Current and Future Perspectives on the Development, Evaluation, and Application of in Silico Approaches for Predicting Toxicity

Drug hypersensitivity reactions: review of the state of the science for prediction and diagnosis

Drug hypersensitivity reactions (DHRs) are a type of adverse drug reaction that can occur with different classes of drugs and affect multiple organ systems and patient populations. DHRs can be classified as allergic or non-allergic based on the cellular mechanisms involved. Whereas nonallergic reactions rely mainly on the innate immune system, allergic reactions involve the generation of an adaptive immune response. Consequently, drug allergies are DHRs for which an immunological mechanism, with antibody and/or T cell, is demonstrated. Despite decades of research, methods to predict the potential for a new chemical entity to cause DHRs or to correctly attribute DHRs to a specific mechanism and a specific molecule are not well-established. This review will focus on allergic reactions induced by systemically administered low-molecular weight drugs with an emphasis on drug- and patient-specific factors that could influence the development of DHRs. Strategies for predicting and diagnosing DHRs, including potential tools based on the current state of the science, will also be discussed.

ARKA: a framework of dimensionality reduction for machine-learning classification modeling, risk assessment, and data gap-filling of sparse environmental toxicity data

Due to the lack of experimental toxicity data for environmental chemicals, there arises a need to fill data gaps by in silico approaches. One of the most commonly used in silico approaches for toxicity assessment of small datasets is the Quantitative Structure-Activity Relationship (QSAR), which generates predictive models for the efficient prediction of query compounds. However, the reliability of the predictions from QSARs derived from small datasets is often questionable from a statistical point of view. This is due to the presence of a larger number of descriptors as compared to the number of training compounds, which reduces the degree of freedom of the developed model. To reduce the overall prediction error for a particular QSAR model, we have proposed here the computation of the novel Arithmetic Residuals in K-groups Analysis (ARKA) descriptors. We have reduced the number of modeling descriptors in a supervised manner by partitioning them into K classes (K = 2 here) depending on the higher mean normalized values of the descriptors to a particular response class, thus preventing the loss of chemical information. A scatter plot of the data points using the values of two ARKA descriptors (ARKA_2 vs. ARKA_1) can potentially identify activity cliffs, less confident data points, and less modelable data points. We have used here five representative environmentally relevant endpoints (skin sensitization, earthworm toxicity, milk/plasma partitioning, algal toxicity, and rodent carcinogenicity of hazardous chemicals) with graded responses to which the ARKA framework was applied for classification modeling. On comparing the performance of the models generated using conventional QSAR descriptors and the ARKA descriptors, the prediction quality of the models derived from ARKA descriptors was found, based on multiple graded-data validation metrics-derived decision criteria, much better than the models derived from QSAR descriptors signifying the potential of ARKA descriptors in ecotoxicological classification modeling of small data sets. Additionally, this holds true for the Read-Across approach as well, since the Read-Across predictions using ARKA descriptors supersede the predictions generated from QSAR descriptors. For the ease of users, a Java-based expert system has been developed that computes the ARKA descriptors from the input of QSAR descriptors.

A novel approach to identify critical knowledge gaps for food safety in circular food systems

The transition from linear production towards a circular agro-food system is an important step towards increasing Europe's sustainability. This requires re-designing the food production systems, which inevitably comes with challenges as regards controlling the safety of our food, animals and the ecosystem. Where in current food production systems many food safety hazards are understood and well-managed, it is anticipated that with the transition towards circular food production systems, known hazards may re-emerge and new hazards will appear or accumulate, leading to new -and less understood- food safety risks. In this perspective paper, we present a simple, yet effective approach, to identify knowledge gaps with regard to food safety in the transition to a circular food system. An approach with five questions is proposed, derived from current food safety management approaches like HACCP. Applying this to two cases shows that risk assessment and management should emphasize more on the exposure to unexpected (with regards to its nature and its origin) hazards, as hazards might circulate and accumulate in the food production system. Five knowledge gaps became apparent: there's a need for (1) risk assessment and management to focus more on unknown hazards and mixtures of hazards, (2) more data on the occurrence of hazards in by-products, (3) better understanding the fate of hazards in the circular food production system, (4) the development of models to adequately perform risk assessments for a broad range of hazards and (5) new ways of valorization of co-products in which a safe-by-design approach should be adopted.

In silico prediction of ocular toxicity of compounds using explainable machine learning and deep learning approaches

The accurate identification of chemicals with ocular toxicity is of paramount importance in health hazard assessment. In contemporary chemical toxicology, there is a growing emphasis on refining, reducing, and replacing animal testing in safety evaluations. Therefore, the development of robust computational tools is crucial for regulatory applications. The performance of predictive models is heavily reliant on the quality and quantity of data. In this investigation, we amalgamated the most extensive dataset (4901 compounds) sourced from governmental GHS-compliant databases and literature to develop binary classification models of chemical ocular toxicity. We employed 12 molecular representations in conjunction with six machine learning algorithms and two deep learning algorithms to create a series of binary classification models. The findings indicated that the deep learning method GCN outperformed the machine learning models in cross-validation, achieving an impressive AUC of 0.915. However, the top-performing machine learning model (RF-Descriptor) demonstrated excellent performance with an AUC of 0.869 on the test set and was therefore selected as the best model. To enhance model interpretability, we conducted the SHAP method and attention weights analysis. The two approaches offered visual depictions of the relevance of key descriptors and substructures in predicting ocular toxicity of chemicals. Thus, we successfully struck a delicate balance between data quality and model interpretability, rendering our model valuable for predicting and comprehending potential ocular-toxic compounds in the early stages of drug discovery.

Advancing Adverse Drug Reaction Prediction with Deep Chemical Language Model for Drug Safety Evaluation

The accurate prediction of adverse drug reactions (ADRs) is essential for comprehensive drug safety evaluation. Pre-trained deep chemical language models have emerged as powerful tools capable of automatically learning molecular structural features from large-scale datasets, showing promising capabilities for the downstream prediction of molecular properties. However, the performance of pre-trained chemical language models in predicting ADRs, especially idiosyncratic ADRs induced by marketed drugs, remains largely unexplored. In this study, we propose MoLFormer-XL, a pre-trained model for encoding molecular features from canonical SMILES, in conjunction with a CNN-based model to predict drug-induced QT interval prolongation (DIQT), drug-induced teratogenicity (DIT), and drug-induced rhabdomyolysis (DIR). Our results demonstrate that the proposed model outperforms conventional models applied in previous studies for predicting DIQT, DIT, and DIR. Notably, an analysis of the learned linear attention maps highlights amines, alcohol, ethers, and aromatic halogen compounds as strongly associated with the three types of ADRs. These findings hold promise for enhancing drug discovery pipelines and reducing the drug attrition rate due to safety concerns.

