thousands of chemicals and diverse biological contexts. We demonstrate a computational approach to select in vitro expression assay targets that are informative and broadly distributed in biological pathway space, using the concept of pathway modularity. Canonical pathways are decomposed into subnetworks (modules) of functionally-related genes based on rules such as co-regulated expression, protein-protein interactions, and coordinated physiological activity. Pathway modules are constructed using these rules but are then restricted by the bounds of canonical pathways. We demonstrate this approach using a subset of genes associated with tumor development and cancer progression. Target genes were identified for assay development, and then validated by using independent, published microarray data. The result is a targeted set of genes that are sensitive predictors of whether a chemical will perturb each pathway module. These selected genes could then form the basis for a battery to test for pathway-chemical interactions under many biological contexts using throughput expression-based assays.

Characterization of genetic variation and natural selection at the arylamine N-acetyltransferase genes in global human populations.

UNLABELLED: Functional variability at the arylamine N-acetyltransferase genes is associated with drug response in humans and may have been adaptive in the past owing to selection pressure from diet and exposure to toxins during human evolution. AIMS: We have characterized nucleotide variation at the NAT1 and NAT2 genes, and at the NATP1 pseudogene in global human populations, including many previously under-represented African populations, in order to identify potential functional variants and to understand the role that natural selection has played in shaping variation at these loci in globally diverse populations. MATERIALS & METHODS: We have resequenced approximately 2800 bp for each of the NAT1 and NAT2 gene regions, as well as the pseudogene NATP1, in 197 African and 132 nonAfrican individuals. RESULTS & CONCLUSION: We observe a signature of balancing selection maintaining variation in the 3'-UTR of NAT1, suggesting that these variants may play a functional role that is currently undefined. In addition, we observed high levels of nonsynonymous functional variation at the NAT2 locus that differs amongst ethnically diverse populations.

Activity profiles of 309 ToxCast™ chemicals evaluated across 292 biochemical targets.

Understanding the potential health risks posed by environmental chemicals is a significant challenge elevated by the large number of diverse chemicals with generally uncharacterized exposures, mechanisms, and toxicities. The present study is a performance evaluation and critical analysis of assay results for an array of 292 high-throughput cell-free assays aimed at preliminary toxicity evaluation of 320 environmental chemicals in EPA's ToxCast™ project (Phase I). The chemicals (309 unique, 11 replicates) were mainly precursors or the active agent of commercial pesticides, for which a wealth of in vivo toxicity data is available. Biochemical HTS (high-throughput screening) profiled cell and tissue extracts using semi-automated biochemical and pharmacological methodologies to evaluate a subset of G-protein coupled receptors (GPCRs), CYP450 enzymes (CYPs), kinases, phosphatases, proteases, HDACs, nuclear receptors, ion channels, and transporters. The primary screen tested all chemicals at a relatively high concentration 25 µM concentration (or 10 µM for CYP assays), and a secondary screen re-tested 9132 chemical-assay pairs in 8-point concentration series from 0.023 to 50 µM (or 0.009-20 µM for CYPs). Mapping relationships across 93,440 chemical-assay pairs based on half-maximal activity concentration (AC50) revealed both known and novel targets in signaling and metabolic pathways. The primary dataset, summary data and details on quality control checks are available for download at http://www.epa.gov/ncct/toxcast/.