Use of connectivity mapping to support read across: A deeper dive using data from 186 chemicals, 19 cell lines and 2 case studies

Systems toxicology of complex wood combustion aerosol reveals gaseous carbonyl compounds as critical constituents

Epidemiological studies identified air pollution as one of the prime causes for human morbidity and mortality, due to harmful effects mainly on the cardiovascular and respiratory systems. Damage to the lung leads to several severe diseases such as fibrosis, chronic obstructive pulmonary disease and cancer. Noxious environmental aerosols are comprised of a gas and particulate phase representing highly complex chemical mixtures composed of myriads of compounds. Although some critical pollutants, foremost particulate matter (PM), could be linked to adverse health effects, a comprehensive understanding of relevant biological mechanisms and detrimental aerosol constituents is still lacking. Here, we employed a systems toxicology approach focusing on wood combustion, an important source for air pollution, and demonstrate a key role of the gas phase, specifically carbonyls, in driving adverse effects. Transcriptional profiling and biochemical analysis of human lung cells exposed at the air-liquid-interface determined DNA damage and stress response, as well as perturbation of cellular metabolism, as major key events. Connectivity mapping revealed a high similarity of gene expression signatures induced by wood smoke and agents prompting DNA-protein crosslinks (DPCs). Indeed, various gaseous aldehydes were detected in wood smoke, which promote DPCs, initiate similar genomic responses and are responsible for DNA damage provoked by wood smoke. Hence, systems toxicology enables the discovery of critical constituents of complex mixtures i.e. aerosols and highlights the role of carbonyls on top of particulate matter as an important health hazard.

A tiered testing strategy based on in vitro phenotypic and transcriptomic data for selecting representative petroleum UVCBs for toxicity evaluation in vivo

Hazard evaluation of substances of "unknown or variable composition, complex reaction products and biological materials" (UVCBs) remains a major challenge in regulatory science because their chemical composition is difficult to ascertain. Petroleum substances are representative UVCBs and human cell-based data have been previously used to substantiate their groupings for regulatory submissions. We hypothesized that a combination of phenotypic and transcriptomic data could be integrated to make decisions as to selection of group-representative worst-case petroleum UVCBs for subsequent toxicity evaluation in vivo. We used data obtained from 141 substances from 16 manufacturing categories previously tested in 6 human cell types (induced pluripotent stem cell [iPSC]-derived hepatocytes, cardiomyocytes, neurons, and endothelial cells, and MCF7 and A375 cell lines). Benchmark doses for gene-substance combinations were calculated, and both transcriptomic and phenotype-derived points of departure (PODs) were obtained. Correlation analysis and machine learning were used to assess associations between phenotypic and transcriptional PODs and to determine the most informative cell types and assays, thus representing a cost-effective integrated testing strategy. We found that 2 cell types-iPSC-derived-hepatocytes and -cardiomyocytes-contributed the most informative and protective PODs and may be used to inform selection of representative petroleum UVCBs for further toxicity evaluation in vivo. Overall, although the use of new approach methodologies to prioritize UVCBs has not been widely adopted, our study proposes a tiered testing strategy based on iPSC-derived hepatocytes and cardiomyocytes to inform selection of representative worst-case petroleum UVCBs from each manufacturing category for further toxicity evaluation in vivo.

Quantifying Analogue Suitability for SAR-Based Read-Across Toxicological Assessment

Structure activity relationship (SAR)-based read-across often is an integral part of toxicological safety assessment, and justification of the prediction presents the most challenging aspect of the approach. It has been established that structural consideration alone is inadequate for selecting analogues and justifying their use, and biological relevance must be incorporated. Here we introduce an approach for considering biological and toxicological related features quantitatively to compute a similarity score that is concordant with suitability for a read-across prediction for systemic toxicity. Fingerprint keys for comparing metabolism, reactivity, and physical chemical properties are presented and used to compare these attributes for 14 case study chemicals each with a list of potential analogues. Within each case study, the sum of these nonstructural similarity scores is consistent with suitability for read-across established using an approach based on expert judgment. Machine learning is applied to determine the contributions from each of the similarity attributes revealing their importance for each structure class. This approach is used to quantify and communicate the differences between a target and a potential analogue as well as rank analogue quality when more than one is relevant. A numerical score with easily interpreted fingerprints increases transparency and consistency among experts, facilitates implementation by others, and ultimately increases chances for regulatory acceptance.