In Silico Prediction of Chemical Acute Dermal Toxicity Using Explainable Machine Learning Methods

The research on acute dermal toxicity has consistently been a crucial component in assessing the potential risks of human exposure to active ingredients in pesticides and related plant protection products. However, it is difficult to directly identify the acute dermal toxicity of potential compounds through animal experiments alone. In our study, we separately integrated 1735 experimental data based on rabbits and 1679 experimental data based on rats to construct acute dermal toxicity prediction models using machine learning and deep learning algorithms. The best models for the two animal species achieved AUC values of 78.0 and 82.0%, respectively, on 10-fold cross-validation. Additionally, we employed SARpy to extract structural alerts, and in conjunction with Shapley additive explanation and attentive FP heatmap, we identified important features and structural fragments associated with acute dermal toxicity. This approach offers valuable insights for the detection of positive compounds. Moreover, a standalone software tool was developed to make acute dermal toxicity prediction easier. In summary, our research would provide an effective tool for acute dermal toxicity evaluation of pesticides, cosmetics, and drug safety assessment.

A Comparison of Machine Learning Approaches for predicting Hepatotoxicity potential using Chemical Structure and Targeted Transcriptomic Data

Animal toxicity testing is time and resource intensive, making it difficult to keep pace with the number of substances requiring assessment. Machine learning (ML) models that use chemical structure information and high-throughput experimental data can be helpful in predicting potential toxicity . However, much of the toxicity data used to train ML models is biased with an unequal balance of positives and negatives primarily since substances selected for in vivo testing are expected to elicit some toxicity effect. To investigate the impact this bias had on predictive performance, various sampling approaches were used to balance in vivo toxicity data as part of a supervised ML workflow to predict hepatotoxicity outcomes from chemical structure and/or targeted transcriptomic data. From the chronic, subchronic, developmental, multigenerational reproductive, and subacute repeat-dose testing toxicity outcomes with a minimum of 50 positive and 50 negative substances, 18 different study-toxicity outcome combinations were evaluated in up to 7 ML models. These included Artificial Neural Networks, Random Forests, Bernouilli Naïve Bayes, Gradient Boosting, and Support Vector classification algorithms which were compared with a local approach, Generalised Read-Across (GenRA), a similarity-weighted k-Nearest Neighbour (k-NN) method. The mean CV F1 performance for unbalanced data across all classifiers and descriptors for chronic liver effects was 0.735 (0.0395 SD). Mean CV F1 performance dropped to 0.639 (0.073 SD) with over-sampling approaches though the poorer performance of KNN approaches in some cases contributed to the observed decrease (mean CV F1 performance excluding KNN was 0.697 (0.072 SD)). With under-sampling approaches, the mean CV F1 was 0.523 (0.083 SD). For developmental liver effects, the mean CV F1 performance was much lower with 0.089 (0.111 SD) for unbalanced approaches and 0.149 (0.084 SD) for under-sampling. Over-sampling approaches led to an increase in mean CV F1 performance (0.234, (0.107 SD)) for developmental liver toxicity. Model performance was found to be dependent on dataset, model type, balancing approach and feature selection. Accordingly tailoring ML workflows for predicting toxicity should consider class imbalance and rely on simpler classifiers first.

A review on potential approach for in silico toxicity analysis of respirable fraction of ambient particulate matter

Epidemiological and toxicological studies have shown the adverse effect of ambient particulate matter (PM) on respiratory and cardiovascular systems inside the human body. Various cellular and acellular assays in literature use indicators like ROS generation, cell inflammation, mutagenicity, etc., to assess PM toxicity and associated health effects. The presence of toxic compounds in respirable PM needs detailed studies for proper understanding of absorption, distribution, metabolism, and excretion mechanisms inside the body as it is difficult to accurately imitate or simulate these mechanisms in lab or animal models. The leaching kinetics of the lung fluid, PM composition, retention time, body temperature, etc., are hard to mimic in an artificial experimental setup. Moreover, the PM size fraction also plays an important role. For example, the ultrafine particles may directly enter systemic circulations while coarser PM10 may be trapped and deposited in the tracheo-bronchial region. Hence, interpretation of these results in toxicity models should be done judiciously. Computational models predicting PM toxicity are rare in the literature. The variable composition of PM and lack of proper understanding for their synergistic role inside the body are prime reasons behind it. This review explores different possibilities of in silico modeling and suggests possible approaches for the risk assessment of PM particles. The toxicity testing approach for engineered nanomaterials, drugs, food industries, etc., have also been investigated for application in computing PM toxicity.

Skin sensitisation prediction using read-across, an illustrative next generation risk assessment (NGRA) case study for vanillin

Skin sensitisation is a key adverse human health effect to be addressed in the safety assessment of cosmetic ingredients. Regulatory demands and scientific progress have led to the development of a Next Generation Risk Assessment (NGRA) framework, relying on the use of New Approach Methodologies (NAM) Defined Approaches (DA) and read-across instead of generating animal data. This case study illustrates the application of read-across for the prediction of the skin sensitisation potential of vanillin at the hypothetical use concentration of 0.5% in a shower gel and face cream. A three-step process was applied to select the most suitable analogues based on their protein reactivity, structural characteristics, physicochemical properties, skin metabolism profile and availability of skin sensitisation data. The applied read-across approach predicted a weak skin sensitiser potential for vanillin corresponding with a Local Lymph Node Assay EC3 value of 10%. Based on this EC3 value a point of departure of 2500 μg/cm2 was derived, resulting in an acceptable exposure level (AEL) of 25 μg/cm2. Because the consumer exposure levels (CEL) for the face cream (13.5 μg/cm2) and shower gel (0.05 μg/cm2) scenarios were lower than the AEL, the NGRA concluded both uses as safe.

Systematic computational toxicity analysis of the ozonolytic degraded compounds of azo dyes: Quantitative structure-activity relationship (QSAR) and adverse outcome pathway (AOP) based approach

The present study identifies and analyses the degraded products of three azo dyes (Reactive Orange 16, Reactive Red 120, and Direct Red 80) and proffers their in silico toxicity predictions. In our previously published work, the synthetic dye effluents were degraded using an ozonolysis-based Advanced Oxidation Process. In the present study, the degraded products of the three dyes were analysed using GC-MS at endpoint strategy and further subjected to in silico toxicity analysis using Toxicity Estimation Software Tool (TEST), Prediction Of TOXicity of chemicals (ProTox-II), and Estimation Programs Interface Suite (EPI Suite). Several physiological toxicity endpoints, such as hepatotoxicity, carcinogenicity, mutagenicity, cellular and molecular interactions, were considered to assess the Quantitative Structure-Activity Relationships (QSAR) and adverse outcome pathways. The environmental fate of the by-products in terms of their biodegradability and possible bioaccumulation was also assessed. Results of ProTox-II suggested that the azo dye degradation products are carcinogenic, immunotoxic, and cytotoxic and displayed toxicity towards Androgen Receptor and Mitochondrial Membrane Potential. TEST results predicted LC50 and IGC50 values for three organisms Tetrahymena pyriformis, Daphnia magna, and Pimephales promelas. EPISUITE software via the BCFBAF module surmises that the degradation products' bioaccumulation (BAF) and bioconcentration factors (BCF) are high. The cumulative inference of the results suggests that most degradation by-products are toxic and need further remediation strategies. The study aims to complement existing tests to predict toxicity and prioritise the elimination/reduction of harmful degradation products of primary treatment procedures. The novelty of this study is that it streamlines in silico approaches to predict the nature of toxicity of degradation by-products of toxic industrial affluents like azo dyes. These approaches can assist the first phase of toxicology assessments for any pollutant for regulatory decision-making bodies to chalk out appropriate action plans for their remediation.

