A framework for categorizing sources of uncertainty in in silico toxicology methods: Considerations for chemical toxicity predictions.

Improving regulatory confidence and acceptance of in silico toxicology methods for chemical risk assessment requires assessment of associated uncertainties. Therefore, there is a need to identify and systematically categorize sources of uncertainty relevant to the methods and their predictions. In the present study, we analyzed studies that have characterized sources of uncertainty across commonly applied in silico toxicology methods.

The evaluation of skin sensitization potential of the UVCB substance diisopentyl phthalate by in silico and in vitro methods.

Diisopentyl phthalate (DiPeP) is primarily used as a plasticizer or additive within the production of polyvinyl chloride (PVC), and has many additional industrial applications. Its metabolites were recently found in urinary samples of pregnant women; thus, this substance is of concern as relates to human exposure. Depending upon the nature of the alcohol used in its synthesis, DiPeP may exist either as a mixture consisting of several branched positional isomers, or as a single defined structure.

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.

Development of an adverse outcome pathway network for nephrotoxicity.

Adverse outcome pathways (AOPs) were introduced in modern toxicology to provide evidence-based representations of the events and processes involved in the progression of toxicological effects across varying levels of the biological organisation to better facilitate the safety assessment of chemicals. AOPs offer an opportunity to address knowledge gaps and help to identify novel therapeutic targets. They also aid in the selection and development of existing and new in vitro and in silico test methods for hazard identification and risk assessment of chemical compounds.

Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project.

Quantitative structure-activity relationship (QSAR) models are powerful in silico tools for predicting the mutagenicity of unstable compounds, impurities and metabolites that are difficult to examine using the Ames test. Ideally, Ames/QSAR models for regulatory use should demonstrate high sensitivity, low false-negative rate and wide coverage of chemical space. To promote superior model development, the Division of Genetics and Mutagenesis, National Institute of Health Sciences, Japan (DGM/NIHS), conducted the Second Ames/QSAR International Challenge Project (2020-2022) as a successor to the First Project (2014-2017), with 21 teams from 11 countries participating.

Update of the Cancer Potency Database (CPDB) to enable derivations of Thresholds of Toxicological Concern (TTC) for cancer potency.

The purpose of this study was to update the existing Cancer Potency Database (CPDB) in order to support the development of a dataset of compounds, with associated points of departure (PoDs), to enable a review and update of currently applied values for the Threshold of Toxicological Concern (TTC) for cancer endpoints. This update of the current CPDB, last reviewed in 2012, includes the addition of new data (44 compounds and 158 studies leading to additional 359 dose-response curves). Strict inclusion criteria were established and applied to select compounds and studies with relevant cancer potency data.

Guidance for good practice in the application of machine learning in development of toxicological quantitative structure-activity relationships (QSARs).

Recent years have seen a substantial growth in the adoption of machine learning approaches for the purposes of quantitative structure-activity relationship (QSAR) development. Such a trend has coincided with desire to see a shifting in the focus of methodology employed within chemical safety assessment: away from traditional reliance upon animal-intensive in vivo protocols, and towards increased application of in silico (or computational) predictive toxicology. With QSAR central amongst techniques applied in this area, the emergence of algorithms trained through machine learning with the objective of toxicity estimation has, quite naturally, arisen.

Making in silico predictive models for toxicology FAIR.

In silico predictive models for toxicology include quantitative structure-activity relationship (QSAR) and physiologically based kinetic (PBK) approaches to predict physico-chemical and ADME properties, toxicological effects and internal exposure. Such models are used to fill data gaps as part of chemical risk assessment. There is a growing need to ensure in silico predictive models for toxicology are available for use and that they are reproducible.

Effects of a postbiotic, with and without a saponin-based product, on turkey performance.

Modern poultry production relies on an ability to prevent and mitigate challenges to bird health, while maintaining a productive bird. A number of different classes of biologics-based feed additives exist, and many have been tested individually for their impacts on poultry health and performance. Fewer studies have examined the combinations of different classes of products.

Construction of an In Silico Structural Profiling Tool Facilitating Mechanistically Grounded Classification of Aquatic Toxicants.

The performance of chemical safety assessment within the domain of environmental toxicology is often impeded by a shortfall of appropriate experimental data describing potential hazards across the many compounds in regular industrial use. In silico schemes for assigning aquatic-relevant modes or mechanisms of toxic action to substances, based solely on consideration of chemical structure, have seen widespread employment─including those of Verhaar, Russom, and later Bauer (MechoA). Recently, development of a further system was reported by Sapounidou, which, in common with MechoA, seeks to ground its classifications in understanding and appreciation of molecular initiating events.

A scheme to evaluate structural alerts to predict toxicity - Assessing confidence by characterising uncertainties.

Structure-activity relationships (SARs) in toxicology have enabled the formation of structural rules which, when coded as structural alerts, are essential tools in in silico toxicology. Whilst other in silico methods have approaches for their evaluation, there is no formal process to assess the confidence that may be associated with a structural alert. This investigation proposes twelve criteria to assess the uncertainty associated with structural alerts, allowing for an assessment of confidence.

The use of Bayesian methodology in the development and validation of a tiered assessment approach towards prediction of rat acute oral toxicity.

There exists consensus that the traditional means by which safety of chemicals is assessed-namely through reliance upon apical outcomes obtained following in vivo testing-is increasingly unfit for purpose. Whilst efforts in development of suitable alternatives continue, few have achieved levels of robustness required for regulatory acceptance. An array of "new approach methodologies" (NAM) for determining toxic effect, spanning in vitro and in silico spheres, have by now emerged.

A Systematic Review of Published Physiologically-based Kinetic Models and an Assessment of their Chemical Space Coverage.

Across multiple sectors, including food, cosmetics and pharmaceutical industries, there is a need to predict the potential effects of xenobiotics. These effects are determined by the intrinsic ability of the substance, or its derivatives, to interact with the biological system, and its concentration-time profile at the target site. Physiologically-based kinetic (PBK) models can predict organ-level concentration-time profiles, however, the models are time and resource intensive to generate .

Derivation, characterisation and analysis of an adverse outcome pathway network for human hepatotoxicity.

Adverse outcome pathways (AOPs) and their networks are important tools for the development of mechanistically based non-animal testing approaches, such as in vitro and/or in silico assays, to assess toxicity induced by chemicals. In the present study, an AOP network connecting 14 linear AOPs related to human hepatotoxicity, currently available in the AOP-Wiki, was derived according to established criteria. The derived AOP network was characterised and analysed with regard to its structure and topological features.

Determination of "fitness-for-purpose" of quantitative structure-activity relationship (QSAR) models to predict (eco-)toxicological endpoints for regulatory use.

In silico models are used to predict toxicity and molecular properties in chemical safety assessment, gaining widespread regulatory use under a number of legislations globally. This study has rationalised previously published criteria to evaluate quantitative structure-activity relationships (QSARs) in terms of their uncertainty, variability and potential areas of bias, into ten assessment components, or higher level groupings. The components have been mapped onto specific regulatory uses (i.

New framework for a non-animal approach adequately assures the safety of cosmetic ingredients - A case study on caffeine.

This case study on the model substance caffeine demonstrates the viability of a 10-step read-across (RAX) framework in practice. New approach methodologies (NAM), including RAX and physiologically-based kinetic (PBK) modelling were used to assess the consumer safety of caffeine. Appropriate animal systemic toxicity data were used from the most relevant RAX analogue while assuming that no suitable animal toxicity data were available for caffeine.

Development of an Enhanced Mechanistically Driven Mode of Action Classification Scheme for Adverse Effects on Environmental Species.

This study developed a novel classification scheme to assign chemicals to a verifiable mechanism of (eco-)toxicological action to allow for grouping, read-across, and model generation. The new classification scheme unifies and extends existing schemes and has, at its heart, direct reference to molecular initiating events (MIEs) promoting adverse outcomes. The scheme is based on three broad domains of toxic action representing nonspecific toxicity (e.

Incorporating lines of evidence from New Approach Methodologies (NAMs) to reduce uncertainties in a category based read-across: A case study for repeated dose toxicity.

A group of triazole compounds was selected to investigate the confidence that may be associated with read-across of a complex data gap: repeated dose toxicity. The read-across was evaluated using Assessment Elements (AEs) from the European Chemicals Agency's (ECHA's) Read-Across Assessment Framework (RAAF), alongside appraisal of associated uncertainties. Following an initial read-across based on chemical structure and properties, uncertainties were reduced by the integration of data streams such as those from New Approach Methodologies (NAM) and other existing data.

A Robust, Mechanistically Based Structural Profiler for Hepatic Cholestasis.

Owing to the primary role which it holds within metabolism of xenobiotics, the liver stands at heightened risk of exposure to, and injury from, potentially hazardous substances. A principal manifestation of liver dysfunction is cholestasis-the impairment of physiological bile circulation from its point of origin within the organ to the site of action in the small intestine. The capacity for early identification of compounds liable to exert cholestatic effects is of particular utility within the field of pharmaceutical development, where contribution toward candidate attrition is great.

Exploring the Potential of ToxCast Data in Supporting Read-Across for Evaluation of Food Chemical Safety.

The intention of this study was to determine the utility of high-throughput screening (HTS) data, as exemplified by ToxCast and Tox21, for application in toxicological read-across in food-relevant chemicals. Key questions were addressed on the extent to which the HTS data could provide information enabling (1) the elucidation of underlying bioactivities associated with apical toxicological outcomes, (2) the closing of existing toxicological data gaps, and (3) the definition of the boundaries of chemical space across which bioactivity could reliably be extrapolated. Results revealed that many biological targets apparently activated within the chemical groupings lack, at this time, validated toxicity pathway associations.

In Silico Identification of Chemicals Capable of Binding to the Ecdysone Receptor.

The process of molting, known alternatively as ecdysis, is a feature integral in the life cycles of species across the arthropod phylum. Regulation occurs as a function of the interaction of ecdysteroid hormones with the arthropod nuclear ecdysone receptor-a process preceding the triggering of a series of downstream events constituting an endocrine signaling pathway highly conserved throughout environmentally prevalent insect, crustacean, and myriapod organisms. Inappropriate ecdysone receptor binding and activation forms the essential molecular initiating event within possible adverse outcome pathways relating abnormal molting to mortality in arthropods.

Potential of ToxCast Data in the Safety Assessment of Food Chemicals.

Tox21 and ToxCast are high-throughput in vitro screening programs coordinated by the U.S. National Toxicology Program and the U.

Toxicology Data Resources to Support Read-Across and (Q)SAR.

A plethora of databases exist online that can assist in 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 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.