Structure-activity relationship read-across and transcriptomics for branched carboxylic acids

The purpose of this study was to use chemical similarity evaluations, transcriptional profiling, in vitro toxicokinetic data, and physiologically based pharmacokinetic (PBPK) models to support read-across for a series of branched carboxylic acids using valproic acid (VPA), a known developmental toxicant, as a comparator. The chemicals included 2-propylpentanoic acid (VPA), 2-ethylbutanoic acid, 2-ethylhexanoic acid (EHA), 2-methylnonanoic acid, 2-hexyldecanoic acid, 2-propylnonanoic acid (PNA), dipentyl acetic acid or 2-pentylheptanoic acid, octanoic acid (a straight chain alkyl acid), and 2-ethylhexanol. Transcriptomics was evaluated in 4 cell types (A549, HepG2, MCF7, and iCell cardiomyocytes) 6 h after exposure to 3 concentrations of the compounds, using the L1000 platform. The transcriptional profiling data indicate that 2- or 3-carbon alkyl substituents at the alpha position of the carboxylic acid (EHA and PNA) elicit a transcriptional profile similar to the one elicited by VPA. The transcriptional profile is different for the other chemicals tested, which provides support for limiting read-across from VPA to much shorter and longer acids. Molecular docking models for histone deacetylases, the putative target of VPA, provide a possible mechanistic explanation for the activity cliff elucidated by transcriptomics. In vitro toxicokinetic data were utilized in a PBPK model to estimate internal dosimetry. The PBPK modeling data show that as the branched chain increases, predicted plasma Cmax decreases. This work demonstrates how transcriptomics and other mode of action-based methods can improve read-across.

Using transcriptomics, proteomics and phosphoproteomics as new approach methodology (NAM) to define biological responses for chemical safety assessment

Omic-based technologies are of particular interest and importance for hazard identification and health risk characterization of chemicals. Their application in the new approach methodologies (NAMs) anchored on cellular toxicity pathways is based on the premise that any apical health endpoint change must be underpinned by some alterations at the omic levels. In the present study we examined the cellular responses to two chemicals, caffeine and coumarin, by generating and integrating multi-omic data from multi-dose and multi-time point transcriptomic, proteomic and phosphoproteomic experiments. We showed that the methodology presented here was able to capture the complete chain of events from the first chemical-induced changes at the phosphoproteome level, to changes in gene expression, and lastly to changes in protein abundance, each with vastly different points of departure (PODs). In HepG2 cells we found that the metabolism of lipids and general cellular stress response to be the dominant biological processes in response to caffeine and coumarin exposure, respectively. The phosphoproteomic changes were detected early in time, at very low doses and provided a fast, adaptive cellular response to chemical exposure with 7-37-fold lower points of departure comparing to the transcriptomics. Changes in protein abundance were found much less frequently than transcriptomic changes. While challenges remain, our study provides strong and novel evidence supporting the notion that these three omic technologies can be used in an integrated manner to facilitate a more complete understanding of pathway perturbations and POD determinations for risk assessment of chemical exposures.

Article title: Transcriptional profiling efficacy to define biological activity similarity for cosmetic ingredients' safety assessment based on next-generation read-across

