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This article aims to provide a comprehensive critical, yet readable, review of general interest to the chemistry community on molecular similarity as applied to chemical informatics and predictive modeling with a special focus on read-across (RA) and read-across structure–activity relationships (RASAR). Molecular similarity-based computational tools, such as quantitative structure–activity relationships (QSARs) and RA, are routinely used to fill the data gaps for a wide range of properties including toxicity endpoints for regulatory purposes. This review will explore the background of RA starting from how structural information has been used through to how other similarity contexts such as physicochemical, absorption, distribution, metabolism, and elimination (ADME) properties, and biological aspects are being characterized. More recent developments of RA's integration with QSAR have resulted in the emergence of novel models such as ToxRead, generalized read-across (GenRA), and quantitative RASAR (q-RASAR). Conventional QSAR techniques have been excluded from this review except where necessary for context. [ABSTRACT FROM AUTHOR]

This article explores the use of omics data, such as genomic, transcriptomic, proteomic, and metabolomic data, in chemical grouping and read-across for chemical risk assessment. It explains that traditional approaches to chemical safety rely on in vivo toxicity data, but there is a growing recognition of the need for more mechanistically based approaches that do not involve animal testing. The article introduces non-experts to the concept of grouping and read-across, discusses the value of omics technologies in this context, presents case studies, and identifies challenges and next steps for incorporating omics data into chemical grouping. It also discusses the regulatory landscape and the need for standardized reporting templates. However, it acknowledges that there are limitations to chemical grouping, as structural similarity does not always predict similar biological responses. Overall, the article highlights the potential of omics data in chemical grouping and the need for further research and implementation in this area. [Extracted from the article]

To transfer toxicological findings from model systems, e.g. animals, to humans, standardized safety factors are applied to account for intra-species and inter-species variabilities. An alternative approach would be to measure and model the actual compound-specific uncertainties. This biological concept assumes that all observed toxicities depend not only on the exposure situation (environment = E), but also on the genetic (G) background of the model (G × E). As a quantitative discipline, toxicology needs to move beyond merely qualitative G × E concepts. Research programs are required that determine the major biological variabilities affecting toxicity and categorize their relative weights and contributions. In a complementary approach, detailed case studies need to explore the role of genetic backgrounds in the adverse effects of defined chemicals. In addition, current understanding of the selection and propagation of adverse outcome pathways (AOP) in different biological environments is very limited. To improve understanding, a particular focus is required on modulatory and counter-regulatory steps. For quantitative approaches to address uncertainties, the concept of "genetic" influence needs a more precise definition. What is usually meant by this term in the context of G × E are the protein functions encoded by the genes. Besides the gene sequence, the regulation of the gene expression and function should also be accounted for. The widened concept of past and present "gene expression" influences is summarized here as Ge. Also, the concept of "environment" needs some re-consideration in situations where exposure timing (Et) is pivotal: prolonged or repeated exposure to the insult (chemical, physical, life style) affects Ge. This implies that it changes the model system. The interaction of Ge with Et might be denoted as Ge × Et. We provide here general explanations and specific examples for this concept and show how it could be applied in the context of New Approach Methodologies (NAM). [ABSTRACT FROM AUTHOR]

Developmental and adult/ageing neurotoxicity is an area needing alternative methods for chemical risk assessment. The formulation of a strategy to screen large numbers of chemicals is highly relevant due to potential exposure to compounds that may have long-term adverse health consequences on the nervous system, leading to neurodegeneration. Adverse Outcome Pathways (AOPs) provide information on relevant molecular initiating events (MIEs) and key events (KEs) that could inform the development of computational alternatives for these complex effects. We propose a screening method integrating multiple Quantitative Structure–Activity Relationship (QSAR) models. The MIEs of existing AOP networks of developmental and adult/ageing neurotoxicity were modelled to predict neurotoxicity. Random Forests were used to model each MIE. Predictions returned by single models were integrated and evaluated for their capability to predict neurotoxicity. Specifically, MIE predictions were used within various types of classifiers and compared with other reference standards (chemical descriptors and structural fingerprints) to benchmark their predictive capability. Overall, classifiers based on MIE predictions returned predictive performances comparable to those based on chemical descriptors and structural fingerprints. The integrated computational approach described here will be beneficial for large-scale screening and prioritisation of chemicals as a function of their potential to cause long-term neurotoxic effects. [ABSTRACT FROM AUTHOR]

