Expanding Predictive Capacities in Toxicology: Insights from Hackathon-Enhanced Data and Model Aggregation

In the realm of predictive toxicology for small molecules, the applicability domain of QSAR models is often limited by the coverage of the chemical space in the training set. Consequently, classical models fail to provide reliable predictions for wide classes of molecules. However, the emergence of innovative data collection methods such as intensive hackathons have promise to quickly expand the available chemical space for model construction. Combined with algorithmic refinement methods, these tools can address the challenges of toxicity prediction, enhancing both the robustness and applicability of the corresponding models. This study aimed to investigate the roles of gradient boosting and strategic data aggregation in enhancing the predictivity ability of models for the toxicity of small organic molecules. We focused on evaluating the impact of incorporating fragment features and expanding the chemical space, facilitated by a comprehensive dataset procured in an open hackathon. We used gradient boosting techniques, accounting for critical features such as the structural fragments or functional groups often associated with manifestations of toxicity.

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

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

High-Throughput Screening to Advance In Vitro Toxicology: Accomplishments, Challenges, and Future Directions

Traditionally, chemical toxicity is determined by in vivo animal studies, which are low throughput, expensive, and sometimes fail to predict compound toxicity in humans. Due to the increasing number of chemicals in use and the high rate of drug candidate failure due to toxicity, it is imperative to develop in vitro, high-throughput screening methods to determine toxicity. The Tox21 program, a unique research consortium of federal public health agencies, was established to address and identify toxicity concerns in a high-throughput, concentration-responsive manner using a battery of in vitro assays. In this article, we review the advancements in high-throughput robotic screening methodology and informatics processes to enable the generation of toxicological data, and their impact on the field; further, we discuss the future of assessing environmental toxicity utilizing efficient and scalable methods that better represent the corresponding biological and toxicodynamic processes in humans.

The probable future of toxicology - probabilistic risk assessment

Both because of the shortcomings of existing risk assessment methodologies, as well as newly available tools to predict hazard and risk with machine learning approaches, there has been an emerging emphasis on probabilistic risk assessment. Increasingly sophisticated AI models can be applied to a plethora of exposure and hazard data to obtain not only predictions for particular endpoints but also to estimate the uncertainty of the risk assessment outcome. This provides the basis for a shift from deterministic to more probabilistic approaches but comes at the cost of an increased complexity of the process as it requires more resources and human expertise. There are still challenges to overcome before a probabilistic paradigm is fully embraced by regulators. Based on an earlier white paper (Maertens et al., 2022), a workshop discussed the prospects, challenges and path forward for implementing such AI-based probabilistic hazard assessment. Moving forward, we will see the transition from categorized into probabilistic and dose-dependent hazard outcomes, the application of internal thresholds of toxicological concern for data-poor substances, the acknowledgement of user-friendly open-source software, a rise in the expertise of toxicologists required to understand and interpret artificial intelligence models, and the honest communication of uncertainty in risk assessment to the public.

Advancing Computational Toxicology by Interpretable Machine Learning

Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals have a critical impact on human health. Traditional animal models to evaluate chemical toxicity are expensive, time-consuming, and often fail to detect toxicants in humans. Computational toxicology is a promising alternative approach that utilizes machine learning (ML) and deep learning (DL) techniques to predict the toxicity potentials of chemicals. Although the applications of ML- and DL-based computational models in chemical toxicity predictions are attractive, many toxicity models are "black boxes" in nature and difficult to interpret by toxicologists, which hampers the chemical risk assessments using these models. The recent progress of interpretable ML (IML) in the computer science field meets this urgent need to unveil the underlying toxicity mechanisms and elucidate the domain knowledge of toxicity models. In this review, we focused on the applications of IML in computational toxicology, including toxicity feature data, model interpretation methods, use of knowledge base frameworks in IML development, and recent applications. The challenges and future directions of IML modeling in toxicology are also discussed. We hope this review can encourage efforts in developing interpretable models with new IML algorithms that can assist new chemical assessments by illustrating toxicity mechanisms in humans.

Current state-of-the-art approaches for mass spectrometry in clinical toxicology: an overview

INTRODUCTION

Hyphenated mass spectrometry (MS) has evolved into a very powerful analytical technique of high sensitivity and specificity. It is used to analyze a very wide spectrum of analytes in classical and alternative matrices. The presented paper will provide an overview of the current state-of-the-art of hyphenated MS applications in clinical toxicology primarily based on review articles indexed in PubMed (1990 to April 2023).

AREAS COVERED

A general overview of matrices, sample preparation, analytical systems, detection modes, and validation and quality control is given. Moreover, selected applications are discussed.

EXPERT OPINION

A more widespread use of hyphenated MS techniques, especially in systematic toxicological analysis and drugs of abuse testing, would help overcome limitations of immunoassay-based screening strategies. This is currently hampered by high instrument cost, qualification requirements for personnel, and less favorable turnaround times, which could be overcome by more user-friendly, ideally fully automated MS instruments. This would help making hyphenated MS-based analysis available in more laboratories and expanding analysis to a large number of organic drugs, poisons, and/or metabolites. Even the most recent novel psychoactive substances (NPS) could be presumptively identified by high-resolution MS methods, their likely presence be communicated to treating physicians, and be confirmed later on.

Advances in Single-Cell Toxicogenomics in Environmental Toxicology

The toxicity evaluation system of environmental pollutants has undergone numerous changes due to the application of new technologies. Single-cell toxicogenomics is rapidly changing our view on environmental toxicology by increasing the resolution of our analysis to the level of a single cell. Applications of this technology in environmental toxicology have begun to emerge and are rapidly expanding the portfolio of existing technologies and applications. Here, we first summarized different methods involved in single-cell isolation and amplification in single-cell sequencing process, compared the advantages and disadvantages of different methods, and analyzed their development trends. Then, we reviewed the main advances of single-cell toxicogenomics in environmental toxicology, emphatically analyzed the application prospects of this technology in identifying the target cells of pollutants in early embryos, clarifying the heterogeneous response of cell subtypes to pollutants, and finding pathogenic bacteria in unknown microbes, and highlighted the unique characteristics of this approach with high resolution, high throughput, and high specificity by examples. We also offered a prediction of the further application of this technology and the revolution it brings in environmental toxicology. Overall, these advances will provide practical solutions for controlling or mitigating exogenous toxicological effects that threaten human and ecosystem health, contribute to improving our understanding of the physiological processes affected by pollutants, and lead to the emergence of new methods of pollution control.

