The Potential of Molecular Docking for Predictive Toxicology.

Molecular modeling techniques are widely used in medicinal chemistry for the study of biological targets, the rational design of new drugs, or the investigation of their mechanism of action.They are also applied in toxicology to identify chemical potential harmful effects.Molecular docking is a computational technique to predict the ligand binding mode and evaluate the interaction energy with a biological target.This chapter describes a computational workflow to predict possible endocrine disruptors on peroxisome proliferator-activated receptor alpha (PPARα), a nuclear receptor involved in glucose and lipid metabolism. The analyzed compounds are food contact chemicals, natural or synthetic substances intentionally added to food or released from the package or during production or technological processes.

Molecular Similarity in Predictive Toxicology with a Focus on the q-RASAR Technique.

The concept of similarity is an important aspect in various in silico-based prediction approaches. Most of these approaches follow the basic similarity property principle that states that two or more compounds having a high level of similarity are expected to exert similar biological activity or physicochemical property. Although in some cases this principle fails to predict the biological activity or property efficiently for certain compounds, it is applicable to most of the compounds in a given dataset. With the emerging need to efficiently fill data gaps in the regulatory context, Read-Across (RA), a similarity-based approach, has gained popularity, since this is not a statistical approach like QSAR, which requires a sizeable amount of data points to train a meaningful model. The basic idea behind Read-Across is the identification of the close source neighbors, and based on the similarity considerations, predictions are made for the query compound. Although RA is originally an unsupervised prediction method, recent efforts for quantitative Read-Across (qRA) have introduced supervised similarity-based weightage for quantitative predictions. RA is a useful tool in predictive toxicology, but one of its important drawbacks is the lack of interpretability of the features (especially for q-RA) used to generate the Read-Across-based predictions. To bridge this gap, a novel quantitative Read-Across Structure-Activity Relationship (q-RASAR) approach has recently been proposed, which combines the concepts of QSAR and Read-Across, generating statistically reliable and predictive models using similarity and error-based descriptors. The q-RASAR models are simple and interpretable and can be efficiently used to identify not only the essential features but also the nature of the source and query compounds. In this chapter, we have discussed the concepts and various studies on RA, q-RA, and q-RASAR along with some of the tools available from different research groups.

Integration of QSAR and NAM in the Read-Across Process for an Effective and Relevant Toxicological Assessment.

Read-Across (RAx) serves as a strategy to fill a data gap in the toxicological profile of a substance (target) using existing information on similar source substances. The principle is applied also to a category of substances for which similarity may follow a regular trend. Demonstration of similarity is not trivial and requires the analysis of different steps, starting from the precise analytical characterization of both target and source substances and including the analysis of the impact that each minor difference can have on the final outcome. Application of QSARs and performing new experimental tests within the new approach methodologies (NAMs) is necessary to increase confidence in the final prediction and reduce the uncertainty.

Computational Toxicology Methods in Chemical Library Design and High-Throughput Screening Hit Validation.

The discovery of molecular toxicity in a clinical drug candidate can have a significant impact on both the cost and timeline of the drug discovery process. Early identification of potentially toxic compounds during screening library preparation or, alternatively, during the hit validation process is critical to ensure that valuable time and resources are not spent pursuing compounds that may possess a high propensity for human toxicity. This report focuses on the application of computational molecular filters, applied either pre- or post-screening, to identify and remove known reactive and/or potentially toxic compounds from consideration in drug discovery campaigns.

PFAS-Biomolecule Interactions: Case Study Using Asclepios Nodes and Automated Workflows in KNIME for Drug Discovery and Toxicology.

The Asclepios suite of KNIME nodes represents an innovative solution for conducting cheminformatics and computational chemistry tasks, specifically tailored for applications in drug discovery and computational toxicology. This suite has been developed using open-source and publicly accessible software. In this chapter, we introduce and explore the Asclepios suite through the lens of a case study. This case study revolves around investigating the interactions between per- and polyfluorinated alkyl substances (PFAS) and biomolecules, such as nuclear receptors. The objective is to characterize the potential toxicity of PFAS and gain insights into their chemical mode of action at the molecular level. The Asclepios KNIME nodes have been designed as versatile tools capable of addressing a wide range of computational toxicology challenges. Furthermore, they can be adapted and customized to accomodate the specific needs of individual users, spanning various domains such as nanoinformatics, biomedical research, and other related applications. This chapter provides an in-depth examination of the technical underpinnings and foundations of these tools. It is accompanied by a practical case study that demonstrates the utilization of Asclepios nodes in a computational toxicology investigation. This showcases the extendable functionalities that can be applied in diverse computational chemistry contexts. By the end of this chapter, we aim for readers to have a comprehensive understanding of the effectiveness of the Asclepios node functions. These functions hold significant potential for enhancing a wide spectrum of cheminformatics applications.

Bayesian statistical concepts with examples from rodent toxicology studies.

