Integrating data gap filling techniques: A case study predicting TEFs for neurotoxicity TEQs to facilitate the hazard assessment of polychlorinated biphenyls

Quantifying the Benefits of Imputation over QSAR Methods in Toxicology Data Modeling

Imputation machine learning (ML) surpasses traditional approaches in modeling toxicity data. The method was tested on an open-source data set comprising approximately 2500 ingredients with limited in vitro and in vivo data obtained from the OECD QSAR Toolbox. By leveraging the relationships between different toxicological end points, imputation extracts more valuable information from each data point compared to well-established single end point methods, such as ML-based Quantitative Structure Activity Relationship (QSAR) approaches, providing a final improvement of up to around 0.2 in the coefficient of determination. A significant aspect of this methodology is its resilience to the inclusion of extraneous chemical or experimental data. While additional data typically introduces a considerable level of noise and can hinder performance of single end point QSAR modeling, imputation models remain unaffected. This implies a reduction in the need for laborious manual preprocessing tasks such as feature selection, thereby making data preparation for ML analysis more efficient. This successful test, conducted on open-source data, validates the efficacy of imputation approaches in toxicity data analysis. This work opens the way for applying similar methods to other types of sparse toxicological data matrices, and so we discuss the development of regulatory authority guidelines to accept imputation models, a key aspect for the wider adoption of these methods.

Predicted versus observed activity of PCB mixtures toward the ryanodine receptor

Non-dioxin-like polychlorinated biphenyls (NDL PCBs) alter the activity of the ryanodine receptor (RyR), and this activity is linked to developmental neurotoxicity. Most work to date has focused on the activity of single congeners rather than relevant mixtures. The current study assessed the RyR activity of single congeners or binary, tertiary, and complex PCB mixtures. Observed mixture activity was then compared to the expected activity calculated using the concentration addition (CA) model or a RyR-specific neurotoxic equivalency scheme (rNEQ). The predictions of the CA model were consistent with the observed activity of binary mixtures at the lower portion of the concentration-response curve, supporting the additivity of RyR1 active PCBs. Findings also show that minimally active congeners can compete for the RyR1 binding site, and congeners that do not activate the RyR1 do not interfere with the activity of a full agonist. Complex PCB mixtures that mimic PCB profiles detected in indoor air, fish tissue, and the serum of mothers and children activated the RyR1 and displayed similar efficacy and potency regardless of varying congener profiles. Neither the CA model nor the rNEQ perfectly predicted the observed activity of complex mixtures, but predictions were often within one magnitude of change from the observed response. Importantly, PCB mixtures approximating profiles found in environmental samples or human serum displayed RyR1 activity at concentrations reported in published research. The work presented will aid in the development of risk assessment platforms for NDL PCBs and similar compounds toward RyR1 activation and related neurotoxicity.

Predicted Versus Observed Activity of PCB Mixtures Toward the Ryanodine Receptor

Non-dioxin-like polychlorinated biphenyls (NDL PCBs) alter the activity of the ryanodine receptor (RyR), and this activity is linked to developmental neurotoxicity. Most work to date has focused on the activity of single congeners rather than relevant mixtures. The current study assessed the RyR activity of single congeners or binary, tertiary, and complex PCB mixtures. Observed mixture activity was then compared to the expected activity calculated using the concentration addition (CA) model or a RyR-specific neurotoxic equivalency scheme (rNEQ). The predictions of the CA model were consistent with the observed activity of binary mixtures at the lower portion of the concentration-response curve, supporting the additivity of RyR1 active PCBs. Findings also show that minimally active congeners can compete for the RyR1 binding site, and congeners that do not activate the RyR1 do not interfere with the activity of a full agonist. Complex PCB mixtures that mimic PCB profiles detected in indoor air, fish tissue, and the serum of mothers and children activated the RyR1 and displayed similar efficacy and potency regardless of varying congener profiles. Neither the CA model nor the rNEQ perfectly predicted the observed activity of complex mixtures, but predictions were often within one magnitude of change from the observed response. Importantly, PCB mixtures approximating profiles found in environmental samples or human serum displayed RyR1 activity at concentrations reported in published research. The work presented will aid in the development of risk assessment platforms for NDL PCBs, and similar compounds, towards RyR1 activation and related neurotoxicity.