Scientific and Regulatory Policy Committee Points to Consider: Primary Digital Histopathology Evaluation and Peer Review for Good Laboratory Practice (GLP) Nonclinical Toxicology Studies

The Society of Toxicologic Pathology's Scientific and Regulatory Policy Committee formed a working group to consider the present and future use of digital pathology in toxicologic pathology in general and specifically its use in primary evaluation and peer review in Good Laboratory Practice (GLP) environments. Digital histopathology systems can save costs by reducing travel, enhancing organizational flexibility, decreasing slide handling, improving collaboration, increasing access to historical images, and improving quality and efficiency through integration with laboratory information management systems. However, the resources to implement and operate a digital pathology system can be significant. Given the magnitude and risks involved in the decision to adopt digital histopathology, this working group used pertinent previously published survey results and its members' expertise to create a Points-to-Consider article to assist organizations with building and implementing digital pathology workflows. With the aim of providing a comprehensive perspective, the current publication summarizes aspects of digital whole-slide imaging relevant to nonclinical histopathology evaluations, and then presents points to consider applicable to both primary digital histopathology evaluation and digital peer review in GLP toxicology studies. The Supplemental Appendices provide additional tabulated resources.

Read-across approaches: current applications and regulatory acceptance in Korea, Japan, and China

The aim of this paper was to investigate the current status of read-across approaches in the Republic of Korea, Japan, and China in terms of applications and regulatory acceptance. In the Republic of Korea, over the last 6 years, approximately 8% of safety data records used for chemical registrations were based upon read-across, and a guideline published on the use of read-across results in 2017. In Japan, read-across is generally accepted for screening hazard classification of toxicological endpoints according to the Chemical Substances Control Law (CSCL). In China, read-across data, along with data from other animal alternatives are accepted as a data source for chemical registrations, but could be only considered when testing is not technically feasible. At present, read-across is not widely used for chemical registrations and regulatory acceptance of read-across may differ among countries in Asia. With consideration of the advantages and limitations of read-across, it is expected that read-across may soon gradually be employed in Asian countries. Thus, regulatory agencies need to prepare for this progression.

A model-based approach to designing developmental toxicology experiments using sea urchin embryos

The key aim of this paper is to suggest a more quantitative approach to designing a dose–response experiment, and more specifically, a concentration–response experiment. The work proposes a departure from the traditional experimental design to determine a dose–response relationship in a developmental toxicology study. It is proposed that a model-based approach to determine a dose–response relationship can provide the most accurate statistical inference for the underlying parameters of interest, which may be estimating one or more model parameters or pre-specified functions of the model parameters, such as lethal dose, at maximal efficiency. When the design criterion or criteria can be determined at the onset, there are demonstrated efficiency gains using a more carefully selected model-based optimal design as opposed to an ad-hoc empirical design. As an illustration, a model-based approach was theoretically used to construct efficient designs for inference in a developmental toxicity study of sea urchin embryos exposed to trimethoprim. This study compares and contrasts the results obtained using model-based optimal designs versus an ad-hoc empirical design.

3D bioprinting of complex tissues in vitro: state-of-the-art and future perspectives

The pharmacology and toxicology of a broad variety of therapies and chemicals have significantly improved with the aid of the increasing in vitro models of complex human tissues. Offering versatile and precise control over the cell population, extracellular matrix (ECM) deposition, dynamic microenvironment, and sophisticated microarchitecture, which is desired for the in vitro modeling of complex tissues, 3D bio-printing is a rapidly growing technology to be employed in the field. In this review, we will discuss the recent advancement of printing techniques and bio-ink sources, which have been spurred on by the increasing demand for modeling tactics and have facilitated the development of the refined tissue models as well as the modeling strategies, followed by a state-of-the-art update on the specialized work on cancer, heart, muscle and liver. In the end, the toxicological modeling strategies, substantial challenges, and future perspectives for 3D printed tissue models were explored.

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.

Emerging Bioinformatics Methods and Resources in Drug Toxicology

Assessing the drug safety at an early stage of a drug discovery program is a critical issue. With the recent advances in molecular biology and genomic, massive amounts of generated and accumulated data by advanced experimental technologies such as RNA sequencing or proteomics start to be at the disposal of the scientific community. Innovative and adequate bioinformatic methods, tools, and protocols are required to analyze properly these diverse and extensive data sources with the aim to identify key features that are related to toxicity observations. Furthermore, the assessment of drug safety can be performed across multiple scales of complexity from molecular, cellular to phenotypic levels; therefore, the application of network science contributes to a better interpretation of the drug's exposure effect on human health. Here, we review databases containing toxicogenomics and chemical-phenotype information, as well as appropriated bioinformatics approaches that are currently used to analyze such data. Extension to others methods such as dose-responses, time-dependent processes, and text mining is also presented giving an overview of suitable tools available for a best practice of drug safety analysis.

Implementation of In Silico Toxicology Protocols in Leadscope

In silico toxicology protocols help ensure the results from computational toxicology models are performed and documented in a standardized, consistent, transparent, and accepted manner. In silico toxicology protocols for skin sensitization and genetic toxicology have been previously published and have been implemented within the Leadscope software. The following chapter outlines how such protocols have been deployed in the Leadscope platform including integration with toxicology databases and a battery of computational toxicology models.

MultiCASE Platform for In Silico Toxicology

Predictive and computational toxicology, a highly scientific and research-based field, is rapidly progressing with wider acceptance by regulatory agencies around the world. Almost every aspect of the field has seen fundamental changes during the last decade due to the availability of more data, usage, and acceptance of a variety of predictive tools and an increase in the overall awareness. Also, the influence from the recent explosive developments in the field of artificial intelligence has been significant. However, the need for sophisticated, easy to use and well-maintained software platforms for in silico toxicological assessments remains very high. The MultiCASE suite of software is one such platform that consists of an integrated collection of software programs, tools, and databases. While providing easy-to-use and highly useful tools that are relevant at present, it has always remained at the forefront of research and development by inventing new technologies and discovering new insights in the area of QSAR, artificial intelligence, and machine learning. This chapter gives the background, an overview of the software and databases involved, and a brief description of the usage methodology with the aid of examples.

Perspectives of future lung toxicology studies using human pluripotent stem cells

The absence of in vitro platforms for human pulmonary toxicology studies is becoming an increasingly serious concern. The respiratory system has a dynamic mechanical structure that extends from the airways to the alveolar region. In addition, the epithelial, endothelial, stromal, and immune cells are highly organized in each region and interact with each other to function synergistically. These cells of varied lineage, particularly epithelial cells, have been difficult to use for long-term culture in vitro, thus limiting the development of useful experimental tools. This limitation has set a large distance between the bench and the bedside for analyzing the pathogenic mechanisms, the efficacy of candidate therapeutic agents, and the toxicity of compounds. Several researchers have proposed solutions to these problems by reporting on methods for generating human lung epithelial cells derived from pluripotent stem cells (PSCs). Moreover, the use of organoid culture, organ-on-a-chip, and material-based techniques have enabled the maintenance of functional PSC-derived lung epithelial cells as well as primary cells. The aforementioned technological advances have facilitated the in vitro recapitulation of genetic lung diseases and the detection of ameliorating or worsening effects of genetic and chemical interventions, thus indicating the future possibility of more sophisticated preclinical compound assessments in vitro. In this review, we will update the recent advances in lung cell culture methods, principally focusing on human PSC-derived lung epithelial organoid culture systems with the hope of their future application in toxicology studies.