An unexpected single crystal structure of nickel(II) complex: Spectral, DFT, NLO, magnetic and molecular docking studies

This work explores the study of a synthesized nickel complex as a possible inhibitor against the main protease (Mpro) of the recent emerging coronavirus disease (COVID-19). Overall, the template reaction of 3-acetyl-2-hydroxy-6-methyl-4H-pyran-4-one with nickel(II) chloride hexahydrate in N,N-dimethylformamide (DMF) medium leads to the formation of neutral nickel complex. This resulting complex is formulated as [Ni(DHA)₂(DMF)₂] on the basis of FT-IR, UV-Vis., single-crystal X-ray diffraction analysis, magnetic susceptibility and CV measurements as well as DFT quantum chemical calculations. Its single crystal suggests was found to be surrounded by the both pairs of molecules of DHA and DMF through six oxygen atoms with octahedral coordination sphere. The obtained magnetic susceptibilities are positive and agree with its paramagnetic state. In addition to the experimental investigations, optimized geometry, spectroscopic and electronic properties were also performed using DFT calculation with B3LYP/6-31G(d,p) level of theory. The nonlinear optical (NLO) properties of this complex are again examined. Some suitable quantum descriptors (E_HOMO, E_LUMO, Energy gap, Global hardness), Milliken atomic charge, Electrophilic potion and Molecular Electrostatic Potential) have been elegantly described. Molecular docking results demonstrated that the docked nickel complex displayed remarkable binding energy with Mpro. Besides, important molecular properties and ADME pharmacokinetic profiles of possible Mpro inhibitors were assessed by in silico prediction.

Commentary: cumulative risk assessment of perfluoroalkyl carboxylic acids and perfluoralkyl sulfonic acids: what is the scientific support for deriving tolerable exposures by assembling 27 PFAS into 1 common assessment group?

This commentary proposes an approach to risk assessment of mixtures of per- and polyfluorinated alkyl substances (PFAS) as EFSA was tasked to derive a tolerable intake for a group of 27 PFAS. The 27 PFAS to be considered contain different functional groups and have widely variable physicochemical (PC) properties and toxicokinetics and thus should not treated as one group based on regulatory guidance for risk assessment of mixtures. The proposed approach to grouping is to split the 27 PFAS into two groups, perfluoroalkyl carboxylates and perfluoroalkyl sulfonates, and apply a relative potency factor approach (as proposed by RIVM) to obtain two separate group TDIs based on liver toxicity in rodents since liver toxicity is a sensitive response of rodents to PFAS. Short chain PFAS and other PFAS structures should not be included in the groups due to their low potency and rapid elimination. This approach is in better agreement with scientific and regulatory guidance for mixture risk assessment.

STopTox: An in Silico Alternative to Animal Testing for Acute Systemic and Topical Toxicity

BACKGROUND

Modern chemical toxicology is facing a growing need to Reduce, Refine, and Replace animal tests (Russell 1959) for hazard identification. The most common type of animal assays for acute toxicity assessment of chemicals used as pesticides, pharmaceuticals, or in cosmetic products is known as a "6-pack" battery of tests, including three topical (skin sensitization, skin irritation and corrosion, and eye irritation and corrosion) and three systemic (acute oral toxicity, acute inhalation toxicity, and acute dermal toxicity) end points.

METHODS

We compiled, curated, and integrated, to the best of our knowledge, the largest publicly available data sets and developed an ensemble of quantitative structure-activity relationship (QSAR) models for all six end points. All models were validated according to the Organisation for Economic Co-operation and Development (OECD) QSAR principles, using data on compounds not included in the training sets.

RESULTS

In addition to high internal accuracy assessed by cross-validation, all models demonstrated an external correct classification rate ranging from 70% to 77%. We established a publicly accessible Systemic and Topical chemical Toxicity (STopTox) web portal (https://stoptox.mml.unc.edu/) integrating all developed models for 6-pack assays.

CONCLUSIONS

We developed STopTox, a comprehensive collection of computational models that can be used as an alternative to in vivo 6-pack tests for predicting the toxicity hazard of small organic molecules. Models were established following the best practices for the development and validation of QSAR models. Scientists and regulators can use the STopTox portal to identify putative toxicants or nontoxicants in chemical libraries of interest. https://doi.org/10.1289/EHP9341.

Integrating publicly available information to screen potential candidates for chemical prioritization under the Toxic Substances Control Act: A proof of concept case study using genotoxicity and carcinogenicity

The Toxic Substances Control Act (TSCA) became law in the U.S. in 1976 and was amended in 2016. The amended law requires the U.S. EPA to perform risk-based evaluations of existing chemicals. Here, we developed a tiered approach to screen potential candidates based on their genotoxicity and carcinogenicity information to inform the selection of candidate chemicals for prioritization under TSCA. The approach was underpinned by a large database of carcinogenicity and genotoxicity information that had been compiled from various public sources. Carcinogenicity data included weight-of-evidence human carcinogenicity evaluations and animal cancer data. Genotoxicity data included bacterial gene mutation data from the Salmonella (Ames) and Escherichia coli WP2 assays and chromosomal mutation (clastogenicity) data. Additionally, Ames and clastogenicity outcomes were predicted using the alert schemes within the OECD QSAR Toolbox and the Toxicity Estimation Software Tool (TEST). The evaluation workflows for carcinogenicity and genotoxicity were developed along with associated scoring schemes to make an overall outcome determination. For this case study, two sets of chemicals, the TSCA Active Inventory non-confidential portion list available on the EPA CompTox Chemicals Dashboard (33,364 chemicals, 'TSCA Active List') and a representative proof-of-concept (POC) set of 238 chemicals were profiled through the two workflows to make determinations of carcinogenicity and genotoxicity potential. Of the 33,364 substances on the 'TSCA Active List', overall calls could be made for 20,371 substances. Here 46.67%% (9507) of substances were non-genotoxic, 0.5% (103) were scored as inconclusive, 43.93% (8949) were predicted genotoxic and 8.9% (1812) were genotoxic. Overall calls for genotoxicity could be made for 225 of the 238 POC chemicals. Of these, 40.44% (91) were non-genotoxic, 2.67% (6) were inconclusive, 6.22% (14) were predicted genotoxic, and 50.67% (114) genotoxic. The approach shows promise as a means to identify potential candidates for prioritization from a genotoxicity and carcinogenicity perspective.

