The integration of existing knowledge to support the risk assessment of chemicals is an ongoing challenge for scientists, risk assessors and risk managers. In addition, European Union regulations ...limiting the use of new animal testing in cosmetics makes already existing information even more valuable.
Applying a previous SEURAT-1 program framework to derive predictions of in vivo toxicity responses for a compound, we selected piperonyl butoxide (PBO) as a case study for identification of knowledge and methodology gaps in understanding a compound's effects on the human liver. This is investigated through integration of data from human in vitro transcriptomics studies, biological pathway analysis, chemical and disease associations, and adverse outcome pathway (AOP) information. The outcomes of the analysis are used to generate AOPs of liver-related endpoints, identifying areas of concern for risk assessors and regulators.
We demonstrate that integration of data through already existing and publicly available tools can produce outcomes comparable to those that may be found through more conventional time- and resource-intensive methods. It is also expected that, with more refinement, this approach could in the future provide evidence to support chemical risk assessment, while also identifying data gaps for which additional testing may be needed.
•Liver fibrosis, steatosis and cancer are found as adverse outcomes (AO) of PBO.•Workflow yields AO networks of biological pathways, phenotypes, and diseases.•Workflow is applicable to most chemicals when transcriptomics data are available.•The workflow also identifies data gaps for which additional testing may be needed.
The predominantly animal-centric approach of chemical safety assessment has increasingly come under pressure. Society is questioning overall performance, sustainability, continued relevance for human ...health risk assessment and ethics of this system, demanding a change of paradigm. At the same time, the scientific toolbox used for risk assessment is continuously enriched by the development of “New Approach Methodologies” (NAMs). While this term does not define the age or the state of readiness of the innovation, it covers a wide range of methods, including quantitative structure–activity relationship (QSAR) predictions, high-throughput screening (HTS) bioassays, omics applications, cell cultures, organoids, microphysiological systems (MPS), machine learning models and artificial intelligence (AI). In addition to promising faster and more efficient toxicity testing, NAMs have the potential to fundamentally transform today’s regulatory work by allowing more human-relevant decision-making in terms of both hazard and exposure assessment. Yet, several obstacles hamper a broader application of NAMs in current regulatory risk assessment. Constraints in addressing repeated-dose toxicity, with particular reference to the chronic toxicity, and hesitance from relevant stakeholders, are major challenges for the implementation of NAMs in a broader context. Moreover, issues regarding predictivity, reproducibility and quantification need to be addressed and regulatory and legislative frameworks need to be adapted to NAMs. The conceptual perspective presented here has its focus on hazard assessment and is grounded on the main findings and conclusions from a symposium and workshop held in Berlin in November 2021. It intends to provide further insights into how NAMs can be gradually integrated into chemical risk assessment aimed at protection of human health, until eventually the current paradigm is replaced by an animal-free “Next Generation Risk Assessment” (NGRA).
Toxicological research faces the challenge of integrating knowledge from diverse fields and novel technological developments generally in the biological and medical sciences. We discuss herein the ...fact that the multiple facets of cancer research, including discovery related to mechanisms, treatment and diagnosis, overlap many up and coming interest areas in toxicology, including the need for improved methods and analysis tools. Common to both disciplines, in vitro and in silico methods serve as alternative investigation routes to animal studies. Knowledge on cancer development helps in understanding the relevance of chemical toxicity studies in cell models, and many bioinformatics‐based cancer biomarker discovery tools are also applicable to computational toxicology. Robotics‐aided, cell‐based, high‐throughput screening, microscale immunostaining techniques and gene expression profiling analyses are common tools in cancer research, and when sequentially combined, form a tiered approach to structured safety evaluation of thousands of environmental agents, novel chemicals or engineered nanomaterials. Comprehensive tumour data collections in databases have been translated into clinically useful data, and this concept serves as template for computer‐driven evaluation of toxicity data into meaningful results. Future ‘cancer research‐inspired knowledge management’ of toxicological data will aid the translation of basic discovery results and chemicals‐ and materials‐testing data to information relevant to human health and environmental safety.
Abstract
Biomarkers that accurately predict outcome or treatment response is lacking for most human tumor types, including for common and well-studied cancers like head and neck squamous cell ...carcinoma (HNSCC). With the aim to summarize existing genomic analysis and biomarker data into a format analyzable with bioinformatics processing tools, we set out to develop a biomarker wiki for HNSCC, hypothesizing that meta-analyses would reveal novel and potentially clinically useful sets of biomarker genes. We applied the user-friendly MediaWiki interface for collecting data in the public domain, and separated the results under the categories: primary tumor profiling, metastasis profiling, cell line profiling and treatment response. The information was captured applying controlled and standardized vocabularies, including with data storage in the multi-omics Investigation/Study/Assay (ISA-Tab) format. Bioinformatics-driven meta-analyses of results from currently over 50 publications indicate that the wiki serves to accurately identify common gene patterns, gene ontologies, molecular networks and key regulator genes within and among the respective categories. Additionally, independent validations relative other databases such as the Human Gene Expression Map, the Human Protein Atlas and the cBio Cancer Genomics Portal confirm many associations, including with significant correlations to poor outcome for gene sub-sets. Overall, the wiki results so far agree with the hypothesis under testing, suggesting novel biomarker genes/signatures for future clinical assessment. On completion, we foresee a query-enabling update-friendly knowledge platform for head and neck cancer studies in the public domain.
Citation Format: Rebecca Ceder, Pekka Kohonen, Roland C. Grafström. The head and neck cancer biomarker wiki: A tool for data mining and biomarker meta-analyses. abstract. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3179. doi:10.1158/1538-7445.AM2013-3179
In vitro models are currently not considered to be suitable replacements for animals in experiments to assess the multiple factors that underlie the development of cancer as a result of environmental ...exposure to chemicals. An evaluation was conducted on the potential use of normal keratinocytes, the SV40 T-antigen-immortalised keratinocyte cell line, SVpgC2a, and the carcinoma cell line, SqCC/Y1, alone and in combination, and under standardised serum-free culture conditions, to study oral cancer progression. In addition, features considered to be central to cancer development as a result of environmental exposure to chemicals, were analysed. Genomic expression, and enzymatic and functional data from the cell lines reflected many aspects of the transition of normal tissue epithelium, via dysplasia, to full malignancy. The composite cell line model develops aberrances in proliferation, terminal differentiation and apoptosis, in a similar manner to oral cancer progression in vivo. Transcript and protein profiling links aberrations in multiple gene ontologies, molecular networks and tumour biomarker genes (some proposed previously, and some new) in oral carcinoma development. Typical specific changes include the loss of tumour-suppressor p53 function and of sensitivity to retinoids. Environmental agents associated with the aetiology of oral cancer differ in their requirements for metabolic activation, and cause toxic effects to cells in both the normal and the transformed states. The results suggest that the model might be useful for studies on the sensitivity of cells to chemicals at different stages of cancer progression, including many aspects of the integrated roles of cytotoxicity and genotoxicity. Overall, the properties of the SVpgC2a and SqCC/Y1 cell lines, relative to normal epithelial cells in monolayer or organotypic culture, support their potential applicability to mechanistic studies on cancer risk factors, including, in particular, the definition of critical toxicity effects and dose-effect relationships.
Acetaldehyde (AA) and methylglyoxal (MG) are reactive, ubiquitous aldehydes, present in the environment and endogenously formed in animals and humans. They have both been shown to readily form DNA ...adducts under simulated physiological conditions. We report here on the use of cultured normal and SV40T antigen-immortalized human buccal epithelial cells as model systems for aldehyde exposure of the oral epithelium, occurring through the ingestion of alcoholic beverages and brewed coffee, as well as by inhalation of tobacco smoke and automobile exhaust. By the application of recently developed
32P-postlabeling methods, the presence of both endogenous and induced AA and MG DNA adducts was demonstrated in cultured human epithelial cells. Furthermore, these DNA adducts were formed in a dose-dependent manner at aldehyde concentrations that were relatively non-toxic to the cells.
Developing safe and sustainable nanomaterials-based solutions to current global challenges including clean energy, sustainable food production and water security requires access to high quality data ...and appropriate analysis and modelling approaches. Achieving these challenges requires increased re-use of research data to accelerate progress and support development of new materials that are safe and sustainable for energy capture and storage, nano-agriculture and environmental remediation. The principles of Findability, Accessibility, Interoperability and Reusability (FAIR) provide a roadmap to enhanced data sharing and re-use, but require consensus within the nanosafety community on metadata, ontologies and persistent identifiers (among other things) and guidance to support implementation and achieve machine-readability. Here, we highlight the main focus of the AdvancedNano GO FAIR Implementation Network in supporting the nanosafety community with implementation of FAIR to maximize data-driven safe and sustainable application of nano- and advanced materials.
Display omitted
•Environmental and human health safety assessment of nano- and advanced materials (NM and AdMa) is a necessity today to avoid devastating adverse effects from nanotechnology.•Safety assessment builds on data in findable, accessible, interoperable, and reusable (FAIR) formats.•The field of nanosafety has recently initiated implementation of the FAIR principles, however, clear rules for sustainable implementation and for active promotion of data reuse are still lacking. Ground-breaking steps need to be taken, especially in the areas of new approaches for hazard and risk assessment, including development and validation of machine-driven modelling approaches.•The AdvancedNano GO FAIR Implementation Network was launched to cover these needs, involving key players such as data generators, database developers, data(base) users and regulators/policy makers, to facilitate FAIRification of nanosafety data.•Overall, the network is a central component in the path towards a safe and sustainable future, built on transparent and effective data-driven risk assessment of NM and AdMa.
The interest toward omics data is growing in the field of toxicology owing to the diverse knowledge they generate, which can improve prediction and dosage profiling for more accurate safety ...assessment. An integration methodology is presented where high‐throughput omics data are enriched with biological‐pathway information to produce a novel set of biological (BIO) descriptors by decomposing omics data to meaningful clusters in terms of both their mechanistic interpretation and correlation affinity. A generalized simulated annealing algorithm is employed to estimate the optimal partition of the enriched data and accordingly produce novel descriptors based on gene content similarity. BIO descriptors are characterized by the pathway information fused to the data; thereby, they refer to groups of genes with similar biological implications rather than specific genes, which could vary across studies. The methodology is applied to an extensive proteomics data set and demonstrates that BIO descriptors are beneficial for modeling prediction, outperforming the prediction accuracy of the original omics data, and offering a readily available biological interpretation of the findings.
Complex biological end‐points assessed by high‐throughput methodologies have gained much attention during the last few years within nanosafety. An integration methodology is presented where high‐throughput omics data are enriched with biological‐pathway information, to produce a novel set of biological descriptors by decomposing omics data to meaningful clusters in terms of both their mechanistic interpretation and correlation affinity.
Abstract
There is an urgent need to apply effective, data-driven approaches to reliably predict engineered nanomaterial (ENM) toxicity. Here we introduce a predictive computational framework based on ...the molecular and phenotypic effects of a large panel of ENMs across multiple in vitro and in vivo models. Our methodology allows for the grouping of ENMs based on multi-omics approaches combined with robust toxicity tests. Importantly, we identify mRNA-based toxicity markers and extensively replicate them in multiple independent datasets. We find that models based on combinations of omics-derived features and material intrinsic properties display significantly improved predictive accuracy as compared to physicochemical properties alone.
PDF