Validation of a New Sensitive Method for the Detection and Quantification of R and S-Epimers of Ergot Alkaloids in Canadian Spring Wheat Utilizing Deuterated Lysergic Acid Diethylamide as an Internal Standard

Ergot sclerotia effect cereal crops intended for consumption. Ergot alkaloids within ergot sclerotia are assessed to ensure contamination is below safety standards established for human and animal health. Ergot alkaloids exist in two configurations, the R and S-epimers. It is important to quantify both configurations. The objective of this study was to validate a new ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) method for quantification of six R and six S-epimers of ergot alkaloids in hard red spring wheat utilizing deuterated lysergic acid diethylamide (LSD-D3) as an internal standard. Validation parameters such as linearity, limit of detection (LOD), limit of quantification (LOQ), matrix effects, recovery and precision were investigated. For the 12 epimers analyzed, low LOD and LOQ values were observed, allowing for the sensitive detection of ergot epimers. Matrix effects ranged between 101-113% in a representative wheat matrix. Recovery was 68.3-119.1% with an inter-day precision of <24% relative standard deviation (RSD). The validation parameters conform with previous studies and exhibit differences between the R and S-epimers which has been rarely documented. This new sensitive method allows for the use of a new internal standard and can be incorporated and applied to research or diagnostic laboratories.

Reconceptualization of Hormetic Responses in the Frame of Redox Toxicology

Cellular adaptive mechanisms emerging after exposure to low levels of toxic agents or stressful stimuli comprise an important biological feature that has gained considerable scientific interest. Investigations of low-dose exposures to diverse chemical compounds signify the non-linear mode of action in the exposed cell or organism at such dose levels in contrast to the classic detrimental effects induced at higher ones, a phenomenon usually referred to as hormesis. The resulting phenotype is a beneficial effect that tests our physiology within the limits of our homeostatic adaptations. Therefore, doses below the region of adverse responses are of particular interest and are specified as the hormetic gain zone. The manifestation of redox adaptations aiming to prevent from disturbances of redox homeostasis represent an area of particular interest in hormetic responses, observed after exposure not only to stressors but also to compounds of natural origin, such as phytochemicals. Findings from previous studies on several agents demonstrate the heterogeneity of the specific zone in terms of the molecular events occurring. Major factors deeply involved in these biphasic phenomena are the bioactive compound per se, the dose level, the duration of exposure, the cell, tissue or even organ exposed to and, of course, the biomarker examined. In the end, the molecular fate is a complex toxicological event, based on beneficial and detrimental effects, which, however, are poorly understood to date.

Synthesis and Toxicity Evaluation of New Pyrroles Obtained by the Reaction of Activated Alkynes with 1-Methyl-3-(cyanomethyl)benzimidazolium Bromide

A series of new pyrrole derivatives were designed as chemical analogs of the 1,4-dihydropyridines drugs in order to develop future new calcium channel blockers. The new tri- and tetra-substituted N-arylpyrroles were synthesized by the one-pot reaction of 1-methyl-3-cyanomethyl benzimidazolium bromide with substituted alkynes having at least one electron-withdrawing substituent, in 1,2-epoxybutane, acting both as the solvent and reagent to generate the corresponding benzimidazolium N3-ylide. The structural characterization of the new substituted pyrroles was based on IR, NMR spectroscopy as well as on single crystal X-ray analysis. The toxicity of the new compounds was assessed on the plant cell using Triticum aestivum L. species and on the animal cell using Artemia franciscana Kellogg and Daphnia magna Straus crustaceans. The compounds showed minimal phytotoxicity on Triticum rootlets and virtually no acute toxicity on Artemia nauplii, while on Daphnia magna, it induced moderate to high toxicity, similar to nifedipine. Our research indicates that the newly synthetized pyrrole derivatives are promising molecules with biological activity and low acute toxicity.

Toxicological Analysis of Gonad Development in Green Mussels (Perna viridis) in Jakarta Bay, Indonesia

&lt;b&gt;Background and Objective:&lt;/b&gt; The accumulation of heavy metals such as cadmium and lead in mussels is very high compared to that in another marine biota. The mussels are sessile, widely distributed filter-feeding organisms, with the ability to sequester many lipophilic organic compounds, absorb anything in their surroundings. The very low mobility allows heavy metal bioaccumulation to occur and cannot avoid pollutants, which increase over time. This bioaccumulation can be toxic to mussels. This study aimed to evaluate the effect of different toxic chemicals and histological changes in green mussels. &lt;b&gt;Materials and Methods:&lt;/b&gt; All archive gonad sample of green mussels in 2015 was fixed in paraformaldehyde 4% solution and were sliced by a rotary microtome at 8 μm thickness and finally, the slides were stained with a solution of hematoxylin-eosin. &lt;b&gt;Results:&lt;/b&gt; The obtained results demonstrated that developmental disorders in the testes are characterized by the arrangement of follicle cells in a relatively less dense state and some follicles are not fully filled with spermatozoa. It means that the male gonad samples of green mussels in the port of Muara Angke undergoing toxicity and the process of gonad developmental was disrupted. &lt;b&gt;Conclusion:&lt;/b&gt; The effects of toxicity of the male gonad of green mussels were more sensitive and were more susceptible than the female gonad of the green mussels.

Jacek Namieśnik-Analytical Chemist and Dedicated Biker: From Wine Analysis to Toxic Compounds

Jacek Namieśnik, who died at the age of 69, was one of the most influential analytical chemists in Poland at the second half of the 20th century and the first two decades of the 21st century [...].

Xenobiotic metabolism and transport in Caenorhabditis elegans

Caenorhabditis elegans has emerged as a major model in biomedical and environmental toxicology. Numerous papers on toxicology and pharmacology in C. elegans have been published, and this species has now been adopted by investigators in academic toxicology, pharmacology, and drug discovery labs. C. elegans has also attracted the interest of governmental regulatory agencies charged with evaluating the safety of chemicals. However, a major, fundamental aspect of toxicological science remains underdeveloped in C. elegans: xenobiotic metabolism and transport processes that are critical to understanding toxicokinetics and toxicodynamics, and extrapolation to other species. The aim of this review was to initially briefly describe the history and trajectory of the use of C. elegans in toxicological and pharmacological studies. Subsequently, physical barriers to chemical uptake and the role of the worm microbiome in xenobiotic transformation were described. Then a review of what is and is not known regarding the classic Phase I, Phase II, and Phase III processes was performed. In addition, the following were discussed (1) regulation of xenobiotic metabolism; (2) review of published toxicokinetics for specific chemicals; and (3) genetic diversity of these processes in C. elegans. Finally, worm xenobiotic transport and metabolism was placed in an evolutionary context; key areas for future research highlighted; and implications for extrapolating C. elegans toxicity results to other species discussed.