The objective of this work was to use transcriptional profiling to assess the biological activity of structurally related chemicals to define their biological similarity and with that, substantiate the validity of a read-across approach usable in risk assessment. Two case studies are presented, one with 4 short alkyl chain parabens: methyl (MP), ethyl (EP), butyl (BP), and propylparaben (PP), as well as their main metabolite, p-hydroxybenzoic acid (pHBA) with the assumption that propylparaben was the target chemical; and a second one with caffeine and its main metabolites theophylline, theobromine and paraxanthine where CA was the target chemical. The comprehensive transcriptional response of MCF7, HepG2, A549 and ICell cardiomyocytes was evaluated (TempO-Seq) after exposure to vehicle-control, each paraben or pHBA, CA or its metabolites, at 3 non-cytotoxic concentrations, for 6 h. Differentially expressed genes (FDR ≥0.05, and fold change ±1.2≥) were identified for each chemical, at each concentration, and used to determine similarities. Each of the chemicals is able to elicit changes in the expression of a number of genes, as compared to controls. Importantly, the transcriptional profile elicited by each of the parabens shares a high degree of similarity across the group. The highest number of genes commonly affected was between butylparaben and PP. The transcriptional profile of the parabens is similar to the one elicited by estrogen receptor agonists, with BP being the closest structural and biological analogue for PP. In the CA case, the transcriptional profile elicited of all four methylxanthines had a high degree of similarity across the cell types, with CA and theophylline being the most active. The most robust response was obtained in the cardiomyocytes with the highest transcriptional profile similarity between CA and TP. The transcriptional profile of the methylxanthines is similar to the one elicited by inhibitors of phosphatidylinositol 3-kinase as well as other kinase inhibitors. Overall, our results support the approach of incorporating transcriptional profiling in well-designed in vitro tests as one robust stream of data to support biological similarity driven read-across procedures and strengthening the traditional structure-based approaches useful in risk assessment.

Prioritization of chemicals in food for risk assessment by integrating exposure estimates and new approach methodologies: A next generation risk assessment case study

Next generation risk assessment is defined as a knowledge-driven system that allows for cost-efficient assessment of human health risk related to chemical exposure, without animal experimentation. One of the key features of next generation risk assessment is to facilitate prioritization of chemical substances that need a more extensive toxicological evaluation, in order to address the need to assess an increasing number of substances. In this case study focusing on chemicals in food, we explored how exposure data combined with the Threshold of Toxicological Concern (TTC) concept could be used to prioritize chemicals, both for existing substances and new substances entering the market. Using a database of existing chemicals relevant for dietary exposure we calculated exposure estimates, followed by application of the TTC concept to identify substances of higher concern. Subsequently, a selected set of these priority substances was screened for toxicological potential using high-throughput screening (HTS) approaches. Remarkably, this approach resulted in alerts for a selection of substances that are already on the market and represent relevant exposure in consumers. Taken together, the case study provides proof-of-principle for the approach taken to identify substances of concern, and this approach can therefore be considered a supportive element to a next generation risk assessment strategy.

Grouping of UVCB substances with dose-response transcriptomics data from human cell-based assays

The application of in vitro biological assays as new approach methodologies (NAMs) to support grouping of UVCB (unknown or variable composition, complex reaction products, and biological materials) substances has recently been demonstrated. In addition to cell-based phenotyping as NAMs, in vitro transcriptomic profiling is used to gain deeper mechanistic understanding of biological responses to chemicals and to support grouping and read-across. However, the value of gene expression profiling for characterizing complex substances like UVCBs has not been explored. Using 141 petroleum substance extracts, we performed dose-response transcriptomic profiling in human induced pluripotent stem cell (iPSC)-derived hepatocytes, cardiomyocytes, neurons, and endothelial cells, as well as cell lines MCF7 and A375. The goal was to determine whether transcriptomic data can be used to group these UVCBs and to further characterize the molecular basis for in vitro biological responses. We found distinct transcriptional responses for petroleum substances by manufacturing class. Pathway enrichment informed interpretation of effects of substances and UVCB petroleum-class. Transcriptional activity was strongly correlated with concentration of polycyclic aromatic compounds (PAC), especially in iPSC-derived hepatocytes. Supervised analysis using transcriptomics, alone or in combination with bioactivity data collected on these same substances/cells, suggest that transcriptomics data provide useful mechanistic information, but only modest additional value for grouping. Overall, these results further demonstrate the value of NAMs for grouping of UVCBs, identify informative cell lines, and provide data that could be used for justifying selection of substances for further testing that may be required for registration.