The theory and practice of statistics comprises two main schools of thought: frequentist statistics and Bayesian statistics. Frequentist methods are most commonly used to analyze animal-based laboratory data, while Bayesian statistical methods have been implemented less widely and may be relatively unfamiliar to practitioners in experimental science. This paper provides a high-level overview of Bayesian statistics and how they compare with frequentist methods. Using examples in rodent toxicity research, we argue that Bayesian methods have much to offer laboratory animal researchers. We advocate for increased attention to and adoption of Bayesian methods in laboratory animal research. Bayesian statistical theory, methods, software, and education have advanced significantly in the last 30 years, making these tools more accessible than ever.

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.

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 [...].

Unlocking the Potential of Clustering and Classification Approaches: Navigating Supervised and Unsupervised Chemical Similarity.

BACKGROUND: The field of toxicology has witnessed substantial advancements in recent years, particularly with the adoption of new approach methodologies (NAMs) to understand and predict chemical toxicity. Class-based methods such as clustering and classification are key to NAMs development and application, aiding the understanding of hazard and risk concerns associated with groups of chemicals without additional laboratory work. Advances in computational chemistry, data generation and availability, and machine learning algorithms represent important opportunities for continued improvement of these techniques to optimize their utility for specific regulatory and research purposes. However, due to their intricacy, deep understanding and careful selection are imperative to align the adequate methods with their intended applications. OBJECTIVES: This commentary aims to deepen the understanding of class-based approaches by elucidating the pivotal role of chemical similarity (structural and biological) in clustering and classification approaches (CCAs). It addresses the dichotomy between general end point-agnostic similarity, often entailing unsupervised analysis, and end point-specific similarity necessitating supervised learning. The goal is to highlight the nuances of these approaches, their applications, and common misuses. DISCUSSION: Understanding similarity is pivotal in toxicological research involving CCAs. The effectiveness of these approaches depends on the right definition and measure of similarity, which varies based on context and objectives of the study. This choice is influenced by how chemical structures are represented and the respective labels indicating biological activity, if applicable. The distinction between unsupervised clustering and supervised classification methods is vital, requiring the use of end point-agnostic vs. end point-specific similarity definition. Separate use or combination of these methods requires careful consideration to prevent bias and ensure relevance for the goal of the study. Unsupervised methods use end point-agnostic similarity measures to uncover general structural patterns and relationships, aiding hypothesis generation and facilitating exploration of datasets without the need for predefined labels or explicit guidance. Conversely, supervised techniques demand end point-specific similarity to group chemicals into predefined classes or to train classification models, allowing accurate predictions for new chemicals. Misuse can arise when unsupervised methods are applied to end point-specific contexts, like analog selection in read-across, leading to erroneous conclusions. This commentary provides insights into the significance of similarity and its role in supervised classification and unsupervised clustering approaches. https://doi.org/10.1289/EHP14001.

Network toxicological and molecular docking to investigate the mechanisms of toxicity of agricultural chemical Thiabendazole.

Food safety is closely linked to human health. Thiabendazole is widely used as a fungicide and deodorant on agricultural products like vegetables and fruits to prevent fungal infections during transport and storage. This study aims to investigate the toxicity and potential mechanisms of Thiabendazole using novel network toxicology and molecular docking techniques. First, the ADMETlab2.0 and ADMETsar databases, along with literature, predicted Thiabendazole's potential to induce cancer and liver damage. Disease target libraries were constructed using GeneCards and TCMIP databases, while Thiabendazole target libraries were constructed using Swiss Target Prediction and TCMIP databases. The Venn database identified potential targets associated with Thiabendazole-induced cancer and liver injury. Protein-protein interaction (PPI) networks were derived from the STRING database, and gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathways were obtained from the DAVID database. Molecular docking assessed the binding affinity between Thiabendazole and core targets. The study revealed 29 potential targets for Thiabendazole-induced cancer and 30 potential targets for liver injury. PPI identified 5 core targets for Thiabendazole-induced cancers and 4 core targets for induced liver injury. KEGG analysis indicated that Thiabendazole might induce gastric and prostate cancer via cyclin-dependent kinase 2 (CDK2) and epidermal growth factor receptor (EGFR) targets, and liver injury through the same targets, with the p53 signaling pathway being central. GO analysis indicated that Thiabendazole-induced cancers and liver injuries were related to mitotic cell cycle G2/M transition and DNA replication. Molecular docking showed stable binding of Thiabendazole with core targets including CDK1, CDK2, EGFR, and checkpoint kinase 1 (CHEK1). These findings suggest Thiabendazole may affect the G2/M transition of the mitotic cell cycle through the p53 signaling pathway, potentially inducing cancer and liver injury. This study provides a theoretical basis for understanding the potential molecular mechanisms underlying Thiabendazole toxicity, aiding in the prevention and treatment of related diseases. Additionally, the network toxicology approach accelerates the elucidation of toxic pathways for uncharacterized agricultural chemicals.