Logistic Regression Models with Unspecified Low Dose-Response Relationships and Experimental Designs for Hormesis Studies

Hormesis refers to a nonmonotonic (biphasic) dose-response relationship in toxicology, environmental science, and related fields. In the presence of hormesis, a low dose of a toxic agent may have a lower risk than the risk at the control dose, and the risk may increase at high doses. When the sample size is small due to practical, logistic, and ethical considerations, a parametric model may provide an efficient approach to hypothesis testing at the cost of adopting a strong assumption, which is not guaranteed to be true. In this article, we first consider alternative parameterizations based on the traditional three-parameter logistic regression. The new parameterizations attempt to provide robustness to model misspecification by allowing an unspecified dose-response relationship between the control dose and the first nonzero experimental dose. We then consider experimental designs including the uniform design (the same sample size per dose group) and the c -optimal design (minimizing the standard error of an estimator for a parameter of interest). Our simulation studies showed that (1) the c -optimal design under the traditional three-parameter logistic regression does not help reducing an inflated Type I error rate due to model misspecification, (2) it is helpful under the new parameterization with three parameters (Type I error rate is close to a fixed significance level), and (3) the new parameterization with four parameters and the c -optimal design does not reduce statistical power much while preserving the Type I error rate at a fixed significance level.

Setting the stage for next-generation risk assessment with non-animal approaches: the EU-ToxRisk project experience

In 2016, the European Commission launched the EU-ToxRisk research project to develop and promote animal-free approaches in toxicology. The 36 partners of this consortium used in vitro and in silico methods in the context of case studies (CSs). These CSs included both compounds with a highly defined target (e.g. mitochondrial respiratory chain inhibitors) as well as compounds with poorly defined molecular initiation events (e.g. short-chain branched carboxylic acids). The initial project focus was on developing a science-based strategy for read-across (RAx) as an animal-free approach in chemical risk assessment. Moreover, seamless incorporation of new approach method (NAM) data into this process (= NAM-enhanced RAx) was explored. Here, the EU-ToxRisk consortium has collated its scientific and regulatory learnings from this particular project objective. For all CSs, a mechanistic hypothesis (in the form of an adverse outcome pathway) guided the safety evaluation. ADME data were generated from NAMs and used for comprehensive physiological-based kinetic modelling. Quality assurance and data management were optimized in parallel. Scientific and Regulatory Advisory Boards played a vital role in assessing the practical applicability of the new approaches. In a next step, external stakeholders evaluated the usefulness of NAMs in the context of RAx CSs for regulatory acceptance. For instance, the CSs were included in the OECD CS portfolio for the Integrated Approach to Testing and Assessment project. Feedback from regulators and other stakeholders was collected at several stages. Future chemical safety science projects can draw from this experience to implement systems toxicology-guided, animal-free next-generation risk assessment.

21st Century Tools for Nanotoxicology: Transcriptomic Biomarker Panel and Precision-Cut Lung Slice Organ Mimic System for the Assessment of Nanomaterial-Induced Lung Fibrosis

There is an urgent need for reliable toxicity assays to support the human health risk assessment of an ever increasing number of engineered nanomaterials (ENMs). Animal testing is not a suitable option for ENMs. Sensitive in vitro models and mechanism-based targeted in vitro assays that enable accurate prediction of in vivo responses are not yet available. In this proof-of-principle study, publicly available mouse lung transcriptomics data from studies investigating xenobiotic-induced lung diseases are used and a 17-gene biomarker panel (PFS17) applicable to the assessment of lung fibrosis is developed. The PFS17 is validated using a limited number of in vivo mouse lung transcriptomics datasets from studies investigating ENM-induced responses. In addition, an ex vivo precision-cut lung slice (PCLS) model is optimized for screening of potentially inflammogenic and pro-fibrotic ENMs. Using bleomycin and a multiwalled carbon nanotube, the practical application of the PCLS method as a sensitive alternative to whole animal tests to screen ENMs that may potentially induce inhalation toxicity is shown. Conditional to further optimization and validation, it is established that a combination of PFS17 and the ex vivo PCLS method will serve as a robust and sensitive approach to assess lung inflammation and fibrosis induced by ENMs.

Nanotoxicology: The Need for a Human Touch?

With the ever-expanding number of manufactured nanomaterials (MNMs) under development there is a vital need for nanotoxicology studies that test the potential for MNMs to cause harm to health. An extensive body of work in cell cultures and animal models is vital to understanding the physicochemical characteristics of MNMs and the biological mechanisms that underlie any detrimental actions to cells and organs. In human subjects, exposure monitoring is combined with measurement of selected health parameters in small panel studies, especially in occupational settings. However, the availability of further in vivo human data would greatly assist the risk assessment of MNMs. Here, the potential for controlled inhalation exposures of MNMs in human subjects is discussed. Controlled exposures to carbon, gold, aluminum, and zinc nanoparticles in humans have already set a precedence to demonstrate the feasibility of this approach. These studies have provided considerable insight into the potential (or not) of nanoparticles to induce inflammation, alter lung function, affect the vasculature, reach the systemic circulation, and accumulate in other organs. The need for further controlled exposures of MNMs in human volunteers - to establish no-effect limits, biological mechanisms, and provide vital data for the risk assessment of MNMs - is advocated.

Role of Artificial Intelligence and Machine Learning in Nanosafety

Robotics and automation provide potentially paradigm shifting improvements in the way materials are synthesized and characterized, generating large, complex data sets that are ideal for modeling and analysis by modern machine learning (ML) methods. Nanomaterials have not yet fully captured the benefits of automation, so lag behind in the application of ML methods of data analysis. Here, some key developments in, and roadblocks to the application of ML methods are reviewed to model and predict potentially adverse biological and environmental effects of nanomaterials. This work focuses on the diverse ways a range of ML algorithms are applied to understand and predict nanomaterials properties, provides examples of the application of traditional ML and deep learning methods to nanosafety, and provides context and future perspectives on developments that are likely to occur, or need to occur in the near future that allow artificial intelligence to make a deeper contribution to nanosafety.

