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.

Zebrafish as a Mainstream Model for In Vivo Systems Pharmacology and Toxicology.

Pharmacology and toxicology are part of a much broader effort to understand the relationship between chemistry and biology. While biomedicine has necessarily focused on specific cases, typically of direct human relevance, there are real advantages in pursuing more systematic approaches to characterizing how health and disease are influenced by small molecules and other interventions. In this context, the zebrafish is now established as the representative screenable vertebrate and, through ongoing advances in the available scale of genome editing and automated phenotyping, is beginning to address systems-level solutions to some biomedical problems. The addition of broader efforts to integrate information content across preclinical model organisms and the incorporation of rigorous analytics, including closed-loop deep learning, will facilitate efforts to create systems pharmacology and toxicology with the ability to continuously optimize chemical biological interactions around societal needs. In this review, we outline progress toward this goal.

TOXRIC: a comprehensive database of toxicological data and benchmarks.

The toxic effects of compounds on environment, humans, and other organisms have been a major focus of many research areas, including drug discovery and ecological research. Identifying the potential toxicity in the early stage of compound/drug discovery is critical. The rapid development of computational methods for evaluating various toxicity categories has increased the need for comprehensive and system-level collection of toxicological data, associated attributes, and benchmarks. To contribute toward this goal, we proposed TOXRIC (https://toxric.bioinforai.tech/), a database with comprehensive toxicological data, standardized attribute data, practical benchmarks, informative visualization of molecular representations, and an intuitive function interface. The data stored in TOXRIC contains 113 372 compounds, 13 toxicity categories, 1474 toxicity endpoints covering in vivo/in vitro endpoints and 39 feature types, covering structural, target, transcriptome, metabolic data, and other descriptors. All the curated datasets of endpoints and features can be retrieved, downloaded and directly used as output or input to Machine Learning (ML)-based prediction models. In addition to serving as a data repository, TOXRIC also provides visualization of benchmarks and molecular representations for all endpoint datasets. Based on these results, researchers can better understand and select optimal feature types, molecular representations, and baseline algorithms for each endpoint prediction task. We believe that the rich information on compound toxicology, ML-ready datasets, benchmarks and molecular representation distribution can greatly facilitate toxicological investigations, interpretation of toxicological mechanisms, compound/drug discovery and the development of computational methods.

Introduction to the Theme "Old and New Toxicology: Interfaces with Pharmacology".

The theme of Volume 61 is "Old and New Toxicology: Interfaces with Pharmacology." Old toxicology is exemplified by the authors of the autobiographical articles: B.M. Olivera's work on toxins and venoms from cone snails and P. Taylor's studies of acetylcholinesterase and the nicotinic cholinergic receptor, which serve as sites of action for numerous pesticides and venoms. Other articles in this volume focus on new understanding and new types of toxicology, including (a) arsenic toxicity, which is an ancient poison that, through evolution, has caused most multicellular organisms to express an active arsenic methyltransferase to methylate arsenite, which accelerates the excretion of arsenic from the body; (b) small molecules that react with lipid dicarbonyls, which are now considered the most toxic oxidative stress end products; (c) immune checkpoint inhibitors (ICIs), which have revolutionized cancer therapy but have numerous immune-related adverse events, including cardiovascular complications; (d) autoimmunity caused by the environment; (e) idiosyncratic drug-induced liver disease, which together with the toxicity of ICIs represents new toxicology interfacing with pharmacology; and (f) sex differences in the development of cardiovascular disease, with men more susceptible than women to vascular inflammation that initiates and perpetuates disease. These articles and others in Volume 61 reflect the interface and close integration of pharmacology and toxicology that began long ago but continues today.

Feasibility and evaluation of high-fidelity simulation education for acute clinical toxicology.

To prepare medical students appropriately for the management of toxicological emergencies, we have developed a simulation-based medical education (SBME) training in acute clinical toxicology. Our aim is to report on the feasibility, evaluation and lessons learned of this training. Since 2019, each year approximately 180 fifth-year medical students are invited to participate in the SBME training. The training consists of an interactive lecture and two SBME stations. For each station, a team of students had to perform the primary assessment and management of an intoxicated patient. After the training, the students completed a questionnaire about their experiences and confidence in clinical toxicology. Overall, the vast majority of students agreed that the training provided a fun, interactive and stimulating way to teach about clinical toxicology. Additionally, they felt more confident regarding their skills in this area. Our pilot study shows that SBME training was well-evaluated and feasible over a longer period.

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.

Complex In Vitro Model Characterization for Context of Use in Toxicologic Pathology: Use Cases by Collaborative Teams of Biologists, Bioengineers, and Pathologists.