Evaluating the utility of a high throughput thiol-containing fluorescent probe to screen for reactivity: A case study with the Tox21 library

High-throughput screening (HTS) assays for bioactivity in the Tox21 program aim to evaluate an array of different biological targets and pathways, but a significant barrier to interpretation of these data is the lack of high-throughput screening (HTS) assays intended to identify non-specific reactive chemicals. This is an important aspect for prioritising chemicals to test in specific assays, identifying promiscuous chemicals based on their reactivity, as well as addressing hazards such as skin sensitisation which are not necessarily initiated by a receptor-mediated effect but act through a non-specific mechanism. Herein, a fluorescence-based HTS assay that allows the identification of thiol-reactive compounds was used to screen 7,872 unique chemicals in the Tox21 10K chemical library. Active chemicals were compared with profiling outcomes using structural alerts encoding electrophilic information. Random Forest classification models based on chemical fingerprints were developed to predict assay outcomes and evaluated through 10-fold stratified cross validation (CV). The mean CV Balanced Accuracy of the validation set was 0.648. The model developed shows promise as a tool to screen untested chemicals for their potential electrophilic reactivity based solely on chemical structural features.

Influence of Source Apportionment of PAHs Occurrence in Aquatic Suspended Particulate Matter at a Typical Post-Industrial City: A Case Study of Freiberger Mulde River

Polycyclic aromatic hydrocarbons (PAHs) have received extensive attention because of their widespread presence in various environmental media and their high environmental toxicity. Thus, figuring out the long-term variances of their occurrence and driving force in the environment is helpful for environmental pollution control. This study investigates the concentration levels, spatial variance, and source apportionment of PAHs in suspended particulate matter of Freiberger Mulde river, Germany. Results show that the concentrations of the 16 priority PAHs suggested by USEPA (Σ16PAHs) were in the range of 707.0–17,243.0 μg kg−1 with a mean value of 5258.0 ± 2569.2 μg kg−1 from 2002 to 2016. The relatively high average concentrations of Σ16PAHs were found in the midstream and upstream stations of the given river (7297.5 and 6096.9 μg kg−1 in Halsbrucke and Hilbersdorf, respectively). In addition, the annual average concentration of Σ16PAHs showed an obvious decreasing pattern with time. Positive Matrix Factorization (PMF) receptor model identified three potential sources: coke ovens (7.6–23.0%), vehicle emissions (35.9–47.7%), and coal and wood combustion (34.5–47.3%). The source intensity variation and wavelet coherence analysis indicated that the use of clean energy played a key role in reducing PAHs pollution levels in suspended sediments. The risk assessment of ecosystem and human health suggested that the Σ16PAHs in the given area posed a non-negligible threat to aquatic organisms and humans. The data provided herein could assist the subsequent management of PAHs in the aquatic environment.

How can we justify grouping of nanoforms for hazard assessment? Concepts and tools to quantify similarity