Mechanistic Similarities between 3D Human Bronchial Epithelium and Mice Lung, Exposed to Copper Oxide Nanoparticles, Support Non-Animal Methods for Hazard Assessment

The diversity and increasing prevalence of products derived from engineered nanomaterials (ENM), warrants implementation of non-animal approaches to health hazard assessment for ethical and practical reasons. Although non-animal approaches are becoming increasingly popular, there are almost no studies of side-by-side comparisons with traditional in vivo assays. Here, transcriptomics is used to investigate mechanistic similarities between healthy/asthmatic models of 3D air-liquid interface (ALI) cultures of donor-derived human bronchial epithelia cells, and mouse lung tissue, following exposure to copper oxide ENM. Only 19% of mouse lung genes with human orthologues are not expressed in the human 3D ALI model. Despite differences in taxonomy and cellular complexity between the systems, a core subset of matching genes cluster mouse and human samples strictly based on ENM dose (exposure severity). Overlapping gene orthologue pairs are highly enriched for innate immune functions, suggesting an important and maybe underestimated role of epithelial cells. In conclusion, 3D ALI models based on epithelial cells, are primed to bridge the gap between traditional 2D in vitro assays and animal models of airway exposure, and transcriptomics appears to be a unifying dose metric that links in vivo and in vitro test systems.

Safety Considerations of Cancer Nanomedicine-A Key Step toward Translation

The rate of translational effort of nanomedicine requires strategic planning of nanosafety research in order to enable clinical trials and safe use of nanomedicine in patients. Herein, the experiences that have emerged based on the safety data of classic liposomal formulations in the space of oncology are discussed, along with a description of the new challenges that need to be addressed according to the rapid expansion of nanomedicine platform beyond liposomes. It is valuable to consider the combined use of predictive toxicological assessment supported by deliberate investigation on aspects such as absorption, distribution, metabolism, and excretion (ADME) and toxicokinetic profiles, the risk that may be introduced during nanomanufacture, unique nanomaterials properties, and nonobvious nanosafety endpoints, for example. These efforts will allow the generation of investigational new drug-enabling safety data that can be incorporated into a rational infrastructure for regulatory decision-making. Since the safety assessment relates to nanomaterials, the investigation should cover the important physicochemical properties of the material that may lead to hazards when the nanomedicine product is utilized in humans.

Drug Development 101: A Primer

Drug development is a term used to define the entire process of bringing a new drug or device to market. It is an integrated, multidisciplinary endeavor that includes drug discovery, chemistry and pharmacology, nonclinical safety testing, manufacturing, clinical trials, and regulatory submissions. This report summarizes presentations of a workshop entitled "Drug Development 101," held at the 39th Annual Meeting of the American College of Toxicology in West Palm Beach, Florida. The workshop was designed to provide an introductory overview of drug development. Experienced scientists from industry and government provided overviews of each area, with a focus on safety assessment, and described some of the challenges that can arise. The role of chemistry and manufacturing was discussed in the context of early- and late-stage product development and approaches to assess, control, and limit impurities. The toxicologic assessment was emphasized in early-phase development, from the selection of a candidate drug through the determination of a first-in-human starting dose. Clinical trial development was discussed in the context of regulatory requirements and expectations. The final topic of issues and considerations in the review processes of different types of submissions to Food and Drug Administration included advice for best practices in authoring good Investigational New Drug and New Drug Application/Biologic License Application submissions and interacting effectively with regulatory reviewers.

Nanotoxicology and Nanosafety: Safety-By-Design and Testing at a Glance

This review offers a systematic discussion about nanotoxicology and nanosafety associated with nanomaterials during manufacture and further biomedical applications. A detailed introduction on nanomaterials and their most frequently uses, followed by the critical risk aspects related to regulatory uses and commercialization, is provided. Moreover, the impact of nanotoxicology in research over the last decades is discussed, together with the currently available toxicological methods in cell cultures (in vitro) and in living organisms (in vivo). A special focus is given to inorganic nanoparticles such as titanium dioxide nanoparticles (TiO2NPs) and silver nanoparticles (AgNPs). In vitro and in vivo case studies for the selected nanoparticles are discussed. The final part of this work describes the significance of nano-security for both risk assessment and environmental nanosafety. "Safety-by-Design" is defined as a starting point consisting on the implementation of the principles of drug discovery and development. The concept "Safety-by-Design" appears to be a way to "ensure safety", but the superficiality and the lack of articulation with which it is treated still raises many doubts. Although the approach of "Safety-by-Design" to the principles of drug development has helped in the assessment of the toxicity of nanomaterials, a combination of scientific efforts is constantly urgent to ensure the consistency of methods and processes. This will ensure that the quality of nanomaterials is controlled and their safe development is promoted. Safety issues are considered strategies for discovering novel toxicological-related mechanisms still needed to be promoted.

Semantic characterization of adverse outcome pathways

This study was undertaken to systematically assess the utilities and performance of ontology-based semantic analysis in adverse outcome pathway (AOP) research. With an increasing number of AOPs developed by scientific domain experts to organize toxicity information and facilitate chemical risk assessment, there is a pressing need for objective approaches to evaluate the biological coherence and quality of these AOPs. Powered by ontologies covering a wide range of biological domains, abundant phenotypic data annotated ontologically, and some sophisticated knowledge computing tools, semantic analysis has great potential in this area of application. With the events in the AOP-Wiki first annotated into logical definitions and then grouped into phenotypic profiles by individual AOPs, the coherence and quality of AOPs were assessed at several levels: paired key event relationships (KER), all possible event pair combinations within AOPs, and the phenotypic profiles of AOPs, genes, biological pathways, human diseases, and selected chemicals. The semantic similarities were assessed at all these levels based on a unified cross-species vertebrate phenotype ontology encompassing the logical definitions of AOP events as well as many other domain ontologies. A substantial number of KERs and AOPs in the AOP-Wiki were found to be semantically coherent. These same coherent AOPs also mapped to many more genes, pathways, and diseases biologically aligned with the intended chain of events therein leading to their respective adverse outcomes. Significantly, these findings imply that semantic analysis should also have utilities in developing future AOPs by selecting candidate events from either the existing AOP-Wiki events or a broader collection of ontology terms semantically similar to the molecular initiating events or adverse outcomes of interest. In addition, semantic analysis enabled AOP networks to be constructed at the level of phenotypic profiles based on similarities, complementing those based on event sharing by bringing genes, pathways, diseases, and chemicals into the networks too-thus greatly expanding the biological scope and our understanding of AOPs.