Complex in vitro models (CIVMs) offer the potential to increase the clinical relevance of preclinical efficacy and toxicity assessments and reduce the reliance on animals in drug development. The European Society of Toxicologic Pathology (ESTP) and Society for Toxicologic Pathology (STP) are collaborating to highlight the role of pathologists in the development and use of CIVM. Pathologists are trained in comparative animal medicine which enhances their understanding of mechanisms of human and animal diseases, thus allowing them to bridge between animal models and humans. This skill set is important for CIVM development, validation, and data interpretation. Ideally, diverse teams of scientists, including engineers, biologists, pathologists, and others, should collaboratively develop and characterize novel CIVM, and collectively assess their precise use cases (context of use). Implementing a morphological CIVM evaluation should be essential in this process. This requires robust histological technique workflows, image analysis techniques, and needs correlation with translational biomarkers. In this review, we demonstrate how such tissue technologies and analytics support the development and use of CIVM for drug efficacy and safety evaluations. We encourage the scientific community to explore similar options for their projects and to engage with health authorities on the use of CIVM in benefit-risk assessment.

A Minimal Approach to Demonstrate Concordance of Digital and Conventional Microscopy in Toxicologic Pathology.

Digitalization of pathology workflows has undergone a rapid evolution and has been widely established in the diagnostic field but remains a challenge in the nonclinical safety context due to lack of regulatory guidance and validation experience for good laboratory practice (GLP) use. One means to demonstrate that digital slides are fit for purpose, that is, provide sufficient quality for pathologists to reach a diagnosis, is conduction of comparison studies, which have been published both, for veterinary and human diagnostic pathology, but not for toxicologic pathology. Here, we present an approach that uses study material from nonclinical safety studies and that allows for the statistical comparison of concordance rates for glass and digital slide evaluation while minimizing time and effort for the involved personnel. Using a benchmark study design, we demonstrate that evaluation of digital slides fits the purpose of nonclinical safety evaluation. These results add to reports of successful workflow validations and support the full adaptation of digital pathology in the regulatory field.

Scientific and Regulatory Policy Committee Points to Consider: Review of the United States Food and Drug Administration (FDA) Guidance on Pathology Peer Review in Nonclinical Toxicology Studies.

In December 2021, the United States Food and Drug Administration (FDA) issued the final guidance for industry titled Pathology Peer Review in Nonclinical Toxicology Studies: Questions and Answers. The stated purpose of the FDA guidance is to provide information to sponsors, applicants, and nonclinical laboratory personnel regarding the management and conduct of histopathology peer review as part of nonclinical toxicology studies conducted in compliance with good laboratory practice (GLP) regulations. On behalf of and in collaboration with global societies of toxicologic pathology and the Society of Quality Assurance, the Scientific and Regulatory Policy Committee (SRPC) of the Society of Toxicologic Pathology (STP) initiated a review of this FDA guidance. The STP has previously published multiple papers related to the scientific conduct of a pathology peer review of nonclinical toxicology studies and appropriate documentation practices. The objectives of this review are to provide an in-depth analysis and summary interpretation of the FDA recommendations and share considerations for the conduct of pathology peer review in nonclinical toxicology studies that claim compliance to GLP regulations. In general, this working group is in agreement with the recommendations from the FDA guidance that has added clear expectations for pathology peer review preparation, conduct, and documentation.

A problem formulation framework for the application of in silico toxicology methods in chemical risk assessment.

The first step in the hazard or risk assessment of chemicals should be to formulate the problem through a systematic and iterative process aimed at identifying and defining factors critical to the assessment. However, no general agreement exists on what components an in silico toxicology problem formulation (PF) should include. The present work aims to develop a PF framework relevant to the application of in silico models for chemical toxicity prediction. We modified and applied a PF framework from the general risk assessment literature to peer reviewed papers describing PFs associated with in silico toxicology models. Important gaps between the general risk assessment literature and the analyzed PF literature associated with in silico toxicology methods were identified. While the former emphasizes the need for PFs to address higher-level conceptual questions, the latter does not. There is also little consistency in the latter regarding the PF components addressed, reinforcing the need for a PF framework that enable users of in silico toxicology models to answer the central conceptual questions aimed at defining components critical to the model application. Using the developed framework, we highlight potential areas of uncertainty manifestation in in silico toxicology PF in instances where particular components are missing or implicitly described. The framework represents the next step in standardizing in silico toxicology PF component. The framework can also be used to improve the understanding of how uncertainty is apparent in an in silico toxicology PF, thus facilitating ways to address uncertainty.

Avoiding a reproducibility crisis in regulatory toxicology-on the fundamental role of ring trials.