Mapping Mechanistic Pathways of Acute Oral Systemic Toxicity Using Chemical Structure and Bioactivity Measurements

Regulatory agencies around the world have committed to reducing or eliminating animal testing for establishing chemical safety. Adverse outcome pathways can facilitate replacement by providing a mechanistic framework for identifying the appropriate non-animal methods and connecting them to apical adverse outcomes. This study separated 11,992 chemicals with curated rat oral acute toxicity information into clusters of structurally similar compounds. Each cluster was then assigned one or more ToxCast/Tox21 assays by looking for the minimum number of assays required to record at least one positive hit call below cytotoxicity for all acutely toxic chemicals in the cluster. When structural information is used to select assays for testing, none of the chemicals required more than four assays and 98% required two assays or less. Both the structure-based clusters and activity from the associated assays were significantly associated with the GHS toxicity classification of the chemicals, which suggests that a combination of bioactivity and structural information could be as reproducible as traditional in vivo studies. Predictivity is improved when the in vitro assay directly corresponds to the mechanism of toxicity, but many indirect assays showed promise as well. Given the lower cost of in vitro testing, a small assay battery including both general cytotoxicity assays and two or more orthogonal assays targeting the toxicological mechanism could be used to improve performance further. This approach illustrates the promise of combining existing in silico approaches, such as the Collaborative Acute Toxicity Modeling Suite (CATMoS), with structure-based bioactivity information as part of an efficient tiered testing strategy that can reduce or eliminate animal testing for acute oral toxicity.

A 10-step framework for use of read-across (RAX) in next generation risk assessment (NGRA) for cosmetics safety assessment

This paper presents a 10-step read-across (RAX) framework for use in cases where a threshold of toxicological concern (TTC) approach to cosmetics safety assessment is not possible. RAX builds on established approaches that have existed for more than two decades using chemical properties and in silico toxicology predictions, by further substantiating hypotheses on toxicological similarity of substances, and integrating new approach methodologies (NAM) in the biological and kinetic domains. NAM include new types of data on biological observations from, for example, in vitro assays, toxicogenomics, metabolomics, receptor binding screens and uses physiologically-based kinetic (PBK) modelling to inform about systemic exposure. NAM data can help to substantiate a mode/mechanism of action (MoA), and if similar chemicals can be shown to work by a similar MoA, a next generation risk assessment (NGRA) may be performed with acceptable confidence for a data-poor target substance with no or inadequate safety data, based on RAX approaches using data-rich analogue(s), and taking account of potency or kinetic/dynamic differences.

A toxicogenomic data space for system-level understanding and prediction of EDC-induced toxicity

Endocrine disrupting compounds (EDCs) are a persistent threat to humans and wildlife due to their ability to interfere with endocrine signaling pathways. Inspired by previous work to improve chemical hazard identification through the use of toxicogenomics data, we developed a genomic-oriented data space for profiling the molecular activity of EDCs in an in silico manner, and for creating predictive models that identify and prioritize EDCs. Predictive models of EDCs, derived from gene expression data from rats (in vivo and in vitro primary hepatocytes) and humans (in vitro primary hepatocytes and HepG2), achieve testing accuracy greater than 90%. Negative test sets indicate that known safer chemicals are not predicted as EDCs. The rat in vivo-based classifiers achieve accuracy greater than 75% when tested for invitro to in vivoextrapolation. This study reveals key metabolic pathways and genes affected by EDCs together with a set of predictive models that utilize these pathways to prioritize EDCs in dose/time dependent manner and to predict EDCevokedmetabolic diseases.

Paving the way for application of next generation risk assessment to safety decision-making for cosmetic ingredients

Next generation risk assessment (NGRA) is an exposure-led, hypothesis-driven approach that has the potential to support animal-free safety decision-making. However, significant effort is needed to develop and test the in vitro and in silico (computational) approaches that underpin NGRA to enable confident application in a regulatory context. A workshop was held in Montreal in 2019 to discuss where effort needs to be focussed and to agree on the steps needed to ensure safety decisions made on cosmetic ingredients are robust and protective. Workshop participants explored whether NGRA for cosmetic ingredients can be protective of human health, and reviewed examples of NGRA for cosmetic ingredients. From the limited examples available, it is clear that NGRA is still in its infancy, and further case studies are needed to determine whether safety decisions are sufficiently protective and not overly conservative. Seven areas were identified to help progress application of NGRA, including further investments in case studies that elaborate on scenarios frequently encountered by industry and regulators, including those where a ‘high risk’ conclusion would be expected. These will provide confidence that the tools and approaches can reliably discern differing levels of risk. Furthermore, frameworks to guide performance and reporting should be developed.