Substance use on admission toxicology screen is associated with peri-injury factors and six-month outcome after traumatic brain injury: A TRACK-TBI Pilot study

Substance use is commonly associated with traumatic brain injury (TBI). We investigate associations between active substance use, peri-injury factors, and outcome after TBI across three U.S. Level I trauma centers. TBI subjects from the prospective Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot (TRACK-TBI Pilot) with Marshall computed tomography (CT) score 1-3, no neurosurgical procedure/operation, and admission urine toxicology screen (tox+/-) were extracted. Associations between tox+/-, comorbidities, hospital variables, and six-month functional (GOSE) and neuropsychiatric (PCL-C, BSI18, RPQ-13, SWLS) outcomes were analyzed. Multivariable regression was performed for associations significant on univariate analysis with odds ratios (mOR) presented. Significance assessed at p < 0.05. In 133 subjects, tox+/tox- were 29.1%/72.9%. Tox+ was younger (35.5/43.6-years, p = 0.018), trended toward male sex (80.6%/63.9%, p = 0.067), was associated with history of seizures (27.8%/10.3%, p = 0.012), self-reported substance use (44.4%/17.5%, p = 0.001), prior TBI (58.8%/34.1%, p = 0.009), GCS < 15 (69.4%/48.4%, p = 0.031) and blood alcohol level >0.08-mg/dl (55.6%/30.8%, p = 0.022). In CT-negative subjects, tox+ was associated with increased hospital admission (95.7%/66.7%, p = 0.034). At six-months, tox+ was associated with screening positive for post-traumatic stress disorder (PCL-C: 40.0%/15.9%; mOR = 8.24, p = 0.022) and psychiatric symptoms (BSI18: 40.0%/14.3%, mOR = 11.06, p = 0.023). Active substance use in TBI may confound GCS assessment, triage to higher level of care, and be associated with increased six-month neuropsychiatric symptoms. Substance use screening should be integrated into standard emergency/acute care TBI protocols to optimize management and resource utilization. Clinicians should be vigilant in providing education, counselling, and follow-up for TBI patients with substance use.

Potential frameworks to support evaluation of mechanistic data for developmental neurotoxicity outcomes: A symposium report

A key challenge in systematically incorporating mechanistic data into human health assessments is that, compared to studies of apical health endpoints, these data are both more abundant (mechanistic studies routinely outnumber other studies by several orders of magnitude) and more heterogeneous (e.g. different species, test system, tissue, cell type, exposure paradigm, or specific assays performed). A structured decision-making process for organizing, integrating, and weighing mechanistic DNT data for use in human health risk assessments will improve the consistency and efficiency of such evaluations. At the Developmental Neurotoxicology Society (DNTS) 2016 annual meeting, a symposium was held to address the application of existing organizing principles and frameworks for evaluation of mechanistic data relevant to interpreting neurotoxicology data. Speakers identified considerations with potential to advance the use of mechanistic DNT data in risk assessment, including considering the context of each exposure, since epigenetics, tissue type, sex, stress, nutrition and other factors can modify toxicity responses in organisms. It was also suggested that, because behavior is a manifestation of complex nervous system function, the presence and absence of behavioral change itself could be used to organize the interpretation of multiple complex simultaneous mechanistic changes. Several challenges were identified with frameworks and their implementation, and ongoing research to develop these approaches represents an early step toward full evaluation of mechanistic DNT data for assessments.

SiO2-PVA-Fe(acac)3 Hybrid Based Superparamagnetic Nanocomposites for Nanomedicine: Morpho-textural Evaluation and In Vitro Cytotoxicity Assay

A facile sol-gel route has been applied to synthesize hybrid silica-PVA-iron oxide nanocomposite materials. A step-by-step calcination (processing temperatures up to 400 °C) was applied in order to oxidize the organics together with the iron precursor. Transmission electron microscopy, X-ray diffraction, small angle neutron scattering, and nitrogen porosimetry were used to determine the temperature-induced morpho-textural modifications. In vitro cytotoxicity assay was conducted by monitoring the cell viability by the means of MTT assay to qualify the materials as MRI contrast agents or as drug carriers. Two cell lines were considered: the HaCaT (human keratinocyte cell line) and the A375 tumour cell line of human melanoma. Five concentrations of 10 µg/mL, 30 µg/mL, 50 µg/mL, 100 µg/mL, and 200 µg/mL were tested, while using DMSO (dimethylsulfoxid) and PBS (phosphate saline buffer) as solvents. The HaCaT and A375 cell lines were exposed to the prepared agent suspensions for 24 h. In the case of DMSO (dimethyl sulfoxide) suspensions, the effect on human keratinocytes migration and proliferation were also evaluated. The results indicate that only the concentrations of 100 μg/mL and 200 μg/mL of the nanocomposite in DMSO induced a slight decrease in the HaCaT cell viability. The PBS based in vitro assay showed that the nanocomposite did not present toxicity on the HaCaT cells, even at high doses (200 μg/mL agent).

On the difficulties of being rigorous in environmental geochemistry studies: some recommendations for designing an impactful paper

There have been numerous environmental geochemistry studies using chemical, geological, ecological, and toxicological methods but each of these fields requires more subject specialist rigour than has generally been applied so far. Field-specific terminology has been misused and the resulting interpretations rendered inaccurate. In this paper, we propose a series of suggestions, based on our experience as teachers, researchers, reviewers, and editorial board members, to help authors to avoid pitfalls. Many scientific inaccuracies continue to be unchecked and are repeatedly republished by the scientific community. These recommendations should help our colleagues and editorial board members, as well as reviewers, to avoid the numerous inaccuracies and misconceptions currently in circulation and establish a trend towards greater rigour in scientific writing.

New Toxicology Tools and the Emerging Paradigm Shift in Environmental Health Decision-Making

BACKGROUND

Numerous types of rapid toxicity or exposure assays and platforms are providing information relevant to human hazard and exposure identification. They offer the promise of aiding decision-making in a variety of contexts including the regulatory management of chemicals, evaluation of products and environmental media, and emergency response. There is a need to consider both the scientific validity of the new methods and the values applied to a given decision using this new information to ensure that the new methods are employed in ways that enhance public health and environmental protection. In 2018, a National Academies of Sciences, Engineering, and Medicine (NASEM) workshop examined both the toxicological and societal aspects of this challenge.

OBJECTIVES

Our objectives were to explore the challenges of adopting new data streams into regulatory decision-making and highlight the need to align new methods with the information and confidence needs of the decision contexts in which the data may be applied.

METHODS

We go beyond the NASEM workshop to further explore the requirements of different decision contexts. We also call for the new methods to be applied in a manner consistent with the core values of public health and environmental protection. We use the case examples presented in the NASEM workshop to illustrate a range of decision contexts that have applied or could benefit from these new data streams. Organizers of the NASEM workshop came together to further evaluate the main themes from the workshop and develop a joint assessment of the critical needs for improved use of emerging toxicology tools in decision-making. We have drawn from our own experience and individual decision or research contexts as well as from the case studies and panel discussions from the workshop to inform our assessment.