Read-across (RAx) translates available information from well-characterized chemicals to a substance for which there is a toxicological data gap. The OECD is working on case studies to probe general applicability of RAx, and several regulations (e.g., EU-REACH) already allow this procedure to be used to waive new in vivo tests. The decision to prepare a review on the state of the art of RAx as a tool for risk assessment for regulatory purposes was taken during a workshop with international experts in Ranco, Italy in July 2018. Three major issues were identified that need optimization to allow a higher regulatory acceptance rate of the RAx procedure: (i) the definition of similarity of source and target, (ii) the translation of biological/toxicological activity of source to target in the RAx procedure, and (iii) how to deal with issues of ADME that may differ between source and target. The use of new approach methodologies (NAM) was discussed as one of the most important innovations to improve the acceptability of RAx. At present, NAM data may be used to confirm chemical and toxicological similarity. In the future, the use of NAM may be broadened to fully characterize the hazard and toxicokinetic properties of RAx compounds. Concerning available guidance, documents on Good Read-Across Practice (GRAP) and on best practices to perform and evaluate the RAx process were identified. Here, in particular, the RAx guidance, being worked out by the European Commission's H2020 project EU-ToxRisk together with many external partners with regulatory experience, is given. [ABSTRACT FROM AUTHOR]

The quantitative adverse outcome pathway (qAOP) concept is gaining interest due to its potential regulatory applications in chemical risk assessment. Even though an increasing number of qAOP models are being proposed as computational predictive tools, there is no framework to guide their development and assessment. As such, the objectives of this review were to: (i) analyse the definitions of qAOPs published in the scientific literature, (ii) define a set of common features of existing qAOP models derived from the published definitions, and (iii) identify and assess the existing published qAOP models and associated software tools. As a result, five probabilistic qAOPs and ten mechanistic qAOPs were evaluated against the common features. The review offers an overview of how the qAOP concept has advanced and how it can aid toxicity assessment in the future. Further efforts are required to achieve validation, harmonisation and regulatory acceptance of qAOP models. [ABSTRACT FROM AUTHOR]

Recent years have seen the emergence into circulation of a growing array of novel psychoactive substances (NPS). Knowledge of the pharmacological profiles and risk liability of these compounds is typically very scarce. Development of chemoinformatic tools enabling prediction of properties within uncharacterised analogues has potential be of particular use. In order to facilitate this, compilation of a chemical inventory comprising known NPS is a necessity. Sourcing a variety of published governmental and analytical reports, a dataset composed of 690 distinct acknowledged NPS, complete with defined chemical structures, has been constructed. This is supplemented by a complementary series of 155 established psychoactive drugs of abuse (EPDA). Classification was performed in accordance with their key molecular structural features, subjective effect profiles and pharmacological mechanisms of action. In excess of forty chemical groupings, spanning seven subjective effect categories and six broad mechanisms of pharmacological action, were identified. Co-occurrence of NPS and EPDA within specific classes was common, showcasing inherent scope both for chemical read-across and for the derivation of structural alerts. [ABSTRACT FROM AUTHOR]

A plethora of databases exist online that can assist in in silico chemical or drug safety assessment. However, a systematic review and grouping of databases, based on purpose and information content, consolidated in a single source, has been lacking. To resolve this issue, this review provides a comprehensive listing of the key in silico data resources relevant to: chemical identity and properties, drug action, toxicology (including nano-material toxicity), exposure, omics, pathways, Absorption, Distribution, Metabolism and Elimination (ADME) properties, clinical trials, pharmacovigilance, patents-related databases, biological (genes, enzymes, proteins, other macromolecules etc.) databases, protein-protein interactions (PPIs), environmental exposure related, and finally databases relating to animal alternatives in support of 3Rs policies. More than nine hundred databases were identified and reviewed against criteria relating to accessibility, data coverage, interoperability or application programming interface (API), appropriate identifiers, types of in vitro, in vivo ,-clinical or other data recorded and suitability for modelling, read-across, or similarity searching. This review also specifically addresses the need for solutions for mapping and integration of databases into a common platform for better translatability of preclinical data to clinical data. [ABSTRACT FROM AUTHOR]

Investigative toxicology describes the de-risking and mechanistic elucidation of toxicities, supporting early safety decisions in the pharmaceutical industry. Recently, investigative toxicology has contributed to a shift in pharmaceutical toxicology, from a descriptive to an evidence-based, mechanistic discipline. This was triggered by high costs and low throughput of Good Laboratory Practice in vivo studies, and increasing demands for adherence to the 3R (Replacement, Reduction, and Refinement) principles of animal welfare. Outside the boundaries of regulatory toxicology, investigative toxicology has the flexibility to embrace new technologies, enhancing translational steps from in silico, in vitro to in vivo mechanistic understanding to eventually predict human response. One major goal of investigative toxicology is to improve preclinical decisions, which coincides with the concept of animal-free safety testing. Currently, compounds under preclinical development are being discarded owing to the use of inappropriate animal models. Progress in investigative toxicology could lead to humanized in vitro test systems and the development of medicines less reliant on animal tests. To advance this field, a group of 14 European-based leaders from the pharmaceutical industry founded the Investigative Toxicology Leaders Forum (ITLF), an open, non-exclusive, and pre-competitive group that shares knowledge and experience. The ITLF collaborated with the Centre for Alternatives to Animal Testing Europe (CAAT-Europe) to organize an "Investigative Toxicology Think Tank", which aimed to enhance interaction with experts from academia and regulatory bodies in the field. Summarizing the topics and discussion of the workshop, this article highlights investigative toxicology's position by identifying key challenges and perspectives. [ABSTRACT FROM AUTHOR]

The current state of the art of (Quantitative) Structure–Activity Relationships ((Q)SARs) to predict environmental toxicity is assessed along with recommendations to develop these models further. The acute toxicity of compounds acting by the non-polar narcotic mechanism of action can be well predicted, however other approaches, including read-across, may be required for compounds acting by specific mechanisms of action. The chronic toxicity of compounds to environmental species is more difficult to predict from (Q)SARs, with robust data sets and more mechanistic information required. In addition, the toxicity of mixtures is little addressed by (Q)SAR approaches. Developments in environmental toxicology including Adverse Outcome Pathways (AOPs) and omics responses should be utilised to develop better, more mechanistically relevant, (Q)SAR models. [ABSTRACT FROM AUTHOR]

Grouping of substances and utilizing read-across of data within those groups represents an important data gap filling technique for chemical safety assessments. Categories/analogue groups are typically developed based on structural similarity and, increasingly often, also on mechanistic (biological) similarity. While read-across can play a key role in complying with legislation such as the European REACH regulation, the lack of consensus regarding the extent and type of evidence necessary to support it often hampers its successful application and acceptance by regulatory authorities. Despite a potentially broad user community, expertise is still concentrated across a handful of organizations and individuals. In order to facilitate the effective use of read-across, this document presents the state of the art, summarizes insights learned from reviewing ECHA published decisions regarding the relative successes/pitfalls surrounding read-across under REACH, and compiles the relevant activities and guidance documents. Special emphasis is given to the available existing tools and approaches, an analysis of ECHA's published final decisions associated with all levels of compliance checks and testing proposals, the consideration and expression of uncertainty, the use of biological support data, and the impact of the ECHA Read-Across Assessment Framework (RAAF) published in 2015. [ABSTRACT FROM AUTHOR]

The development of adverse outcome pathways (AOPs) is becoming a key component of twenty-first century toxicology. AOPs provide a conceptual framework that links the molecular initiating event to an adverse outcome through organized toxicological knowledge, bridging the gap from chemistry to toxicological effect. As nuclear receptors (NRs) play essential roles for many physiological processes within the body, they are used regularly as drug targets for therapies to treat many diseases including diabetes, cancer and neurodegenerative diseases. Due to the heightened development of NR ligands, there is increased need for the identification of related AOPs to facilitate their risk assessment. Many NR ligands have been linked specifically to steatosis. This article reviews and summarizes the role of NR and their importance with links between NR examined to identify plausible putative AOPs. The following NRs are shown to induce hepatic steatosis upon ligand binding: aryl hydrocarbon receptor, constitutive androstane receptor, oestrogen receptor, glucocorticoid receptor, farnesoid X receptor, liver X receptor, peroxisome proliferator-activated receptor, pregnane X receptor and the retinoic acid receptor. A preliminary, putative AOP was formed for NR binding linked to hepatic steatosis as the adverse outcome. [ABSTRACT FROM AUTHOR]

Adverse Outcome Pathways (AOPs) provide an opportunity to develop new and more accurate safety assessment processes for drugs and other chemicals, and may ultimately play an important role in regulatory decision making. Not only can the development and application of AOPs pave the way for the development of improved evidence-based approaches for hazard and risk assessment, there is also the promise of a significant impact on animal welfare, with a reduced reliance on animal-based methods. The establishment of a useable and coherent knowledge framework under which AOPs will be developed and applied has been a first critical step towards realizing this opportunity. This article explores how the development of AOPs under this framework, and their application in practice, could benefit the science and practice of safety assessment, while in parallel stimulating a move away from traditional methods towards an increased acceptance of non-animal approaches. We discuss here the key areas where current, and future initiatives should be focused to enable the translation of AOPs into routine chemical safety assessment, and lasting 3Rs benefits. © 2015 The Authors. Journal of Applied Toxicology published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

SEURAT-1 is a European public-private research consortium that is working towards animal-free testing of chemical compounds and the highest level of consumer protection. A research strategy was formulated based on the guiding principle to adopt a toxicological mode-of-action framework to describe how any substance may adversely affect human health. The proof of the initiative will be in demonstrating the applicability of the concepts on which SEURAT-1 is built on three levels: (i) Theoretical prototypes for adverse outcome pathways are formulated based on knowledge already available in the scientific literature on investigating the toxicological modes-of-action leading to adverse outcomes (addressing mainly liver toxicity); (ii) adverse outcome pathway descriptions are used as a guide for the formulation of case studies to further elucidate the theoretical model and to develop integrated testing strategies for the prediction of certain toxicological effects (i.e., those related to the adverse outcome pathway descriptions); (iii) further case studies target the application of knowledge gained within SEURAT-1 in the context of safety assessment. The ultimate goal would be to perform ab initio predictions based on a complete understanding of toxicological mechanisms. In the near-term, it is more realistic that data from innovative testing methods will support read-across arguments. Both scenarios are addressed with case studies for improved safety assessment. A conceptual framework for a rational integrated assessment strategy emerged from designing the case studies and is discussed in the context of international developments focusing on alternative approaches for evaluating chemicals using the new 21st century tools for toxicity testing. [ABSTRACT FROM AUTHOR]

Read-across is a data gap filling technique used within category and analogue approaches. It has been utilized as an alternative approach to address information requirements under various past and present regulatory programs such as the OECD High Production Volume Programme as well as the EU's Registration, Evaluation, Authorisation and restriction of CHemicals (REACH) regulation. Although read-across raises a number of expectations, many misconceptions still remain around what it truly represents; how to address its associated justification in a robust and scientifically credible manner; what challenges/issues exist in terms of its application and acceptance; and what future efforts are needed to resolve them. In terms of future enhancements, read-across is likely to embrace more biologicallyorientated approaches consistent with the Toxicity in the 21st Century vision (Tox-21c). This Food for Thought article, which is notably not a consensus report, aims to discuss a number of these aspects and, in doing so, to raise awareness of the ongoing efforts and activities to enhance read-across. It also intends to set the agenda for a CAAT read-across initiative in 2014-2015 to facilitate the proper use of this technique. [ABSTRACT FROM AUTHOR]

The comprehensive understanding of the precise mode of action and/or adverse outcome pathway (MoA/AOP) of chemicals has become a key step toward the development of a new generation of predictive toxicology tools. One of the challenges of this process is to test the feasibility of the molecular modelling approaches to explore key molecular initiating events (MIE) within the integrated strategy of MoA/AOP characterisation. The description of MoAs leading to toxicity and liver damage has been the focus of much interest. Growing evidence underlines liver PPAR? ligand-dependent activation as a key MIE in the elicitation of liver steatosis. Synthetic PPAR? full agonists are of special concern, since they may trigger a number of adverse effects not observed with partial agonists. In this study, molecular modelling was performed based on the PPAR? complexes with full agonists extracted from the Protein Data Bank. The receptor binding pocket was analysed, and the specific ligand-receptor interactions were identified for the most active ligands. A pharmacophore model was derived, and the most important pharmacophore features were outlined and characterised in relation to their specific role for PPAR? activation. The results are useful for the characterisation of the chemical space of PPAR? full agonists and could facilitate the development of preliminary filtering rules for the effective virtual ligand screening of compounds with PPAR? full agonistic activity. [ABSTRACT FROM AUTHOR]

Quality assessment (QA) requires high levels of domain-specific experience and knowledge. QA tasks for toxicological data are usually performed by human experts manually, although a number of quality evaluation schemes have been proposed in the literature. For instance, the most widely utilised Klimisch scheme1 defines four data quality categories in order to tag data instances with respect to their qualities; ToxRTool2 is an extension of the Klimisch approach aiming to increase the transparency and harmonisation of the approach. Note that the processes of QA in many other areas have been automatised by employing expert systems. Briefly, an expert system is a computer program that uses a knowledge base built upon human expertise, and an inference engine that mimics the reasoning processes of human experts to infer new statements from incoming data. In particular, expert systems have been extended to deal with the uncertainty of information by representing uncertain information (such as linguistic terms) as fuzzy sets under the framework of fuzzy set theory and performing inferences upon fuzzy sets according to fuzzy arithmetic. This paper presents an experimental fuzzy expert system for toxicological data QA which is developed on the basis of the Klimisch approach and the ToxRTool in an effort to illustrate the power of expert systems to toxicologists, and to examine if fuzzy expert systems are a viable solution for QA of toxicological data. Such direction still faces great difficulties due to the well-known common challenge of toxicological data QA that 'five toxicologists may have six opinions'. In the meantime, this challenge may offer an opportunity for expert systems because the construction and refinement of the knowledge base could be a converging process of different opinions which is of significant importance for regulatory policy making under the regulation of REACH, though a consensus may never be reached. Also, in order to facilitate the implementation of Weight of Evidence approaches and in silico modelling proposed by REACH, there is a higher appeal of numerical quality values than nominal (categorical) ones, where the proposed fuzzy expert system could help. Most importantly, the deriving processes of quality values generated in this way are fully transparent, and thus comprehensible, for final users, which is another vital point for policy making specified in REACH. Case studies have been conducted and this report not only shows the promise of the approach, but also demonstrates the difficulties of the approach and thus indicates areas for future development. [ABSTRACT FROM AUTHOR]

This study has investigated the development of quantitative structure-activity relationships (QSARs) for the toxicity to rainbow trout Onchorhyncus mykiss Walbaum of 75 organophosphorus and carbamate pesticides. The toxicity data were obtained from an openly available toxicological database and were selected to be representative of a single endpoint. A large number of physicochemical and structural descriptors were calculated for the pesticides. QSAR models were developed using multiple linear regression and partial least- squares analyses. Following the removal of a small number of outliers, predictive QSARs were developed on small numbers of mechanistically relevant descriptors. Applying mechanistic knowledge to the development of QSAR further improved predictivity. [ABSTRACT FROM AUTHOR]