Endocrine disruption (ED) has been introduced as a new classification, labelling and packaging (CLP) hazard category under Regulation (EC) No 1272/2008. Additionally, consideration of endocrine-disrupting properties and endocrine-related effects continues to be an important aspect of chemicals management under the Canadian Environmental Protection Act (CEPA) 1999 for the prioritization and hazard characterization of potential hormone disrupting substances. To support chemical prioritization and hazard assessment, this study presents a structured in silico protocol for assessing endocrine activity across the estrogen (E), androgen (A), thyroid (T), and steroidogenesis (S) (EATS) modalities. The protocol integrates (Quantitative) Structure-Activity Relationship ((Q)SAR) predictions with experimental data using a structured approach grounded in a hazard assessment framework (HAF) and defines principles for evaluating the reliability and confidence of predictions. Key endpoints and model development opportunities are identified for each modality. Two case studies are presented to demonstrate the application of the protocol. In the assessment of 4-Chloro-1-[2,2-dichloro-1-(4-chlorophenyl) ethenyl]-2-(methylsulfonyl)benzene, structurally similar analogs supported a medium-confidence assessment of estrogen and androgen activity. Whereas, in the assessment of chloroprene, uncertainties due to potential metabolic transformation limited confidence in negative assessments. These case studies illustrate how model outputs, experimental evidence, an analysis of analogs, and expert review can be integrated to produce transparent and reproducible assessments. The framework supports a weight-of-evidence (WOE) non-testing approach for identifying endocrine-active substances.

Quantitative Structure Activity Relationship (QSAR) models are widely used for genotoxicity assessment in regulatory settings. In silico profilers are a special case of models capturing mechanistic insights specific to a particular toxicological endpoint or reflecting chemistry-related attributes that may not be directly associated with a defined mechanism of toxicity. This study explores the accuracy of using such profilers as a lower tier in genotoxicity assessment to inform regulatory concerns. Relevant profilers in the OECD QSAR Toolbox are investigated using an external validation dataset derived from the MultiCASE Genotoxicity database, which contains AMES mutagenicity and in vivo micronucleus (MNT) experimental results. The MNT dataset includes the commercial in vivo MNT dataset expanded with pesticide data from regulatory documents. This analysis incorporates the use of metabolism simulations by the OECD QSAR Toolbox to assess their influence on profiler performance. The present findings show that the absence of profiler alerts correlates well with experimentally negative outcomes. However, the calculated accuracy for the MNT-related and AMES-related profilers varies considerably (41%-78% for MNT-related profilers and 62%-88% for AMES-related profilers using the full set with and without consideration of metabolism). Incorporating metabolism simulations increases accuracy by 4-6% for the full AMES-dataset, and 4-16% for the full MNT-dataset. Together, genotoxicity assessment using the Toolbox profilers should include a critical evaluation of any triggered alerts, considering the overall performance statistics of the profilers presented within this work. Results from third-party QSAR models provide critical insights to complement the expert review of any profiler positive result, as profilers alone are not recommended to be used directly for prediction purpose.

We present a simple methodology that allows the building and sharing of predictive models without compromising the confidentiality of the structures of the training series. Multiple shared models can be used to obtain ensemble models, providing better coverage of the chemical space and better predictions than the original one. This approach is demonstrated in a collaborative exercise where four pharmaceutical and chemical companies developed predictive models for the AMES mutagenicity end point and shared them to build ensemble models using logical and machine learning algorithms. The results were systematically analyzed and compared, obtaining clear benefits in predictive quality. The method has the advantage of being very simple to execute, using only open-source software, and the possibility to audit the whole process and interchange files to guarantee that no confidential information is exported from the company facilities.

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.

In silico predictive models for toxicology include quantitative structure-activity relationship (QSAR) and physi-ologically 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 repro-ducible. This paper describes how the FAIR (Findable, Accessible, Interoperable, Reusable) principles, developed for data sharing, have been applied to in silico predictive models. In particular, this investigation has focussed on how the FAIR principles could be applied to improved regulatory acceptance of predictions from such models. Eighteen principles have been developed that cover all aspects of FAIR. It is intended that FAIRification of in silico predictive models for toxicology will increase their use and acceptance.