Toward regulatory acceptance and improving the prediction confidence of in silico approaches: a case study of genotoxicity

Introduction: Genotoxicity is an imperative component of the human health safety assessment of chemicals. Its secure forecast is of the utmost importance for all health prevention strategies and regulations.Areas covered: We surveyed several types of alternative, animal-free approaches ((quantitative) structure-activity relationship (Q)SAR, read-across, Adverse Outcome Pathway, Integrated Approaches to Testing and Assessment) for genotoxicity prediction within the needs of regulatory frameworks, putting special emphasis on data quality and uncertainties issues.Expert opinion: (Q)SAR models and read-across approaches for in vitro bacterial mutagenicity have sufficient reliability for use in prioritization processes, and as support in regulatory decisions in combination with other types of evidence. (Q)SARs and read-across methodologies for other genotoxicity endpoints need further improvements and should be applied with caution. It appears that there is still large room for improvement of genotoxicity prediction methods. Availability of well-curated high-quality databases, covering a broader chemical space, is one of the most important needs. Integration of in silico predictions with expert knowledge, weight-of-evidence-based assessment, and mechanistic understanding of genotoxicity pathways are other key points to be addressed for the generation of more accurate and trustable results.

Machine learning-integrated omics for the risk and safety assessment of nanomaterials

With the advancement in nanotechnology, we are experiencing transformation in world order with deep insemination of nanoproducts from basic necessities to advanced electronics, health care products and medicines. Therefore, nanoproducts, however, can have negative side effects and must be strictly monitored to avoid negative outcomes. Future toxicity and safety challenges regarding nanomaterial incorporation into consumer products, including rapid addition of nanomaterials with diverse functionalities and attributes, highlight the limitations of traditional safety evaluation tools. Currently, artificial intelligence and machine learning algorithms are envisioned for enhancing and improving the nano-bio-interaction simulation and modeling, and they extend to the post-marketing surveillance of nanomaterials in the real world. Thus, hyphenation of machine learning with biology and nanomaterials could provide exclusive insights into the perturbations of delicate biological functions after integration with nanomaterials. In this review, we discuss the potential of combining integrative omics with machine learning in profiling nanomaterial safety and risk assessment and provide guidance for regulatory authorities as well.

Curated Data In - Trustworthy In Silico Models Out: The Impact of Data Quality on the Reliability of Artificial Intelligence Models as Alternatives to Animal Testing

New Approach Methodologies (NAMs) that employ artificial intelligence (AI) for predicting adverse effects of chemicals have generated optimistic expectations as alternatives to animal testing. However, the major underappreciated challenge in developing robust and predictive AI models is the impact of the quality of the input data on the model accuracy. Indeed, poor data reproducibility and quality have been frequently cited as factors contributing to the crisis in biomedical research, as well as similar shortcomings in the fields of toxicology and chemistry. In this article, we review the most recent efforts to improve confidence in the robustness of toxicological data and investigate the impact that data curation has on the confidence in model predictions. We also present two case studies demonstrating the effect of data curation on the performance of AI models for predicting skin sensitisation and skin irritation. We show that, whereas models generated with uncurated data had a 7-24% higher correct classification rate (CCR), the perceived performance was, in fact, inflated owing to the high number of duplicates in the training set. We assert that data curation is a critical step in building computational models, to help ensure that reliable predictions of chemical toxicity are achieved through use of the models.

Root causes of adverse drug events in hospitals and artificial intelligence capabilities for prevention

AIMS

To identify and prioritize the root causes of adverse drug events (ADEs) in hospitals and to assess the ability of artificial intelligence (AI) capabilities to prevent ADEs.

DESIGN

A mixed method design was used.

METHODS

A cross-sectional study for hospitals in Spain was carried out between February and April 2019 to identify and prioritize the root causes of ADEs. A nominal group technique was also used to assess the ability of AI capabilities to prevent ADEs.

RESULTS

The main root cause of ADEs was a lack of adherence to safety protocols (64.8%), followed by identification errors (57.4%), and fragile and polymedicated patients (44.4%). An analysis of the AI capabilities to prevent the root causes of ADEs showed that identification and reading are two potentially useful capabilities.

CONCLUSION

Identification error is one of the main root causes of drug adverse events and AI capabilities could potentially prevent drug adverse events.

IMPACT

This study highlights the role of AI capabilities in safely identifying both patients and drugs, which is a crucial part of the medication administration process, and how this can prevent ADEs in hospitals.

Organizing mechanism-related information on chemical interactions using a framework based on the aggregate exposure and adverse outcome pathways

This paper presents a framework for organizing and accessing mechanistic data on chemical interactions. The framework is designed to support the assessment of risks from combined chemical exposures. The framework covers interactions between chemicals that occur over the entire source-to-outcome continuum including interactions that are studied in the fields of chemical transport, environmental fate, exposure assessment, dosimetry, and individual and population-based adverse outcomes. The framework proposes to organize data using a semantic triple of a chemical (subject), has impact (predicate), and a causal event on the source-to-outcome continuum of a second chemical (object). The location of the causal event on the source-to-outcome continuum and the nature of the impact are used as the basis for a taxonomy of interactions. The approach also builds on concepts from the Aggregate Exposure Pathway (AEP) and Adverse Outcome Pathway (AOP). The framework proposes the linking of AEPs of multiple chemicals and the AOP networks relevant to those chemicals to form AEP-AOP networks that describe chemical interactions that cannot be characterized using AOP networks alone. Such AEP-AOP networks will aid the construction of workflows for both experimental design and the systematic review or evaluation performed in risk assessments. Finally, the framework is used to link the constructs of existing component-based approaches for mixture toxicology to specific categories in the interaction taxonomy.

The EDCMET Project: Metabolic Effects of Endocrine Disruptors

Endocrine disruptors (EDs) are defined as chemicals that mimic, block, or interfere with hormones in the body's endocrine systems and have been associated with a diverse array of health issues. The concept of endocrine disruption has recently been extended to metabolic alterations that may result in diseases, such as obesity, diabetes, and fatty liver disease, and constitute an increasing health concern worldwide. However, while epidemiological and experimental data on the close association of EDs and adverse metabolic effects are mounting, predictive methods and models to evaluate the detailed mechanisms and pathways behind these observed effects are lacking, thus restricting the regulatory risk assessment of EDs. The EDCMET (Metabolic effects of Endocrine Disrupting Chemicals: novel testing METhods and adverse outcome pathways) project brings together systems toxicologists; experimental biologists with a thorough understanding of the molecular mechanisms of metabolic disease and comprehensive in vitro and in vivo methodological skills; and, ultimately, epidemiologists linking environmental exposure to adverse metabolic outcomes. During its 5-year journey, EDCMET aims to identify novel ED mechanisms of action, to generate (pre)validated test methods to assess the metabolic effects of Eds, and to predict emergent adverse biological phenotypes by following the adverse outcome pathway (AOP) paradigm.

Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across: An EFSA funded project

To facilitate the practical implementation of the guidance on the residue definition for dietary risk assessment, EFSA has organized an evaluation of applicability of existing in silico models for predicting the genotoxicity of pesticides and their metabolites, including literature survey, application of QSARs and development of Read Across methodologies. This paper summarizes the main results. For the Ames test, all (Q)SAR models generated statistically significant predictions, comparable with the experimental variability of the test. The reliability of the models for other assays/endpoints appears to be still far from optimality. Two new Read Across approaches were evaluated: Read Across was largely successful for predicting the Ames test results, but less for in vitro Chromosomal Aberrations. The worse results for non-Ames endpoints may be attributable to the several revisions of experimental protocols and evaluation criteria of results, that have made the databases qualitatively non-homogeneous and poorly suitable for modeling. Last, Parent/Metabolite structural differences (besides known Structural Alerts) that may, or may not cause changes in the Ames mutagenicity were identified and catalogued. The findings from this work are suitable for being integrated into Weight-of-Evidence and Tiered evaluation schemes. Areas needing further developments are pointed out.