DISCUSSION

Many of the statutes that regulate chemicals in the environment place a high priority on the protection of public health and the environment. Moving away from the sole reliance on traditional approaches and information sources used in hazard, exposure, and risk assessment, toward the more expansive use of rapidly acquired chemical information via in vitro, in silico, and targeted testing strategies will require careful consideration of the information needed and values considerations associated with a particular decision. In this commentary, we explore the ability and feasibility of using emerging data streams, particularly those that allow for the rapid testing of a large number of chemicals across numerous biological targets, to shift the chemical testing paradigm to one in which potentially harmful chemicals are more rapidly identified, prioritized, and addressed. Such a paradigm shift could ultimately save financial and natural resources while ensuring and preserving the protection of public health. https://doi.org/10.1289/EHP4745.

Review of high-content screening applications in toxicology

High-content screening (HCS) technology combining automated microscopy and quantitative image analysis can address biological questions in academia and the pharmaceutical industry. Various HCS experimental applications have been utilized in the research field of in vitro toxicology. In this review, we describe several HCS application approaches used for studying the mechanism of compound toxicity, highlight some challenges faced in the toxicological community, and discuss the future directions of HCS in regards to new models, new reagents, data management, and informatics. Many specialized areas of toxicology including developmental toxicity, genotoxicity, developmental neurotoxicity/neurotoxicity, hepatotoxicity, cardiotoxicity, and nephrotoxicity will be examined. In addition, several newly developed cellular assay models including induced pluripotent stem cells (iPSCs), three-dimensional (3D) cell models, and tissues-on-a-chip will be discussed. New genome-editing technologies (e.g., CRISPR/Cas9), data analyzing tools for imaging, and coupling with high-content assays will be reviewed. Finally, the applications of machine learning to image processing will be explored. These new HCS approaches offer a huge step forward in dissecting biological processes, developing drugs, and making toxicology studies easier.

Transitioning to composite bacterial mutagenicity models in ICH M7 (Q)SAR analyses

The International Council on Harmonisation (ICH) M7(R1) guideline describes the use of complementary (quantitative) structure-activity relationship ((Q)SAR) models to assess the mutagenic potential of drug impurities in new and generic drugs. Historically, the CASE Ultra and Leadscope software platforms used two different statistical-based models to predict mutations at G-C (guanine-cytosine) and A-T (adenine-thymine) sites, to comprehensively assess bacterial mutagenesis. In the present study, composite bacterial mutagenicity models covering multiple mutation types were developed. These new models contain more than double the number of chemicals (n = 9,254 and n = 13,514) than the corresponding non-composite models and show better toxicophore coverage. Additionally, the use of a single composite bacterial mutagenicity model simplifies impurity analysis in an ICH M7 (Q)SAR workflow by reducing the number of model outputs requiring review. An external validation set of 388 drug impurities representing proprietary pharmaceutical chemical space showed performance statistics ranging from of 66-82% in sensitivity, 91-95% in negative predictivity and 96% in coverage. This effort represents a major enhancement to these (Q)SAR models and their use under ICH M7(R1), leading to improved patient safety through greater predictive accuracy, applicability, and efficiency when assessing the bacterial mutagenic potential of drug impurities.

Genetic toxicology in silico protocol

In silico toxicology (IST) approaches to rapidly assess chemical hazard, and usage of such methods is increasing in all applications but especially for regulatory submissions, such as for assessing chemicals under REACH as well as the ICH M7 guideline for drug impurities. There are a number of obstacles to performing an IST assessment, including uncertainty in how such an assessment and associated expert review should be performed or what is fit for purpose, as well as a lack of confidence that the results will be accepted by colleagues, collaborators and regulatory authorities. To address this, a project to develop a series of IST protocols for different hazard endpoints has been initiated and this paper describes the genetic toxicity in silico (GIST) protocol. The protocol outlines a hazard assessment framework including key effects/mechanisms and their relationships to endpoints such as gene mutation and clastogenicity. IST models and data are reviewed that support the assessment of these effects/mechanisms along with defined approaches for combining the information and evaluating the confidence in the assessment. This protocol has been developed through a consortium of toxicologists, computational scientists, and regulatory scientists across several industries to support the implementation and acceptance of in silico approaches.

Progress in data interoperability to support computational toxicology and chemical safety evaluation

New approach methodologies (NAMs) in chemical safety evaluation are being explored to address the current public health implications of human environmental exposures to chemicals with limited or no data for assessment. For over a decade since a push toward "Toxicity Testing in the 21st Century," the field has focused on massive data generation efforts to inform computational approaches for preliminary hazard identification, adverse outcome pathways that link molecular initiating events and key events to apical outcomes, and high-throughput approaches to risk-based ratios of bioactivity and exposure to inform relative priority and safety assessment. Projects like the interagency Tox21 program and the US EPA ToxCast program have generated dose-response information on thousands of chemicals, identified and aggregated information from legacy systems, and created tools for access and analysis. The resulting information has been used to develop computational models as viable options for regulatory applications. This progress has introduced challenges in data management that are new, but not unique, to toxicology. Some of the key questions require critical thinking and solutions to promote semantic interoperability, including: (1) identification of bioactivity information from NAMs that might be related to a biological process; (2) identification of legacy hazard information that might be related to a key event or apical outcomes of interest; and, (3) integration of these NAM and traditional data for computational modeling and prediction of complex apical outcomes such as carcinogenesis. This work reviews a number of toxicology-related efforts specifically related to bioactivity and toxicological data interoperability based on the goals established by Findable, Accessible, Interoperable, and Reusable (FAIR) Data Principles. These efforts are essential to enable better integration of NAM and traditional toxicology information to support data-driven toxicology applications.

Integrating in silico models and read-across methods for predicting toxicity of chemicals: A step-wise strategy

In silico methods and models are increasingly used for predicting properties of chemicals for hazard identification and hazard characterisation in the absence of experimental toxicity data. Many in silico models are available and can be used individually or in an integrated fashion. Whilst such models offer major benefits to toxicologists, risk assessors and the global scientific community, the lack of a consistent framework for the integration of in silico results can lead to uncertainty and even contradictions across models and users, even for the same chemicals. In this context, a range of methods for integrating in silico results have been proposed on a statistical or case-specific basis. Read-across constitutes another strategy for deriving reference points or points of departure for hazard characterisation of untested chemicals, from the available experimental data for structurally-similar compounds, mostly using expert judgment. Recently a number of software systems have been developed to support experts in this task providing a formalised and structured procedure. Such a procedure could also facilitate further integration of the results generated from in silico models and read-across. This article discusses a framework on weight of evidence published by EFSA to identify the stepwise approach for systematic integration of results or values obtained from these "non-testing methods". Key criteria and best practices for selecting and evaluating individual in silico models are also described, together with the means to combining the results, taking into account any limitations, and identifying strategies that are likely to provide consistent results.