We present a case study for afidopyropen (AF; insecticide) to characterize chronic dietary human health risk using a Risk 21-based approach. Our objective is to use a well-tested pesticidal active ingredient (AF) to show how a new approach methodology (NAM), using the kinetically-derived maximum dose (KMD) and with far less animal testing, can reliably identify a health-protective point of departure (PoD) for chronic dietary human health risk assessments (HHRA). Chronic dietary HHRA involves evaluation of both hazard and exposure information to characterize risk. Although both are important, emphasis has been placed on a checklist of required toxicological studies for hazard characterization, with human exposure information only considered after evaluation of hazard data. Most required studies are not used to define the human endpoint for HHRA. The information presented demonstrates a NAM that uses the KMD determined by saturation of a metabolic pathway, which can be used as an alternative POD. In these cases, the full toxicological database may not need to be generated. Demonstration that the compound is not genotoxic and that the KMD is protective of adverse effects in 90-day oral rat and reproductive/developmental studies is sufficient to support the use of the KMD as an alternative POD.

Today, computational tools for the prediction of the metabolite structures of xenobiotics are widely available and employed in small-molecule research. Reflecting the availability of measured data, these in silico tools are trained and validated primarily on drug metabolism data. In this work, we assessed the capacity of five leading metabolite structure predictors to represent the metabolism of agrochemicals observed in rats. More specifically, we tested the ability of SyGMa, GLORY, GLORYx, BioTransformer 3.0, and MetaTrans to correctly predict and rank the experimentally observed metabolites of a set of 85 parent compounds. We found that the models were able to recover about one to two-thirds of the experimentally observed first-generation, second-generation and third-generation metabolites, confirming their value in applications such as metabolite identification. However, precision was low for all investigated tools and did not exceed approximately 18 % for the pool of first-generation metabolites and 2 % for the pool of compounds representing the first three generations of metabolites. The variance in prediction success rates was high across the individual metabolic maps, meaning that outcomes depend strongly on the specific compound under investigation. We also found that the predictions for individual parent compounds differed strongly between the tools, particularly between those built on orthogonal technologies (e.g., rule-based and end-to-end machine learning approaches). This renders ensemble model strategies promising for improving success rates. Overall, the results of this benchmark study show that there is still considerable room for the improvement of metabolite structure predictors left. Our discussion points out several avenues to progress. The bottleneck in method development certainly has been, and will remain, for the foreseeable future, the limited quantity and quality of available measured data on small-molecule metabolism.

Thirteen pharmaceutical companies have shared and integrated preclinical and clinical data for creating computational resources that enhance translational drug safety assessment.

There are many challenges that must be overcome before in silico toxicity predictions are ripe for regulatory decision-making. Today, mandates in the United States of America and the European Union to avoid animal usage in toxicity testing is driving the need to consider alternative technologies, including Quantitative Structure Activity Relationship (QSAR) models, and read across approaches. However, when adopting new methods, it is critical that both new approach developers as well as regulatory users understand the strengths and challenges with these new approaches. In this paper, we identify potential sources of bias in machine learning methods specific to toxicity predictions, that may impact the overall performance of in silico models. We also discuss ways to mitigate these biases. Based on our experiences, the most prevalent sources of bias include class imbalance (differing numbers of "toxic" vs "nontoxic" compounds), limited numbers of chemicals within a particular chemistry, and biases within the studies that make up the database used for model building, as well as model evaluation biases. While this is already complex for repeated dose toxicity, in reproduction and developmental toxicity a further level of complexity is introduced by the need to evaluate effects on individual animal and litter basis (e.g., a hierarchal structure). We also discuss key considerations developers and regulators need to make when they use machine learning models to predict chemical safety. Our objective is for our paper to serve as a desk reference for model developers and regulators as they evaluate machine learning models and as they make decisions using these models.

Oxidative treatment methods are valuable tools for the microbial safety of drinking water. However, the reaction of oxidants with natural substances or anthropogenic contaminants present in the raw water can potentially lead to the formation of harmful transformation products (TPs). The present paper proposes a tiered approach for the risk evaluation of TPs formed from pesticide residues during drinking water treatment. First, the concentrations of pesticide residues in raw water used for drinking water production are evaluated (step 1). Substances with a predicted concentration in raw water above 0.1 µg/L proceed further to a reactivity assessment, examining the behavior in water treatment plants (step 2). Using information available in the scientific literature, prediction of structural elements in the TPs can be made and allow a worst-case assessment based on the Threshold of Toxicological Concern (TTC) (step 3). If concerns remain, experiments may be conducted to simulate water treatment (step 4). Because of their complexity and variability, experiments for the simulation of water treatment should focus on prioritized substances of potential concern. The test conditions should be realistic (i.e., close to EU-representative conditions in waterworks) and ozonation and chlorination should be combined with pre- and post-treatment steps, as is normally the case in European waterworks. As a first screening option, we propose to test the toxicity of the reaction mixture. If the treated water shows an enhanced toxicity, further experiments can be conducted to identify and quantify the major TPs (step 5). We propose to define major TPs as substances present at more than 10

Understanding the reliability and relevance of a toxicological assessment is important for gauging the overall confidence and communicating the degree of uncertainty related to it. The process involved in assessing reliability and relevance is well defined for experimental data. Similar criteria need to be established for in silico predictions, as they become increasingly more important to fill data gaps and need to be reasonably integrated as additional lines of evidence. Thus, in silico assessments could be communicated with greater confidence and in a more harmonized manner. The current work expands on previous definitions of reliability, relevance, and confidence and establishes a conceptional framework to apply those to in silico data. The approach is used in two case studies: 1) phthalic anhydride, where experimental data are readily available and 2) 4-hydroxy-3-propoxybenzaldehyde, a data poor case which relies predominantly on in silico methods, showing that reliability, relevance, and confidence of in silico assessments can be effectively communicated within Integrated approaches to testing and assessment (IATA).

Species differences in developmental toxicity can be due to varying expression of xenobiotic transporters. Hence, knowledge on the ontogeny of these transporters, especially in human, rat and rabbit, is pivotal. Two superfamilies of transporters, the ATP-binding cassette (ABC) and the solute carrier (SLC) transporters, are well known for their role in the absorption, distribution and/or elimination of xenobiotics and endogenous substances. The aim of this study was to compare the expression levels of these xenobiotic transporters in liver, kidney and placenta of man, Wistar rat and New Zealand White rabbit during pre- and postnatal development. For this purpose, qPCR experiments were performed for rat and rabbit tissues and the gene expression profiles were compared with literature data from man, rat and rabbit. Data analysis showed large differences in transporter expression in development and between species. These results can be used to better understand developmental toxicity findings in non-clinical species and their relevance for man.

BACKGROUND:In silico methods for toxicity prediction have increased significantly in recent years due to the 3Rs principle. This also applies to predicting reproductive toxicology, which is one of the most critical factors in pesticide approval. The widely used quantitative structure-activity relationship (QSAR) models use experimental toxicity data to create a model that relates experimentally observed toxicity to molecular structures to predict toxicity. Aim of the study was to evaluate the available prediction models for developmental and reproductive toxicity regarding their strengths and weaknesses in a pesticide database. METHODS:The reproductive toxicity of 315 pesticides, which have a GHS classification by ECHA, was compared with the prediction of different in silico models: VEGA, OECD (Q)SAR Toolbox, Leadscope Model Applier, and CASE Ultra by MultiCASE. RESULTS:In all models, a large proportion (up to 77%) of all pesticides were outside the chemical space of the model. Analysis of the prediction of remaining pesticides revealed a balanced accuracy of the models between 0.48 and 0.66. CONCLUSION:Overall, predictions were only meaningful in rare cases and therefore always require evaluation by an expert. The critical factors were the underlying data and determination of molecular similarity, which offer great potential for improvement.

Afidopyropen is an insecticide that acts as a transient receptor potential vanilloid subtype (TRPV) channel modulator in chordotonal organs of target insects and has been assessed for a wide range of toxicity endpoints including chronic toxicity and carcinogenicity in rats and mice. The current study evaluates the toxicokinetic properties of afidopyropen and its plasma metabolites in rats at dose levels where the pharmacokinetics (PK) are linear and nonlinear in an attempt to identify a point of inflection. Based on the results of this study and depending on the analysis method used, the kinetically derived maximum dose (KMD) is estimated to be between 2.5 and 12.5 mg/kg bw/d with linearity observed at doses below 2.5 mg/kg bw/d. A defined point of inflection could not be determined. These data demonstrate that consideration of PK is critical for improving the dose-selection in toxicity studies as well as to enhance human relevance of the interpretation of animal toxicity studies. The study also demonstrates the technical difficulty in obtaining a defined point of inflection from in vivo PK data.