An overview of current practices for regulatory risk assessment with lessons learnt from cosmetics in the European Union

Risk assessments of various types of chemical compounds are carried out in the European Union (EU) foremost to comply with legislation and to support regulatory decision-making with respect to their safety. Historically, risk assessment has relied heavily on animal experiments. However, the EU is committed to reduce animal experimentation and has implemented several legislative changes, which have triggered a paradigm shift towards human-relevant animal-free testing in the field of toxicology, in particular for risk assessment. For some specific endpoints, such as skin corrosion and irritation, validated alternatives are available whilst for other endpoints, including repeated dose systemic toxicity, the use of animal data is still central to meet the information requirements stipulated in the different legislations. The present review aims to provide an overview of established and more recently introduced methods for hazard assessment and risk characterisation for human health, in particular in the context of the EU Cosmetics Regulation (EC No 1223/2009) as well as the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) Regulation (EC 1907/2006).

Progress towards an OECD reporting framework for transcriptomics and metabolomics in regulatory toxicology

Omics methodologies are widely used in toxicological research to understand modes and mechanisms of toxicity. Increasingly, these methodologies are being applied to questions of regulatory interest such as molecular point-of-departure derivation and chemical grouping/read-across. Despite its value, widespread regulatory acceptance of omics data has not yet occurred. Barriers to the routine application of omics data in regulatory decision making have been: 1) lack of transparency for data processing methods used to convert raw data into an interpretable list of observations; and 2) lack of standardization in reporting to ensure that omics data, associated metadata and the methodologies used to generate results are available for review by stakeholders, including regulators. Thus, in 2017, the Organisation for Economic Co-operation and Development (OECD) Extended Advisory Group on Molecular Screening and Toxicogenomics (EAGMST) launched a project to develop guidance for the reporting of omics data aimed at fostering further regulatory use. Here, we report on the ongoing development of the first formal reporting framework describing the processing and analysis of both transcriptomic and metabolomic data for regulatory toxicology. We introduce the modular structure, content, harmonization and strategy for trialling this reporting framework prior to its publication by the OECD.

A comparison of classical and 21st century genotoxicity tools: A proof of concept study of 18 chemicals comparing in vitro micronucleus, ToxTracker and genomics-based methods (TGx-DDI, whole genome clustering and connectivity mapping)

A key step in the risk assessment process of a substance is the assessment of its genotoxic potential. Irrespective of the industry involved, current approaches rely on combinations of two or three in vitro tests and while highly sensitive, their specificity is thought to be limited. A refined in vitro genotoxicity testing strategy with improved predictive capacity would be beneficial and "3R" friendly as it helps to avoid unnecessary in vivo follow-up testing. Here, we describe a proof of concept study evaluating a balanced set of compounds that have in vivo negative or positive outcomes, but variable in vitro data, to determine if we could differentiate between direct and indirect acting genotoxicants. Compounds were examined in TK6 cells using an approach in which the same sample was used to evaluate both early genomic markers (Affymetrix analysis 4 hr post treatment), and the genotoxic outcome (micronuclei [MN] after 24 hr). The resulting genomic data was then analyzed using the TGx-DDI biomarker, Connectivity mapping and whole genome clustering. Chemicals were also tested in the ToxTracker assay, which uses six different biomarker genes. None of the methods correctly differentiated all direct from indirect acting genotoxicants when used alone, however, the ToxTracker assay, TGx-DDI biomarker and whole genome approaches provided high predictive capacity when used in combination with the MN assay (1/18, 2/18, 1/18 missed calls). Ultimately, a "fit for purpose" combination will depend on the specific tools available to the end user, as well as considerations of the unique benefits of the individual assays.

Drivers of and Obstacles to the Adoption of Toxicogenomics for Chemical Risk Assessment: Insights from Social Science Perspectives

BACKGROUND

Some 20 y ago, scientific and regulatory communities identified the potential of omics sciences (genomics, transcriptomics, proteomics, metabolomics) to improve chemical risk assessment through development of toxicogenomics. Recognizing that regulators adopt new scientific methods cautiously given accountability to diverse stakeholders, the scope and pace of adoption of toxicogenomics tools and data have nonetheless not met the ambitious, early expectations of omics proponents.