ToxRefDB version 2.0: Improved utility for predictive and retrospective toxicology analyses

The Toxicity Reference Database (ToxRefDB) structures information from over 5000 in vivo toxicity studies, conducted largely to guidelines or specifications from the US Environmental Protection Agency and the National Toxicology Program, into a public resource for training and validation of predictive models. Herein, ToxRefDB version 2.0 (ToxRefDBv2) development is described. Endpoints were annotated (e.g. required, not required) according to guidelines for subacute, subchronic, chronic, developmental, and multigenerational reproductive designs, distinguishing negative responses from untested. Quantitative data were extracted, and dose-response modeling for nearly 28,000 datasets from nearly 400 endpoints using Benchmark Dose (BMD) Modeling Software were generated and stored. Implementation of controlled vocabulary improved data quality; standardization to guideline requirements and cross-referencing with United Medical Language System (UMLS) connects ToxRefDBv2 observations to vocabularies linked to UMLS, including PubMed medical subject headings. ToxRefDBv2 allows for increased connections to other resources and has greatly enhanced quantitative and qualitative utility for predictive toxicology.

Determination of chemical-disease risk values to prioritize connections between environmental factors, genetic variants, and human diseases

Traditional methods for chemical risk assessment are too time-consuming and resource-intensive to characterize either the diversity of chemicals to which humans are exposed or how that diversity may manifest in population susceptibility differences. The advent of novel toxicological data sources and their integration with bioinformatic databases affords opportunities for modern approaches that consider gene-environment (GxE) interactions in population risk assessment. Here, we present an approach that systematically links multiple data sources to relate chemical risk values to diseases and gene-disease variants. These data sources include high-throughput screening (HTS) results from Tox21/ToxCast, chemical-disease relationships from the Comparative Toxicogenomics Database (CTD), hazard data from resources like the Integrated Risk Information System, exposure data from the ExpoCast initiative, and gene-variant-disease information from the DisGeNET database. We use these integrated data to identify variants implicated in chemical-disease enrichments and develop a new value that estimates the risk of these associations toward differential population responses. Finally, we use this value to prioritize chemical-disease associations by exploring the genomic distribution of variants implicated in high-risk diseases. We offer this modular approach, termed DisQGOS (Disease Quotient Genetic Overview Score), for relating overall chemical-disease risk to potential for population variable responses, as a complement to methods aiming to modernize aspects of risk assessment.

Using a hybrid read-across method to evaluate chemical toxicity based on chemical structure and biological data

Read-across has become a primary approach to fill data gaps for chemical safety assessments. Chemical similarity based on structure, reactivity, and physic-chemical property information is a traditional approach applied for read-across toxicity studies. However, toxicity mechanisms are usually complicated in a biological system, so only using chemical similarity to perform the read-across for new compounds was not satisfactory for most toxicity endpoints, especially when the chemically similar compounds show dissimilar toxicities. This study aims to develop an enhanced read-across method for chemical toxicity predictions. To this end, we used two large toxicity datasets for read-across purposes. One consists of 3979 compounds with Ames mutagenicity data, and the other contains 7332 compounds with rat acute oral toxicity data. First, biological data for all compounds in these two datasets were obtained by querying thousands of PubChem bioassays. The PubChem bioassays with at least five compounds from either of these two datasets showing active responses were selected to generate comprehensive bioprofiles. The read-across studies were performed by using chemical similarity search only and also by using a hybrid similarity search based on both chemical descriptors and bioprofiles. Compared to traditional read-across based on chemical similarity, the hybrid read-across approach showed improved accuracy of predictions for both Ames mutagenicity and acute oral toxicity. Furthermore, we could illustrate potential toxicity mechanisms by analyzing the bioprofiles used for this hybrid read-across study. The results of this study indicate that the new hybrid read-across approach could be an applicable computational tool for chemical toxicity predictions. In this way, the bottleneck of traditional read-across studies can be overcome by introducing public biological data into the traditional process. The incorporation of bioprofiles generated from the additional biological data for compounds can partially solve the "activity cliff" issue and reveal their potential toxicity mechanisms. This study leads to a promising direction to utilize data-driven approaches for computational toxicology studies in the big data era.

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.

Making Waves: New Developments in Toxicology With the Zebrafish

The laboratory zebrafish (Danio rerio) is now an accepted model in toxicologic research. The zebrafish model fills a niche between in vitro models and mammalian biomedical models. The developmental characteristics of the small fish are strategically being used by scientists to study topics ranging from high-throughput toxicity screens to toxicity in multi- and transgenerational studies. High-throughput technology has increased the utility of zebrafish embryonic toxicity assays in screening of chemicals and drugs for toxicity or effect. Additionally, advances in behavioral characterization and experimental methodology allow for observation of recognizable phenotypic changes after xenobiotic exposure. Future directions in zebrafish research are predicted to take advantage of CRISPR-Cas9 genome editing methods in creating models of disease and interrogating mechanisms of action with fluorescent reporters or tagged proteins. Zebrafish can also model developmental origins of health and disease and multi- and transgenerational toxicity. The zebrafish has many advantages as a toxicologic model and new methodologies and areas of study continue to expand the usefulness and application of the zebrafish.

In Vitro Tests within the REACH Information Strategies

Tonnage-based information requirements are specified in the proposal on the regulation on the Registration, Evaluation and Authorisation of Chemicals (REACH) in the European Union. The hazard assessment for toxic endpoints should be performed by using a tiered approach, i.e. as an information strategy (IS), starting with an evaluation of all of the data already available, including animal in vivo and in vitro data, and human evidence and case reports, as well as data from (Quantitative)-Structure Activity Relationships ([Q]SARs) or read-across, before any further testing is suggested. To contribute to the implementation of the REACH system, the Nordic countries launched two projects: 1) a review of currently used testing strategies, including a comparison with the REACH requirements; and 2) the development of detailed ISs for skin and eye irritation/corrosion. The review showed that the ISs and classification criteria for the selected endpoints are inconsistent in many cases. In the classification criteria, human data and in vivo test results are usually the prerequisites. Other types of information, such as data from in vitro studies, can sometimes be used, but usually as supportive evidence only. This differs from the REACH ISs, where QSARs, read-across and in vitro testing are important elements. In the other part of the project, an IS for skin and eye irritation/corrosion was proposed. The strategy was “tested” by using four high production volume (HPV) chemicals: hydrogen peroxide, methyl tertiary-butyl ether (MTBE), trivalent chromium, and diantimony trioxide, but only MTBE and trivalent chromium are dealt with in this paper. The “test” revealed that in vivo data, human case reports and physical-chemical data were available and could be used in the evaluation. Classification could be based on the proposed IS and the existing data in all cases, except for the eye irritation/corrosion of trivalent chromium. Weight-of-evidence analysis appeared to be a useful step in the ISs proposed, and including it in the REACH strategies should be considered. For these chemicals, few in vitro and (Q)SAR data were available — more of these data would be generated, if the relevant guidance and legislation on classification were updated.