The risk of each nanoform (NF) of the same substance cannot be assumed to be the same, as they may vary in their physicochemical characteristics, exposure and hazard. However, neither can we justify a need for more animal testing and resources to test every NF individually. To reduce the need to test all NFs, (regulatory) information requirements may be fulfilled by grouping approaches. For such grouping to be acceptable, it is important to demonstrate similarities in physicochemical properties, toxicokinetic behaviour, and (eco)toxicological behaviour. The GRACIOUS Framework supports the grouping of NFs, by identifying suitable grouping hypotheses that describe the key similarities between different NFs. The Framework then supports the user to gather the evidence required to test these hypotheses and to subsequently assess the similarity of the NFs within the proposed group. The evidence needed to support a hypothesis is gathered by an Integrated Approach to Testing and Assessment (IATA), designed as decision trees constructed of decision nodes. Each decision node asks the questions and provides the methods needed to obtain the most relevant information. This White paper outlines existing and novel methods to assess similarity of the data generated for each decision node, either via a pairwise analysis conducted property-by-property, or by assessing multiple decision nodes simultaneously via a multidimensional analysis. For the pairwise comparison conducted property-by-property we included in this White paper: The x-fold, Bayesian and Arsinh-OWA distance algorithms performed comparably in the scoring of similarity between NF pairs. The Euclidean distance was also useful, but only with proper data transformation. The x-fold method does not standardize data, and thus produces skewed histograms, but has the advantage that it can be implemented without programming knowhow. A range of multidimensional evaluations, using for example dendrogram clustering approaches, were also investigated. Multidimensional distance metrics were demonstrated to be difficult to use in a regulatory context, but from a scientific perspective were found to offer unexpected insights into the overall similarity of very different materials. In conclusion, for regulatory purposes, a property-by-property evaluation of the data matrix is recommended to substantiate grouping, while the multidimensional approaches are considered to be tools of discovery rather than regulatory methods.

Non-dioxin-like polychlorinated biphenyl neurotoxic equivalents found in environmental and human samples

Non-dioxin like polychlorinated biphenyls (NDL PCB) are recognized neurotoxicants with implications on altered neurodevelopment and neurodegeneration in exposed organisms. NDL PCB neurotoxic relative potency schemes have been developed for a single mechanism, namely activity toward the ryanodine receptor (RyR), or combined mechanisms including, but not limited to, alterations of RyR and dopaminergic pathways. We compared the applicability of the two neurotoxic equivalency (NEQ) schemes and applied each scheme to PCB mixtures found in environmental and human serum samples. A multiple mechanistic NEQ predicts higher neurotoxic exposure concentrations as compared to a scheme based on the RyR alone. Predictions based on PCB ortho categorization, versus homologue categorization, lead to a higher prediction of neurotoxic exposure concentrations, especially for the mMOA. The application of the NEQ schemes to PCB concentration data suggests that PCBs found in fish from US lakes represent a considerable NEQ exposure to fish consuming individuals, that indoor air of schools contained high NEQ concentrations representing an exposure concern when inhaled by children, and that levels already detected in the serum of adults and children may contribute to neurotoxicity. With further validation and in vivo exposure data the NEQ scheme would help provide a more inclusive measure of risk presented by PCB mixtures.

Polychlorinated Biphenyls in Food

We measured the concentrations of 205 polychlorinated biphenyl (PCB) congeners in 26 food items: beef steak, butter, canned tuna, catfish, cheese, eggs, french fries, fried chicken, ground beef, ground pork, hamburger, hot dog, ice cream, liver, luncheon meat, margarine, meat-free dinner, milk, pizza, poultry, salmon, sausage, shrimp, sliced ham, tilapia, and vegetable oil. Using Diet History Questionnaire II, we calculated the PCB dietary exposure in mothers and children participating in the AESOP Study in East Chicago, Indiana, and Columbus Junction, Iowa. Salmon had the highest concentration followed by canned tuna, but fish is a minor contributor to exposure. Other animal proteins are more important sources of PCB dietary exposure in this study population. Despite the inclusion of few congeners and food types in previous studies, we found evidence of a decline in PCB concentrations over the last 20 years. We also found strong associations of PCB congener distributions with Aroclors in most foods and found manufacturing byproduct PCBs, including PCB11, in tilapia and catfish. The reduction in PCB levels in food indicates that dietary exposure is comparable to PCB inhalation exposures reported for the same study population.

Practices and Trends of Machine Learning Application in Nanotoxicology

Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications.