Computational Toxicology最新文献

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.

For organic contaminants in lake sediments, aquifers, and anaerobic bioreactors, their reduction is one of the primary transformation paths in these anoxic water environments. A simple model is introduced to predict pseudo-first order rate constants (k_obs) for the abiotic reduction of organic compounds featuring diverse reducible functional groups. It utilizes the largest experimental dataset of –log k_obs, encompassing 59 organic compounds (278 data points). Unlike available complex quantitative structure–activity relationship (QSAR) methods, the novel approach requires both experimental conditions and structural parameters. In comparison to one of the available general QSAR methods, the new model demonstrates favorable performance. The average absolute deviation (AAD), absolute maximum deviation (AD_max), average absolute relative deviation (AARD%), and R-squared (R²) values of the estimated outputs for 54/5 training/test data sets of the new model are 0.641/1.761, 1.761/1.417, 20.52/83.87, and 0.797/0.949, respectively. On the other hand, the available general comparative QSAR method shows the AAD: 1.311/2.301, AD_max: 3.795/3.732, AARD%: 641.0/821.2, and R²: 0.003/0.447. For the test set, AAD, AARD%, AD_max, and R² values for the new/comparative models are 0.649/2.403, 62.20/190.5, 1.215/3.732 and 0.974/0.789, respectively. In summary, the new model offers a straightforward approach for the manual calculation of –log k_obs, demonstrating excellent goodness-of-fit, reliability, precision, and accuracy.

Justifying read-across predictions for subchronic effects, such as no observed adverse effect levels (NOAEL), is challenging. The scarcity of suitable experimental data hampers such predictions, such that a conservative approach is often employed where the structural similarity between target and the tested source substances is very high. A less stringent interpretation of structural similarity may be used to expand data gap-filling by read-across if other types of similarity (e.g., toxicokinetic and toxicodynamic consideration) are factored into the justification. Herein, qualitative and quantitative in silico-assisted procedures are described and demonstrated for those instances where no structurally similar analogues are identified. In the qualitative approach, the toxicity classification of the most toxic metabolite is assigned directly to the target compound. While simple, this approach may lead to an over-classification of the target compound and a false positive result. In contrast, the quantitative approach is more complicated. In addition to identifying those metabolites causing toxicity, it examines the quantitative information for the amount of the most toxic metabolite. The maximum dose of the parent chemical is estimated which will not result in the generation of toxic metabolites sufficient to cause harmful effects. This quantitative approach permits a calculation of the margin of exposure, is noteworthy for industrial assessment purposes.

MoS-TEC is a newly developed toxicogenomics database for time-expression curves fitted with a statistical model selection approach. Toxicogenomic data provide information on the response of the genome to compounds, often measured in terms of gene expression values. When such experimental data are available for different exposure times, the functional relationships between the exposure time and the expression values of genes might be of interest. The TG-GATEs (Open Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System) database provides such information for genomewide gene expression data for 170 compounds. We performed extensive model selection using MCP-Mod on these data. Specifically, gene expression data measured for eight time points from in vivo experiments on rat liver for 120 compounds with complete datasets were considered. MCP-Mod is a two-step approach, including a multiple comparison procedure (MCP) and a modelling (Mod) approach. The results are estimated time-expression curves that model the relationship between exposure time and gene expression values for all combinations of genes and compounds. We present an appropriate data normalization approach and report which models were selected per compound and in total. For high-quality model fits with a large value for the explained variance, the sigEmax model was most frequently selected. The new R Shiny application MoS-TEC provides easy access for researchers to the best curve fit for all genes individually for all compounds. It can be used online without installing additional software.

The potential toxicity arising from antibiotics and pesticides poses a significant risk to the preservation of groundwater. This study investigates the effects of binary mixtures of pharmaceuticals and pesticides by assessing their log EC₅₀, log EC₃₀, and log EC₁₀ values in relation to Vibrio fischeri bacteria. Based on a comprehensive dataset of 459 observations, this work identifies suitable simple descriptors. Rigorous statistical analysis confirms the models’ reliability, accuracy, precision, and favorable goodness-of-fit. Notably, the ratios of coefficient of determination (R²) for the novel models compared to the best comparative models exceed 1.0: 0.8618/0.8085 for log EC₅₀, 0.8856/0.8422 for log EC₃₀, and 0.8973/0.8556 for log EC₁₀. Additionally, the ratios of root mean square error (RMSE) for the new models relative to their counterparts are all below 1.0: 0.159/0.191 for log EC₅₀, 0.131/0.169 for log EC₃₀, and 0.182/0.215 for log EC₁₀.

Non-animal-based in vitro and in silico approaches for the safety assessment of cosmetic ingredients, recently referred to as Next Generation Risk Assessment (NGRA)/New Approach Methodologies (NAMs), are evolving rapidly as approaches to provide a basis for the regulatory acceptance of new materials. However, predictive models should be applied only to chemicals within the chemical space defined by the dataset used in generating the model. Thus, only predictions for new molecules that are relatively similar to the modeling set can considered reliable with strong confidence. In this study, we developed the S-COPHY model, which employs deep learning to classify new compounds based on their structural similarity to a large collection of pharmaceutical and cosmetic compounds. S-COPHY shows high predictive accuracy both internally and externally, and in particular, there were only a few instances where pharmaceuticals were incorrectly predicted as cosmetics. The use of deep learning enabled the automatic generation of input data from SMILES (Simplified Molecular Input Line Entry System) information, resulting in more consistent model outcomes. Furthermore, GRAD-CAM (Gradient-weighted Class Activation Map) analysis provided insights into the specific structures that contribute to the model's predictions. The potentiality of S-COPHY to identify characteristic structures associated with pharmaceutical-like activity indicates its potential value in supporting safety assessments of cosmetic ingredients. Our results indicate that the S-COPHY model is a promising approach to support decision-making in large chemical spaces, thereby contributing to the safety evaluation of cosmetic ingredients. Expansion of the model to other categories, such as pesticides, could further extend its applicability.