OBJECTIVE

Our objective was, therefore, to inventory, investigate, and derive insights into drivers of and obstacles to adoption of toxicogenomics in chemical risk assessment. By invoking established social science frameworks conceptualizing innovation adoption, we also aimed to develop recommendations for proponents of toxicogenomics and other new approach methodologies (NAMs).

METHODS

We report findings from an analysis of 56 scientific and regulatory publications from 1998 through 2017 that address the adoption of toxicogenomics for chemical risk assessment. From this purposeful sample of toxicogenomics discourse, we identified major categories of drivers of and obstacles to adoption of toxicogenomics tools and data sets. We then mapped these categories onto social science frameworks for conceptualizing innovation adoption to generate actionable insights for proponents of toxicogenomics.

DISCUSSION

We identify the most salient drivers and obstacles. From 1998 through 2017, adoption of toxicogenomics was understood to be helped by drivers such as those we labeled Superior scientific understanding, New applications, and Reduced cost & increased efficiency but hindered by obstacles such as those we labeled Insufficient validation, Complexity of interpretation, and Lack of standardization. Leveraging social science frameworks, we find that arguments for adoption that draw on the most salient drivers, which emphasize superior and novel functionality of omics as rationales, overlook potential adopters' key concerns: simplicity of use and compatibility with existing practices. We also identify two perspectives-innovation-centric and adopter-centric-on omics adoption and explain how overreliance on the former may be undermining efforts to promote toxicogenomics. https://doi.org/10.1289/EHP6500.

Comparison of Approaches for Determining Bioactivity Hits from High-Dimensional Profiling Data

Phenotypic profiling assays are untargeted screening assays that measure a large number (hundreds to thousands) of cellular features in response to a stimulus and often yield diverse and unanticipated profiles of phenotypic effects, leading to challenges in distinguishing active from inactive treatments. Here, we compare a variety of different strategies for hit identification in imaging-based phenotypic profiling assays using a previously published Cell Painting data set. Hit identification strategies based on multiconcentration analysis involve curve fitting at several levels of data aggregation (e.g., individual feature level, aggregation of similarly derived features into categories, and global modeling of all features) and on computed metrics (e.g., Euclidean and Mahalanobis distance metrics and eigenfeatures). Hit identification strategies based on single-concentration analysis included measurement of signal strength (e.g., total effect magnitude) and correlation of profiles among biological replicates. Modeling parameters for each approach were optimized to retain the ability to detect a reference chemical with subtle phenotypic effects while limiting the false-positive rate to 10%. The percentage of test chemicals identified as hits was highest for feature-level and category-based approaches, followed by global fitting, whereas signal strength and profile correlation approaches detected the fewest number of active hits at the fixed false-positive rate. Approaches involving fitting of distance metrics had the lowest likelihood for identifying high-potency false-positive hits that may be associated with assay noise. Most of the methods achieved a 100% hit rate for the reference chemical and high concordance for 82% of test chemicals, indicating that hit calls are robust across different analysis approaches.

Incorporation of in vitro techniques for botanicals dietary supplement safety assessment - Towards evaluation of developmental and reproductive toxicity (DART)

As complex mixtures, botanicals present unique challenges when assessing safe use, particularly when endpoint gaps exist that cannot be fully resolved by existing toxicological literature. Here we explore in vitro gene expression as well receptor binding and enzyme activity as alternative assays to inform on developmental and reproductive toxicity (DART) relevant modes of action, since DART data gaps are common for botanicals. Specifically, botanicals suspected to have DART effects, in addition to those with a significant history of use, were tested in these assays. Gene expression changes in a number of different cell types were analysed using the connectivity mapping approach (CMap) to identify modes of action through a functional read across approach. Taken together with ligand affinity data obtained using a set of molecular targets customised towards known DART relevant modes of action, it was possible to inform DART risk using functional analogues, potency comparisons and a margin of internal exposure approach.