The ongoing transition from chemical hazard and risk assessment based on animal studies to assessment relying mostly on non-animal data, requires a multitude of novel experimental methods, and this means that guidance on the validation and standardisation of test methods intended for international applicability and acceptance, needs to be updated. These so-called new approach methodologies (NAMs) must be applicable to the chemical regulatory domain and provide reliable data which are relevant to hazard and risk assessment. Confidence in and use of NAMs will depend on their reliability and relevance, and both are thoroughly assessed by validation. Validation is, however, a time- and resource-demanding process. As updates on validation guidance are conducted, the valuable components must be kept: Reliable data are and will remain fundamental. In 2016, the scientific community was made aware of the general crisis in scientific reproducibility-validated methods must not fall into this. In this commentary, we emphasize the central importance of ring trials in the validation of experimental methods. Ring trials are sometimes considered to be a major hold-up with little value added to the validation. Here, we clarify that ring trials are indispensable to demonstrate the robustness and reproducibility of a new method. Further, that methods do fail in method transfer and ring trials due to different stumbling blocks, but these provide learnings to ensure the robustness of new methods. At the same time, we identify what it would take to perform ring trials more efficiently, and how ring trials fit into the much-needed update to the guidance on the validation of NAMs.

Applications of 3D organoids in toxicological studies: a comprehensive analysis based on bibliometrics and advances in toxicological mechanisms.

A mechanism exploration is an important part of toxicological studies. However, traditional cell and animal models can no longer meet the current needs for in-depth studies of toxicological mechanisms. The three-dimensional (3D) organoid derived from human embryonic stem cells (hESC) or induced pluripotent stem cells (hiPSC) is an ideal experimental model for the study of toxicological effects and mechanisms, which further recapitulates the human tissue microenvironment and provides a reliable method for studying complex cell-cell interactions. This article provides a comprehensive overview of the state of the 3D organoid technology in toxicological studies, including a bibliometric analysis of the existing literature and an exploration of the latest advances in toxicological mechanisms. The use of 3D organoids in toxicology research is growing rapidly, with applications in disease modeling, organ-on-chips, and drug toxicity screening being emphasized, but academic communications among countries/regions, institutions, and research scholars need to be further strengthened. Attempts to study the toxicological mechanisms of exogenous chemicals such as heavy metals, nanoparticles, drugs and organic pollutants are also increasing. It can be expected that 3D organoids can be better applied to the safety evaluation of exogenous chemicals by establishing a standardized methodology.

CEBS update: curated toxicology database with enhanced tools for data integration.

The Chemical Effects in Biological Systems database (CEBS) contains extensive toxicology study results and metadata from the Division of the National Toxicology Program (NTP) and other studies of environmental health interest. This resource grants public access to search and collate data from over 10 250 studies for 12 750 test articles (chemicals, environmental agents). CEBS has made considerable strides over the last 5 years to integrate growing internal data repositories into data warehouses and data marts to better serve the public with high quality curated datasets. This effort includes harmonizing legacy terms and metadata to current standards, mapping test articles to external identifiers, and aligning terms to OBO (Open Biological and Biomedical Ontology) Foundry ontologies. The data are made available through the CEBS Homepage (https://cebs.niehs.nih.gov/cebs/), guided search applications, flat files on FTP (file transfer protocol), and APIs (application programming interface) for user access and to provide a bridge for computational tools. The user interface is intuitive with a single search bar to query keywords related to study metadata, publications, and data availability. Results are consolidated to single pages for each test article with NTP conclusions, publications, individual studies, data collections, and links to related test articles and projects available together.

Points to consider regarding the use and implementation of virtual controls in nonclinical general toxicology studies.

The replacement of a proportion of concurrent controls by virtual controls in nonclinical safety studies has gained traction over the last few years. This is supported by foundational work, encouraged by regulators, and aligned with societal expectations regarding the use of animals in research. This paper provides an overview of the points to consider for any institution on the verge of implementing this concept, with emphasis given on database creation, risks, and discipline-specific perspectives.

The database makes the poison: How the selection of datasets in QSAR models impacts toxicant prediction of higher tier endpoints.

As the United States and the European Union continue their steady march towards the acceptance of new approach methodologies (NAMs), we need to ensure that the available tools are fit for purpose. Critics will be well-positioned to caution against NAMs acceptance and adoption if the tools turn out to be inadequate. In this paper, we focus on Quantitative Structure Activity-Relationship (QSAR) models and highlight how the training database affects quality and performance of these models. Our analysis goes to the point of asking, "are the endpoints extracted from the experimental studies in the database trustworthy, or are they false negatives/positives themselves?" We also discuss the impacts of chemistry on QSAR models, including issues with 2-D structure analyses when dealing with isomers, metabolism, and toxicokinetics. We close our analysis with a discussion of challenges associated with translational toxicology, specifically the lack of adverse outcome pathways/adverse outcome pathway networks (AOPs/AOPNs) for many higher tier endpoints. We recognize that it takes a collaborate effort to build better and higher quality QSAR models especially for higher tier toxicological endpoints. Hence, it is critical to bring toxicologists, statisticians, and machine learning specialists together to discuss and solve these challenges to get relevant predictions.