In silico prediction of toxicity and its applications for chemicals at work

OBJECTIVE AND METHODS

This study reviewed the concept of in silico prediction of chemical toxicity for prevention of occupational cancer and future prospects in workers' health. In this review, a new approach to determine the credibility of in silico predictions with raw data is explored, and the method of determining the confidence level of evaluation based on the credibility of data is discussed. I searched various papers and books related to the in silico prediction of chemical toxicity and carcinogenicity. The intention was to utilize the most recent reports after 2015 regarding in silico prediction.

RESULTS AND CONCLUSION

The application of in silico methods is increasing with the prediction of toxic risks to human and the environment. The various toxic effects of industrial chemicals have triggered the recognition of the importance of using a combination of in silico models in the risk assessments. In silico occupational exposure models, industrial accidents, and occupational cancers are effectively managed and chemicals evaluated. It is important to identify and manage hazardous substances proactively through the rigorous evaluation of chemicals.

Pharmaceutical analysis combined with in-silico therapeutic and toxicological profiling on zileuton and its impurities to assist in modern drug discovery

The obligatory testing of drug molecules and their impurities to protect users against toxic compounds seems to provide interesting opportunities for new drug discovery. Impurities, which proved to be non-toxic, may be explored for their own therapeutic potential and thus be a part of future drug discovery. The essential role of pharmaceutical analysis can thus be extended to achieve this purpose. The present study examined these objectives by characterizing the major degradation products of zileuton (ZLT), a 5-lipoxygenase (5-LOX) inhibitor being prevalently used to treat asthma. The drug sample was exposed to forced degradation and found susceptible to hydrolysis and oxidative stress. The obtained Forced Degradation Products (FDP's) were resolved using an earlier developed and validated Ultra-High-Pressure Liquid Chromatography Photo-Diode-Array (UHPLC-PDA) protocol. ZLT, along with acid-and alkali-stressed samples, were subjected to Liquid-chromatography Mass-spectrometry Quadrupole Time-of-flight (LC/MS-QTOF) studies. Major degradation products were isolated using Preparative TLC and characterized using Q-TOF and/or Proton nuclear magnetic resonance (¹HNMR) studies. The information obtained was assembled for structural conformation. Toxicity Prediction using Komputer Assisted Technology (TOPKAT) toxicity analyses indicated some FDP's as non-toxic when compared to ZLT. Hence, these non-toxic impurities may have bio-affinity and can be explored to interact with other therapeutic targets, to assist in drug discovery. The drug molecule and the characterized FDP's were subjected to 3-Dimensional Extra Precision (3D-XP)-molecular docking to explore changes in bio-affinity for the 5-LOX enzyme (PDB Id: 3V99). One FDP was found to have a higher binding affinity than the drug itself, indicating it may be a suitable antiasthmatic. The possibility of being active at other sites cannot be neglected and this is evaluated to a reasonable extent by Prediction of Activity Spectra for Substances (PASS). Besides being antiasthmatic, some FDP's were predicted antineoplastic, antiallergic and inhibitors of Complement Factor-D.

Artificial Intelligence for Drug Toxicity and Safety

Interventional pharmacology is one of medicine's most potent weapons against disease. These drugs, however, can result in damaging side effects and must be closely monitored. Pharmacovigilance is the field of science that monitors, detects, and prevents adverse drug reactions (ADRs). Safety efforts begin during the development process, using in vivo and in vitro studies, continue through clinical trials, and extend to postmarketing surveillance of ADRs in real-world populations. Future toxicity and safety challenges, including increased polypharmacy and patient diversity, stress the limits of these traditional tools. Massive amounts of newly available data present an opportunity for using artificial intelligence (AI) and machine learning to improve drug safety science. Here, we explore recent advances as applied to preclinical drug safety and postmarketing surveillance with a specific focus on machine and deep learning (DL) approaches.

Towards quantitative read across: Prediction of Ames mutagenicity in a large database

In silico chemical safety assessment can support the evaluation of hazard and risk following potential exposure to a substance, thus stimulating an increased interest for the use of Structure-Activity based approaches by regulatory authorities, particularly QSAR and Read Across. Whereas the longer history of QSAR led to recognize the crucial requirements for predictivity, there are still challenges faced by adopting Read Across to a larger extent in a regulatory setting, namely standardization and objective criteria. In previous research, suitable conditions for applying Read Across to the prediction of the Ames mutagenicity of metabolites and degradation products of pesticides were established: a standardized similarity criterion based simultaneously on basic molecular properties and Structural Similarity was successfully applied to a number of case studies. Here the investigation is extended to a large database of curated Ames mutagenicity results. For around 2,000 chemicals for which the similarity criterion was applicable, the predictivity of Read Across was high: specificity 0.72, sensitivity 0.90, accuracy 0.85. This compares favourably with the Ames test intra-assay variability, and with the predictivity of QSAR models. The need for standardization and rigorous validation of Read Across is emphasized.

The Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency

The U.S. Environmental Protection Agency (EPA) is faced with the challenge of efficiently and credibly evaluating chemical safety often with limited or no available toxicity data. The expanding number of chemicals found in commerce and the environment, coupled with time and resource requirements for traditional toxicity testing and exposure characterization, continue to underscore the need for new approaches. In 2005, EPA charted a new course to address this challenge by embracing computational toxicology (CompTox) and investing in the technologies and capabilities to push the field forward. The return on this investment has been demonstrated through results and applications across a range of human and environmental health problems, as well as initial application to regulatory decision-making within programs such as the EPA's Endocrine Disruptor Screening Program. The CompTox initiative at EPA is more than a decade old. This manuscript presents a blueprint to guide the strategic and operational direction over the next 5 years. The primary goal is to obtain broader acceptance of the CompTox approaches for application to higher tier regulatory decisions, such as chemical assessments. To achieve this goal, the blueprint expands and refines the use of high-throughput and computational modeling approaches to transform the components in chemical risk assessment, while systematically addressing key challenges that have hindered progress. In addition, the blueprint outlines additional investments in cross-cutting efforts to characterize uncertainty and variability, develop software and information technology tools, provide outreach and training, and establish scientific confidence for application to different public health and environmental regulatory decisions.

Guidance on the use of the Threshold of Toxicological Concern approach in food safety assessment

The Scientific Committee confirms that the Threshold of Toxicological Concern (TTC) is a pragmatic screening and prioritisation tool for use in food safety assessment. This Guidance provides clear step-by-step instructions for use of the TTC approach. The inclusion and exclusion criteria are defined and the use of the TTC decision tree is explained. The approach can be used when the chemical structure of the substance is known, there are limited chemical-specific toxicity data and the exposure can be estimated. The TTC approach should not be used for substances for which EU food/feed legislation requires the submission of toxicity data or when sufficient data are available for a risk assessment or if the substance under consideration falls into one of the exclusion categories. For substances that have the potential to be DNA-reactive mutagens and/or carcinogens based on the weight of evidence, the relevant TTC value is 0.0025 μg/kg body weight (bw) per day. For organophosphates or carbamates, the relevant TTC value is 0.3 μg/kg bw per day. All other substances are grouped according to the Cramer classification. The TTC values for Cramer Classes I, II and III are 30 μg/kg bw per day, 9 μg/kg bw per day and 1.5 μg/kg bw per day, respectively. For substances with exposures below the TTC values, the probability that they would cause adverse health effects is low. If the estimated exposure to a substance is higher than the relevant TTC value, a non-TTC approach is required to reach a conclusion on potential adverse health effects.