Historically, identifying carcinogens has relied primarily on tumor studies in rodents, which require enormous resources in both money and time. In silico models have been developed for predicting rodent carcinogens but have not yet found general regulatory acceptance, in part due to the lack of a generally accepted protocol for performing such an assessment as well as limitations in predictive performance and scope. There remains a need for additional, improved in silico carcinogenicity models, especially ones that are more human-relevant, for use in research and regulatory decision-making. As part of an international effort to develop in silico toxicological protocols, a consortium of toxicologists, computational scientists, and regulatory scientists across several industries and governmental agencies evaluated the extent to which in silico models exist for each of the recently defined 10 key characteristics (KCs) of carcinogens. This position paper summarizes the current status of in silico tools for the assessment of each KC and identifies the data gaps that need to be addressed before a comprehensive in silico carcinogenicity protocol can be developed for regulatory use.

Afidopyropen is an insecticide that acts as a TRPV channel modulator in chordotonal organs of target insects and has been assessed for a wide range of toxicity endpoints including developmental toxicity in rats and rabbits. The GLP developmental toxicity study in rabbits did not produce evidence of maternal or fetal toxicity at the highest dose tested (32 mg/kg/day) but pharmacokinetics (PK) in pregnant rabbits in this study exhibited onset of PK nonlinearity from 5 mg/kg/day on, as measured by plasma Cmax and AUC. The NOAEL (32 mg/kg/day) is 9000X higher than maximum expected human dietary exposures to afidopyropen; the dose range where nonlinear PK were observed (5-15 mg/kg/day) is 1400-4200X higher. As nonlinearity occurred between 5 and 15 mg/kg/day, 32 mg/kg/day is concluded to be a sufficiently high dose (kinetically derived maximum dose) for a prenatal developmental toxicity study. As recognized by regulatory dose-selection guidance, onset of saturated PK is evidence of excessive biological stress to test animals rendering any effects at such doses of questionable relevance for human risk assessment. These data demonstrate that consideration of PK is critical for improving the dose-selection in developmental toxicity studies to enhance human relevance of animal toxicity studies.

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.

Afidopyropen is a novel insecticide that acts as a TRPV channel modulator in chordotonal organs of target insects. In two carcinogenicity studies with Fischer rats, an increased incidence of uterine adenocarcinomas was observed at 1000 and 3000 ppm. This finding prompted an investigation of the mechanism of the tumor formation as well as the relevance of this mechanism to humans. The mechanistic work took parallel paths: one path investigated the pharmacokinetic properties of the test substance at the doses where the tumors were found; while the second path examined the key mechanistic events that culminated in uterine adenocarcinomas. The results of the investigation indicated that the tumors only occurred at doses where excretion of test substance was saturated - indicating that homeostatic biological and/or physiological processes were overwhelmed. At the doses where these processes were overwhelmed, the test substance acted through a mechanism of dopamine agonism, triggering a cascade key events that resulted in uterine adenocarcinomas. An analysis of these mechanisms observed in rat showed that they are both quantitatively (pharmacokinetic mechanism) and qualitatively (dopamine agonism mechanism) not relevant to humans. Therefore the uterine adenocarcinomas observed in the rat associated with high doses of Afidopyropen are not expected to pose a carcinogenic risk to humans.

An increasing body of evidence demonstrates that there is bidirectional communication between the neuroendocrine and immune systems. Interaction between these systems results in a variety of outcomes, including the well documented "sickness behavior" elicited by cytokines of the immune system that can enter the brain and activate second messengers that modify neuronal activity. Crosstalk between the neuroendocrine and immune systems can also result in production of factors by the nervous and endocrine systems that alter immune cell function and subsequent modulation of immune responses against infectious agents and other pathogens. Continued exposure to molecules produced by the neuroendocrine system has also been known to increase susceptibility and/or severity of disease. Furthermore, neuroendocrine factors are thought to play a major role in gender-specific differences in development of certain disorders, including autoimmune/inflammatory diseases that have a two to tenfold higher incidence in females compared to males. Neuroendocrine factors can affect immune cells at the level of gene transcription but have also been shown to modify immune cell activity by interacting with intracellular molecules, resulting in modified ability of these cells to mount a potent immune response. In this review, we will consider various effects of the neuroendocrine system and its proteins on specific populations of immune cells and associated responses in host immunity against pathogens. We will further discuss how this modification of immune cell activity by the neuroendocrine system can contribute to susceptibility/severity of disease development.