Decision-making on the use and management of chemicals in society is on the brink of a scientific and technological revolution. At the same time world politics is focusing more on chemicals, waste and pollution prevention, alongside climate change and biodiversity loss. To enable effective decision-making, policy makers and regulators will need to draw upon the best scientific evidence available on the real-life causation and consequences of adverse effects of chemical and waste exposures affecting humans, wildlife and the environment. New Approach Method (NAM) data from modern day multidisciplinary science and technology is becoming more available using cheminformatics, computational prediction algorithms using AI, transcriptomics, genomics, proteomics, mathematical modelling, epidemiology, biological monitoring, and clinical science. Current chemical regulation has been shaped by the animal models of the 20th century. NAMs and Next Generation Risk Assessment (NGRA) have the potential to better support innovations in chemicals and materials through science-informed decision making that is more species-relevant and protective of adverse outcomes; this will require future-proofed regulatory transformation. Capacity building and skills development in computational and in vitro NAMs will be key to this transformation.

We present machine learning studies devoted to the creation of predictive models for toxicity evaluation of imidazolium- and pyridinium-containing ionic liquids. New created predictive models were developed using the OCHEM. The predictive ability of the models was tested by cross-validation, giving a coefficient of determination q² = 0.77–0.82. The models were applied to screen a virtual chemical library to the toxicity of ILs in Danio rerio and Daphnia magna bioassays. Models were used to predict toxicity for 25 ILs, which were then synthesized and tested in vivo. The in vivo toxicity studies found that D. magna is a more sensitive aquatic test organism than D. rerio – 67 % of the studied ILs are classified as extremely toxic with an LC₅₀ range from 0.005 to 0.01 mg/l. At the same time, only one IL 1-dodecylpyridinium bromide with an LC₅₀ of 0.08 mg/l is classified as extremely toxic, and 76 % are classified as slightly and moderately toxic compounds using D. rerio as a test organism. The most toxic ILs 5 and 19 were docked into the human AChE active center and demonstrated calculated binding energy values −9.5 and −9.3 kcal/mol that is comparable with the complexation of the human AChE inhibitor Donepezil, which provides insight into the potential molecular mechanisms of ILs toxicity. The created QSTR models are a successful tool for the toxicity analysis of new promising ILs. QSTR models demonstrated not only high predictive indicators but also a high percentage of correctly predicted toxicity values in vivo studies.

Adverse Outcome Pathways (AOPs) provide a basis for non-animal testing, by outlining the cascade of molecular and cellular events initiated upon stressor exposure, leading to adverse effects. In recent years, the scientific community has shown interest in developing AOPs through crowdsourcing, with the results archived in the AOP-Wiki: a centralized repository coordinated by the OECD, hosting nearly 512 AOPs (April, 2023). However, the AOP-Wiki platform currently lacks a versatile querying system, which hinders developers' exploration of the AOP network and impedes its practical use in risk assessment. This work proposes to unleash the full potential of the AOP-Wiki archive by adapting its data into a Labelled Property Graph (LPG) schema. Additionally, the tool offers a visual network query interface for both database-specific and natural language queries, facilitating the retrieval and analysis of graph data. The multi-query interface allows non-technical users to construct flexible queries, thereby enhancing the potential for AOP exploration. By reducing the time and technical requirements, the present query engine enhances the practical utilization of the valuable data within AOP-Wiki. To evaluate the platform, a case study is presented with three levels of use-case scenarios (simple, moderate, and complex queries). AOPWIKI-EXPLORER is freely available on GitHub (https://github.com/Crispae/AOPWiki_Explorer) for wider community reach and further enhancement.

Sequencing depth and biological replication represent key experimental design considerations in toxicogenomics and risk assessment. However, their relative impacts on differential gene expression analysis remain unclear. Using an 8-dose chemical (Prochloraz) perturbation RNA-seq dataset in A549 cells, we systematically subsampled sequencing depth (5–100 %) and replicates (2–4) to evaluate effects on number of differentially expressed genes. While dose was the primary variance driver, replication had a greater influence than depth for optimizing detection power. With only 2 replicates, over 80% of the ∼2000 differential genes were unique to specific depths, indicating high variability. Increasing to 4 replicates substantially improved reproducibility, with over 550 genes consistently identified across most depths, representing 30% of the total differential genes. Higher replicates also increased the rate of overlap of benchmark dose pathways and precision of median benchmark dose estimates. However, key gene ontology pathways related to DNA replication, cell cycle, and division were consistently captured even at lower replicates. Thus, replication enhanced confidence but did not fundamentally expand biological findings. Our study delineates key trade-offs between sequencing depth and replication for toxicogenomic experimental design. While additional replicates fundamentally improve reproducibility, gains from depth exhibit diminishing returns. Prioritizing biological replication over depth provides a cost-effective approach to enhance interpretation without sacrificing detection of core gene expression patterns. Altogether, this study provides important insights into the experimental design of toxicogenomics experiments.