Use of big data in drug development for precision medicine: an update

INTRODUCTION

Big-data-driven drug development resources and methodologies have been evolving with ever-expanding data from large-scale biological experiments, clinical trials, and medical records from participants in data collection initiatives. The enrichment of biological- and clinical-context-specific large-scale data has enabled computational inference more relevant to real-world biomedical research, particularly identification of therapeutic targets and drugs for specific diseases and clinical scenarios.

AREAS COVERED

Here we overview recent progresses made in the fields: new big-data-driven approach to therapeutic target discovery, candidate drug prioritization, inference of clinical toxicity, and machine-learning methods in drug discovery.

EXPERT OPINION

In the near future, much larger volumes and complex datasets for precision medicine will be generated, e.g., individual and longitudinal multi-omic, and direct-to-consumer datasets. Closer collaborations between experts with different backgrounds would also be required to better translate analytic results into prognosis and treatment in the clinical practice. Meanwhile, cloud computing with protected patient privacy would become more routine analytic practice to fill the gaps within data integration along with the advent of big-data. To conclude, integration of multitudes of data generated for each individual along with techniques tailored for big-data analytics may eventually enable us to achieve precision medicine.

Unlocking the potential of in silico chemical safety assessment - A report on a cross-sector symposium on current opportunities and future challenges

In silico chemical safety assessment can support the evaluation of hazard and risk following potential exposure to a substance. A symposium identified a number of opportunities and challenges to implement in silico methods, such as quantitative structure-activity relationships (QSARs) and read-across, to assess the potential harm of a substance in a variety of exposure scenarios, e.g. pharmaceuticals, personal care products, and industrial chemicals. To initiate the process of in silico safety assessment, clear and unambiguous problem formulation is required to provide the context for these methods. These approaches must be built on data of defined quality, while acknowledging the possibility of novel data resources tapping into on-going progress with data sharing. Models need to be developed that cover appropriate toxicity and kinetic endpoints, and that are documented appropriately with defined uncertainties. The application and implementation of in silico models in chemical safety requires a flexible technological framework that enables the integration of multiple strands of data and evidence. The findings of the symposium allowed for the identification of priorities to progress in silico chemical safety assessment towards the animal-free assessment of chemicals.

Filling Data Gaps by Read-across: A Mini Review on its Application, Developments and Challenges

Read-across is a non-testing data gap filling technique which provides information for toxicological assessments by inferring from known toxicity data of compound(s) with a "similar" property or chemical profile. The increased usage of read-across was driven by monetary, timing and ethical costs associated with in vivo testing, as well as promoted by regulatory frameworks to minimize new animal testing (e. g., EU-REACH). Several guidance documents have been published by ECHA and OECD providing guidelines on how to perform, assess and document a read-across study. In parallel, much effort was invested by the scientific community to provide good read-across practices and structured frameworks to enhance validity of read-across justifications. Nevertheless, read-across is an evolving method with several open issues and opportunities. A brief review is here provided on key developments on the use of read-across, regulatory and scientific expectations, practical hurdles and open challenges.

Best Practices for QSAR Model Reporting: Physical and Chemical Properties, Ecotoxicity, Environmental Fate, Human Health, and Toxicokinetics Endpoints

BACKGROUND

Quantitative and qualitative structure–activity relationships (QSARs) have been used to understand chemical behavior for almost a century. The main source of QSAR models is the scientific literature, but the open question is how well these models are documented.

OBJECTIVES

The main aim of this study was to critically analyze the publication practices of QSARs with regard to transparency, potential reproducibility, and independent verification. The focus was on the level of technical completeness of the published QSARs.

METHODS

A total of 1,533 QSAR articles reporting 79 individual endpoints, mostly in environmental and health science, were reviewed. The QSAR parameters required for technical completeness were grouped into five categories: chemical structures, experimental endpoint values, descriptor values, mathematical representation of the model, and predicted endpoint values. The data were summarized and discussed using Circos plots.

RESULTS

Altogether, 42.5% of the reviewed articles were found to be potentially reproducible. The potential reproducibility for different endpoint groups varied; the respective rates were 39% for physical and chemical properties, 52% for ecotoxicity, 56% for environmental fate, 30% for human health, and 32% for toxicokinetics. The reproducibility of QSARs is discussed and placed in the context of the reproducibility of the experimental methods. Included are 65 references to open QSAR datasets as examples of models restored from scientific articles.

DISCUSSION

Strikingly poor documentation of QSARs was observed, which reduces the transparency, availability, and consequently, the application of research results in scientific, industrial, and regulatory areas. A list of the components needed to ensure the best practices for QSAR reporting is provided, allowing long-term use and preservation of the models. This list also allows an assessment of the reproducibility of models by interested parties such as journal editors, reviewers, regulators, evaluators, and potential users. https://doi.org/10.1289/EHP3264.

Prediction of the Formation of Reactive Metabolites by A Novel Classifier Approach Based on Enrichment Factor Optimization (EFO) as Implemented in the VEGA Program

The study is aimed at developing linear classifiers to predict the capacity of a given substrate to yield reactive metabolites. While most of the hitherto reported predictive models are based on the occurrence of known structural alerts (e.g., the presence of toxophoric groups), the present study is focused on the generation of predictive models involving linear combinations of physicochemical and stereo-electronic descriptors. The development of these models is carried out by using a novel classification approach based on enrichment factor optimization (EFO) as implemented in the VEGA suite of programs. The study took advantage of metabolic data as collected by manually curated analysis of the primary literature and published in the years 2004⁻2009. The learning set included 977 substrates among which 138 compounds yielded reactive first-generation metabolites, plus 212 substrates generating reactive metabolites in all generations (i.e., metabolic steps). The results emphasized the possibility of developing satisfactory predictive models especially when focusing on the first-generation reactive metabolites. The extensive comparison of the classifier approach presented here using a set of well-known algorithms implemented in Weka 3.8 revealed that the proposed EFO method compares with the best available approaches and offers two relevant benefits since it involves a limited number of descriptors and provides a score-based probability thus allowing a critical evaluation of the obtained results. The last analyses on non-cheminformatics UCI datasets emphasize the general applicability of the EFO approach, which conveniently performs using both balanced and unbalanced datasets.