Rapid hazard characterization of environmental chemicals using a compendium of human cell lines from different organs

The lack of adequate toxicity data for the vast majority of chemicals in the environment has spurred the development of new approach methodologies (NAMs). This study aimed to develop a practical high-throughput in vitro model for rapidly evaluating potential hazards of chemicals using a small number of human cells. Forty-two compounds were tested using human induced pluripotent stem cell (iPSC)-derived cells (hepatocytes, neurons, cardiomyocytes and endothelial cells), and a primary endothelial cell line. Both functional and cytotoxicity endpoints were evaluated using high-content imaging. Concentration-response was used to derive points-of-departure (POD). PODs were integrated with ToxPi and used as surrogate NAM-based PODs for risk characterization. We found chemical class-specific similarity among the chemicals tested; metal salts exhibited the highest overall bioactivity. We also observed cell type-specific patterns among classes of chemicals, indicating the ability of the proposed in vitro model to recognize effects on different cell types. Compared to available NAM datasets, such as ToxCast/Tox21 and chemical structure-based descriptors, we found that the data from the five-cell-type model was as good or even better in assigning compounds to chemical classes. Additionally, the PODs from this model performed well as a conservative surrogate for regulatory in vivo PODs and were less likely to underestimate in vivo potency and potential risk compared to other NAM-based PODs. In summary, we demonstrate the potential of this in vitro screening model to inform rapid risk-based decision-making through ranking, clustering, and assessment of both hazard and risks of diverse environmental chemicals.

Clustering a Chemical Inventory for Safety Assessment of Fragrance Ingredients: Identifying Read-Across Analogs to Address Data Gaps

A valuable approach to chemical safety assessment is the use of read-across chemicals to provide safety data to support the assessment of structurally similar chemicals. An inventory of over 6000 discrete organic chemicals used as fragrance materials in consumer products has been clustered into chemical class-based groups for efficient search of read-across sources. We developed a robust, tiered system for chemical classification based on (1) organic functional group, (2) structural similarity and reactivity features of the hydrocarbon skeletons, (3) predicted or experimentally verified Phase I and Phase II metabolism, and (4) expert pruning to consider these variables in the context of specific toxicity end points. The systematic combination of these data yielded clusters, which may be visualized as a top-down hierarchical clustering tree. In this tree, chemical classes are formed at the highest level according to organic functional groups. Each subsequent subcluster stemming from classes in this hierarchy of the cluster is a chemical cluster defined by common organic functional groups and close similarity in the hydrocarbon skeleton. By examining the available experimental data for a toxicological endpoint within each cluster, users can better identify potential read-across chemicals to support safety assessments.

In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways

In silico toxicology is an emerging field. It gains increasing importance as research is aiming to decrease the use of animal experiments as suggested in the 3R principles by Russell and Burch. In silico toxicology is a means to identify hazards of compounds before synthesis, and thus in very early stages of drug development. For chemical industries, as well as regulatory agencies it can aid in gap‐filling and guide risk minimization strategies. Techniques such as structural alerts, read‐across, quantitative structure–activity relationship, machine learning, and deep learning allow to use in silico toxicology in many cases, some even when data is scarce. Especially the concept of adverse outcome pathways puts all techniques into a broader context and can elucidate predictions by mechanistic insights.

This article is categorized under:

Structure and Mechanism > Computational Biochemistry and Biophysics

Data Science > Chemoinformatics

New Tricks for Old Guys: Recent Developments in the Chemistry, Biochemistry, Applications and Exploitation of Selected Species from the Lamiaceae Family

Lamiaceae is one of the largest families of flowering plants comprising about 250 genera and over 7,000 species. Most of the plants of this family are aromatic and therefore important source of essential oils. Lamiaceae are widely used as culinary herbs and reported as medicinal plants in several folk traditions. In the Mediterranean area oregano, sage, rosemary, thyme and lavender stand out for geographical diffusion and variety of uses. The aim of this review is to provide recent data dealing with the phytochemical and pharmacological studies, and the more recent applications of the essential oils and the non-volatile phytocomplexes. This literature survey suggests how the deeper understanding of biomolecular processes in the health and food sectors as per as pest control bioremediation of cultural heritage, or interaction with human microbiome, fields, leads to the rediscovery and new potential applications of well-known plants.