Recovery Animals in Toxicology Studies: An Innovation and Quality Consortium Perspective on Best Practices With Case Study Examples.

The inclusion of recovery animals in nonclinical safety studies that support clinical trials is undertaken with a wide diversity of approaches even while operating under harmonized regulatory guidance. While empirical evaluation of reversibility may enhance the overall nonclinical risk assessment, there are often overlooked opportunities to reduce recovery animal use by leveraging robust scientific and regulatory information. In the past, there were several attempts to benchmark recovery practices; however, recommendations have not been consistently applied across the pharmaceutical industry. A working group (WG) sponsored by the 3Rs Translational and Predictive Sciences Leadership Group of the IQ Consortium conducted a survey of current industry practice related to the evaluation of reversibility/recovery in repeat dose toxicity studies. Discussion among the WG representatives included member company strategies and case studies that highlight challenges and opportunities for continuous refinements in the use of recovery animals. The case studies presented in this paper demonstrate increasing alignment with the Society of Toxicologic Pathology recommendations (2013) towards (1) excluding recovery phase cohorts by default (include only when scientifically justified), (2) minimizing the number of recovery groups (e.g., control and one dose level), and (3) excluding controls in the recovery cohort by leveraging external and/or dosing phase data. Recovery group exclusion and decisions regarding the timing of reversibility evaluation may be driven by indication, modality, and/or other scientific or strategic factors using a weight of evidence approach. The results and recommendations discussed present opportunities to further decrease animal use without impacting the quality of human risk assessment.

Letter re: Regulatory toxicology approaches in workplaces of Iran.

The objective of establishing occupational exposure limits (OELs) is to utilize them as a risk management tool, ensuring the protection of workers' health and well-being from hazardous substances present in the workplace. To regulate and develop an OEL, it is essential to conduct toxicological studies on both animals and humans, to determine the dose-response relationship for each chemical compound, and to determine whether the dose-response relationship is linear or non-linear. Because the OELs suggested by different organizations or countries are just the result of their scientific methods, knowledge, and judgment, this does not confirm the applicability in other countries. Therefore, it is not scientific and logical to imitate the permissible limits recommended in Western countries. In most Western Asian nations, there is a significant difference in the suggested OEL levels between the reference organizations, and in assessing and managing a specific situation's risk, using any of the proposed OELs can lead to contradictory results. Suggestions for the development and improvement of the basics of determining the OELs for chemical pollution in West Asian countries have been made.

Zebrafish Models in Toxicology and Disease Studies.

Danio rerio is a small tropical freshwater fish, also known as Brachydanio rerio and commonly referred to as zebrafish, described for the first time in 1822 by Francis Hamilton in the Ganges River but widespread throughout the entire Great Himalayan region of Southeast Asia [...].

ToxDAR: A Workflow Software for Analyzing Toxicologically Relevant Proteomic and Transcriptomic Data, from Data Preparation to Toxicological Mechanism Elucidation.

Exploration of toxicological mechanisms is imperative for the assessment of potential adverse reactions to chemicals and pharmaceutical agents, the engineering of safer compounds, and the preservation of public health. It forms the foundation of drug development and disease treatment. High-throughput proteomics and transcriptomics can accurately capture the body's response to toxins and have become key tools for revealing complex toxicological mechanisms. Recently, a vast amount of omics data related to toxicological mechanisms have been accumulated. However, analyzing and utilizing these data remains a major challenge for researchers, especially as there is a lack of a knowledge-based analysis system to identify relevant biological pathways associated with toxicity from the data and to establish connections between omics data and existing toxicological knowledge. To address this, we have developed ToxDAR, a workflow-oriented R package for preprocessing and analyzing toxicological multi-omics data. ToxDAR integrates packages like NormExpression, DESeq2, and igraph, and utilizes R functions such as prcomp and phyper. It supports data preparation, quality control, differential expression analysis, functional analysis, and network analysis. ToxDAR's architecture also includes a knowledge graph with five major categories of mechanism-related biological entities and details fifteen types of interactions among them, providing comprehensive knowledge annotation for omics data analysis results. As a case study, we used ToxDAR to analyze a transcriptomic dataset on the toxicology of triphenyl phosphate (TPP). The results indicate that TPP may impair thyroid function by activating thyroid hormone receptor ß (THRB), impacting pathways related to programmed cell death and inflammation. As a workflow-oriented data analysis tool, ToxDAR is expected to be crucial for understanding toxic mechanisms from omics data, discovering new therapeutic targets, and evaluating chemical safety.

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.