Extending the Generalised Read-Across approach (GenRA): A systematic analysis of the impact of physicochemical property information on read-across performance

Read-across is a useful data gap filling technique used within category and analogue approaches in regulatory hazard and risk assessment. Recently we developed an algorithmic, approach called Generalised Read-Across (GenRA) (Shah et al., 2016) which makes read-across predictions of toxicity effects using a similarity weighted average of source analogues characterised by their chemical and/or bioactivity descriptors. A default GenRA approach (termed baseline GenRA) relies on identifying 10 source analogues relative to a target substance that are structurally similar based on Morgan chemical fingerprints and computing an activity score to estimate presence or absence of in vivo toxicity. This current study investigated the impact that similarity in bioavailability plays in altering the local neighbourhood of source analogues as well as read-across performance relative to baseline GenRA using physicochemical property information as a surrogate for bioavailability. Two approaches were evaluated: 1) a filtering approach which restricted structurally related analogues based on their physicochemical properties; and 2) a search expansion approach which included additional analogues based on a combined structural and physicochemical similarity index. Filtering minimally improved performance, and was very dependent on the similarity threshold selected. The search expansion approach performed at least as well as the baseline GenRA, and showed up to a 9% improvement in read-across performance for at least 10 of the 50 organs considered. We summarise the overall impact that physicochemical information plays on GenRA performance, illustrate the improvement for a specific case study substance and describe how to select the most appropriate physicochemical similarity threshold to achieve optimal read-across performance depending on the toxicity effect and chemical of interest. The analyses show that physicochemical property information does result in a modest (up to 9% increase) improvement in structural based read-across predictions.

Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility

Earlier we created a chemical hazard database via natural language processing of dossiers submitted to the European Chemical Agency with approximately 10 000 chemicals. We identified repeat OECD guideline tests to establish reproducibility of acute oral and dermal toxicity, eye and skin irritation, mutagenicity and skin sensitization. Based on 350-700+ chemicals each, the probability that an OECD guideline animal test would output the same result in a repeat test was 78%-96% (sensitivity 50%-87%). An expanded database with more than 866 000 chemical properties/hazards was used as training data and to model health hazards and chemical properties. The constructed models automate and extend the read-across method of chemical classification. The novel models called RASARs (read-across structure activity relationship) use binary fingerprints and Jaccard distance to define chemical similarity. A large chemical similarity adjacency matrix is constructed from this similarity metric and is used to derive feature vectors for supervised learning. We show results on 9 health hazards from 2 kinds of RASARs-"Simple" and "Data Fusion". The "Simple" RASAR seeks to duplicate the traditional read-across method, predicting hazard from chemical analogs with known hazard data. The "Data Fusion" RASAR extends this concept by creating large feature vectors from all available property data rather than only the modeled hazard. Simple RASAR models tested in cross-validation achieve 70%-80% balanced accuracies with constraints on tested compounds. Cross validation of data fusion RASARs show balanced accuracies in the 80%-95% range across 9 health hazards with no constraints on tested compounds.

Utilizing Threshold of Toxicological Concern (TTC) with High Throughput Exposure Predictions (HTE) as a Risk-Based Prioritization Approach for thousands of chemicals

Regulatory agencies across the world are facing the challenge of performing risk-based prioritization of thousands of chemicals in commerce. Here, we present an approach using the Threshold of Toxicological Concern (TTC) combined with heuristic high-throughput exposure (HTE) modelling to rank order chemicals for further evaluation. Accordingly, for risk-based prioritization, chemicals with exposures > TTC would be ranked as higher priority for further evaluation whereas substances with exposures < TTC would be ranked as lower priority. An initial proof of concept, using a dataset of 7986 substances with previously modeled median and upper 95% credible interval (UCI) total daily median exposure rates showed fewer than 5% of substances had UCI exposures > the Cramer Class III TTC (1.5 μg/kg-day). We extended the analysis by profiling the same dataset through the TTC workflow published by Kroes et al (2004) which accounts for known exclusions to the TTC as well as structural alerts. UCI exposures were then compared to the appropriate class-specific TTC. None of the substances categorized as Cramer Class I or Cramer Class II exceeded their respective TTC values and no more than 2% of substances categorized as Cramer Class III or acetylcholinesterase inhibitors exceeded their respective TTC values. The modeled UCI exposures for the majority of the 1853 chemicals with genotoxicity structural alerts did exceed the TTC of 0.0025 μg/kg-day, but only 79 substances exceeded this TTC if median exposure values were used. For substances for which UCI exposures exceeded relevant TTC values, we highlight possible approaches for consideration to refine the HTE : TTC approach. Overall, coupling TTC with HTE offers promise as a pragmatic first step in ranking substances as part of a risk-based prioritization approach.

An evaluation of selected (Q)SARs/expert systems for predicting skin sensitisation potential

Predictive testing to characterise substances for their skin sensitisation potential has historically been based on animal models such as the Local Lymph Node Assay (LLNA) and the Guinea Pig Maximisation Test (GPMT). In recent years, EU regulations, have provided a strong incentive to develop non-animal alternatives, such as expert systems software. Here we selected three different types of expert systems: VEGA (statistical), Derek Nexus (knowledge-based) and TIMES-SS (hybrid), and evaluated their performance using two large sets of animal data: one set of 1249 substances from eChemportal and a second set of 515 substances from NICEATM. A model was considered successful at predicting skin sensitisation potential if it had at least the same balanced accuracy as the LLNA and the GPMT had in predicting the other outcomes, which ranged from 79% to 86%. We found that the highest balanced accuracy of any of the expert systems evaluated was 65% when making global predictions. For substances within the domain of TIMES-SS, however, balanced accuracies for the two datasets were found to be 79% and 82%. In those cases where a chemical was within the TIMES-SS domain, the TIMES-SS skin sensitisation hazard prediction had the same confidence as the result from LLNA or GPMT.

Big Data Approaches for Modeling Response and Resistance to Cancer Drugs

Despite significant progress in cancer research, current standard-of-care drugs fail to cure many types of cancers. Hence, there is an urgent need to identify better predictive biomarkers and treatment regimes. Conventionally, insights from hypothesis-driven studies are the primary force for cancer biology and therapeutic discoveries. Recently, the rapid growth of big data resources, catalyzed by breakthroughs in high-throughput technologies, has resulted in a paradigm shift in cancer therapeutic research. The combination of computational methods and genomics data has led to several successful clinical applications. In this review, we focus on recent advances in data-driven methods to model anticancer drug efficacy, and we present the challenges and opportunities for data science in cancer therapeutic research.

In silico identification of protein targets for chemical neurotoxins using ToxCast in vitro data and read-across within the QSAR toolbox

This study evaluates the application of QSAR Toolbox and ToxCast screening data to identify neurological targets for pyrethroids.

There are many mechanisms of neurotoxicity that are initiated by the interaction of chemicals with different neurological targets. Under the U.S. Environmental Protection Agency's ToxCast program, the biological activity of thousands of chemicals was screened in biochemical and cell-based assays in a high-throughput manner. Two hundred sixteen assays in the ToxCast screening database were identified as targeting a total of 123 proteins having neurological functions according to the Gene Ontology database. Data from these assays were imported into the Organization for Economic Co-operation and Development QSAR Toolbox and used to predict neurological targets for chemical neurotoxins. Two sets of data were generated: one set was used to classify compounds as active or inactive and another set, composed of AC₅₀s for only active compounds, was used to predict AC₅₀ values for unknown chemicals. Chemical grouping and read-across within the QSAR Toolbox were used to identify neurologic targets and predict interactions for pyrethroids, a class of compounds known to elicit neurotoxic effects in humans. The classification prediction results showed 79% accuracy while AC₅₀ predictions demonstrated mixed accuracy compared with the ToxCast screening data.

Nontest Methods to Predict Acute Toxicity: State of the Art for Applications of In Silico Methods

The assessment of acute toxicity of chemicals by in silico methods is actually done by two methodologies, read-across and QSAR. The two approaches are strongly based on the similarity between the chemical for which a risk assessment is required and the reference chemical(s) for which the experimental data are known. Here, we describe the two methodologies with some main publications as illustrations and the in silico data associated with acute toxicity endpoints (ECHA, REACH) accessible via eChemPortal.

Aquatic ecotoxicological models and their applicability in Arctic regions

Dose-response modeling is one of the most important steps of ecological risk assessment. It requires concentration-effects relationships for the species under consideration. There are very limited studies and experimental data available for the Arctic aquatic species. Lack of toxicity data hinders obtaining dose-response relationships for lethal (LC50 values), sub-lethal and carcinogenic effects. Gaps in toxicity data could be filled using a variety of in-silico ecotoxicological methods. This paper reviews the suitability of such methods for the Arctic scenario. Mechanistic approaches like toxicokinetic and toxicodynamic analysis are found to be better suited for interspecies extrapolation than statistical methods, such as Quantitative Structure-Activity Relationships/Quantitative Structure Activity-Activity Relationship, ICE, and other empirical models, such as Haber's law and Ostwald's equation. A novel approach is proposed where the effects of the toxicant exposure are quantified based on the probability of cellular damage and metabolites interactions. This approach recommends modeling cellular damage using a toxicodynamic model and physiology or metabolites interactions using a toxicokinetic model. Together, these models provide more reliable estimates of toxicity in the Arctic aquatic species, which will assist in conducting ecological risk assessment of Arctic environment.

What if the number of nanotoxicity data is too small for developing predictive Nano-QSAR models? An alternative read-across based approach for filling data gaps

Over the past decade, computational nanotoxicology, in particular Quantitative Structure-Activity Relationship models (Nano-QSAR) that help in assessing the biological effects of nanomaterials, have received much attention. In effect, a solid basis for uncovering the relationships between the structure and property/activity of nanoparticles has been created. Nonetheless, six years after the first pioneering computational studies focusing on the investigation of nanotoxicity were commenced, these computational methods still suffer from many limitations. These are mainly related to the paucity of widely available, systematically varied, libraries of experimental data necessary for the development and validation of such models. This results in the still-low acceptance of these methods as valuable research tools for nanosafety and raises the query as to whether these methods could gain wide acceptance of regulatory bodies as alternatives for traditional in vitro methods. This study aimed to give an answer to the following question: How to remedy the paucity of experimental nanotoxicity data and thereby, overcome key roadblock that hinders the development of approaches for data-driven modeling of nanoparticle properties and toxicities? Here, a simple and transparent read-across algorithm for a pre-screening hazard assessment of nanomaterials that provides reasonably accurate results by making the best use of existing limited set of observations will be introduced.

Combination of multiple neural crest migration assays to identify environmental toxicants from a proof-of-concept chemical library

Many in vitro tests have been developed to screen for potential neurotoxicity. However, only few cell function-based tests have been used for comparative screening, and thus experience is scarce on how to confirm and evaluate screening hits. We addressed these questions for the neural crest cell migration test (cMINC). After an initial screen, a hit follow-up strategy was devised. A library of 75 compounds plus internal controls (NTP80-list), assembled by the National Toxicology Program of the USA (NTP) was used. It contained some known classes of (developmental) neurotoxic compounds. The primary screen yielded 23 confirmed hits, which comprised ten flame retardants, seven pesticides and six drug-like compounds. Comparison of concentration-response curves for migration and viability showed that all hits were specific. The extent to which migration was inhibited was 25-90%, and two organochlorine pesticides (DDT, heptachlor) were most efficient. In the second part of this study, (1) the cMINC assay was repeated under conditions that prevent proliferation; (2) a transwell migration assay was used as a different type of migration assay; (3) cells were traced to assess cell speed. Some toxicants had largely varying effects between assays, but each hit was confirmed in at least one additional test. This comparative study allows an estimate on how confidently the primary hits from a cell function-based screen can be considered as toxicants disturbing a key neurodevelopmental process. Testing of the NTP80-list in more assays will be highly interesting to assemble a test battery and to build prediction models for developmental toxicity.

A History of the Molecular Initiating Event

The adverse outcome pathway (AOP) framework provides an alternative to traditional in vivo experiments for the risk assessment of chemicals. AOPs consist of a number of key events (KEs) linked by key event relationships across a range of biological organization backed by scientific evidence. The first KE in the pathway is the molecular initiating event (MIE)-the initial chemical trigger that starts an AOP. Over the past 3 years the AOP conceptual framework has gained a large amount of momentum in toxicology as an alternative to animal methods, and so the MIE has come into the spotlight. What is an MIE? How can MIEs be measured or predicted? What research is currently contributing to our understanding of MIEs? In this Perspective we outline answers to these key questions.

Understanding Cytotoxicity and Cytostaticity in a High-Throughput Screening Collection

While mechanisms of cytotoxicity and cytostaticity have been studied extensively from the biological side, relatively little is currently understood regarding areas of chemical space leading to cytotoxicity and cytostasis in large compound collections. Predicting and rationalizing potential adverse mechanism-of-actions (MoAs) of small molecules is however crucial for screening library design, given the link of even low level cytotoxicity and adverse events observed in man. In this study, we analyzed results from a cell-based cytotoxicity screening cascade, comprising 296 970 nontoxic, 5784 cytotoxic and cytostatic, and 2327 cytostatic-only compounds evaluated on the THP-1 cell-line. We employed an in silico MoA analysis protocol, utilizing 9.5 million active and 602 million inactive bioactivity points to generate target predictions, annotate predicted targets with pathways, and calculate enrichment metrics to highlight targets and pathways. Predictions identify known mechanisms for the top ranking targets and pathways for both phenotypes after review and indicate that while processes involved in cytotoxicity versus cytostaticity seem to overlap, differences between both phenotypes seem to exist to some extent. Cytotoxic predictions highlight many kinases, including the potentially novel cytotoxicity-related target STK32C, while cytostatic predictions outline targets linked with response to DNA damage, metabolism, and cytoskeletal machinery. Fragment analysis was also employed to generate a library of toxicophores to improve general understanding of the chemical features driving toxicity. We highlight substructures with potential kinase-dependent and kinase-independent mechanisms of toxicity. We also trained a cytotoxic classification model on proprietary and public compound readouts, and prospectively validated these on 988 novel compounds comprising difficult and trivial testing instances, to establish the applicability domain of models. The proprietary model performed with precision and recall scores of 77.9% and 83.8%, respectively. The MoA results and top ranking substructures with accompanying MoA predictions are available as a platform to assess screening collections.