In silico simulation of benzo[a]pyrene toxicity in the worm Caenorhabditiselegans

This study aimed to develop a toxicological screening tool using a virtual (in silico) population of Caenorhabditis elegans exposed to different concentrations of benzo[a]pyrene (BAP). The model used computational tools based on a previous study to simulate the life cycle and characteristics of C. elegans. The model was implemented in Python and adapted with fewer repetitions of simulations to reduce execution time. The toxicity function was based on in vivo data from previous studies, and the results of the model were compared with experimental results. The model showed good accuracy in reproducing the survival data of worms exposed to BAP since the lethal concentration for 50% (LC50) and the 95% confidence interval of exposed worms during 72 h was 77.92 μg/L (71.32-85.12 μg/L). The LC50 of the simulated data was 87.10 μg/L (76.13-99.85 μg/L). It was concluded that the in silico model can be a useful alternative to conventional in vivo testing methods, saving cost and time and addressing ethical concerns.

The bitter side of toxicity: A big data analysis spotted the interaction between trichothecenes and bitter receptors

The bitter taste perception evolved in human and animals to rapidly perceive and avoid potential toxic compounds. This is mediated by taste receptors type 2 (TAS2R), expressed in various tissues, which recently proved to be involved in roles beyond the bitter perception itself. With this study, the interaction between food-related toxic compounds and TAS2R46 has been investigated via computational approaches, starting with a virtual screening and moving to molecular docking and dynamics simulations. The virtual screening analysis identified trichothecolone and the trichothecenes class it belongs to, which includes mycotoxins widespread in several commodities raising food safety concerns, as possible TAS2R46 binders. Molecular docking and dynamics simulations were performed to further explore the trichotecenes-TAS2R46 interaction. The results indicated that deoxynivalenol and its 15-acetylated derivative could activate TAS2R46. Eventually, this study provided initial evidence supporting the involvement of TAS2R46 in the underpinning mechanisms of deoxynivalenol action highlighting the need of digging into the involvement of TAS2R46 and TAS2Rs in the adverse effects of deoxynivalenol and congeners.

Uncertainty assessment of proarrhythmia predictions derived from multi-level in silico models

In silico methods can be used for an early assessment of arrhythmogenic properties of drug candidates. However, their use for decision-making is conditioned by the possibility to estimate the predictions' uncertainty. This work describes our efforts to develop uncertainty quantification methods for the predictions produced by multi-level proarrhythmia models. In silico models used in this field usually start with experimental or predicted IC50 values that describe drug-induced ion channel blockade. Using such inputs, an electrophysiological model computes how the ion channel inhibition, exerted by a drug in a certain concentration, translates to an altered shape and duration of the action potential in cardiac cells, which can be represented as arrhythmogenic risk biomarkers such as the APD90. Using this framework, we identify the main sources of aleatory and epistemic uncertainties and propose a method based on probabilistic simulations that replaces single-point estimates predicted using multiple input values, including the IC50s and the electrophysiological parameters, by distributions of values. Two selected variability types associated with these inputs are then propagated through the multi-level model to estimate their impact on the uncertainty levels in the output, expressed by means of intervals. The proposed approach yields single predictions of arrhythmogenic risk biomarkers together with value intervals, providing a more comprehensive and realistic description of drug effects on a human population. The methodology was tested by predicting arrhythmogenic biomarkers on a series of twelve well-characterised marketed drugs, belonging to different arrhythmogenic risk classes.

A computational study on the biotransformation of alkenylbenzenes by a selection of CYPs: Reflections on their possible bioactivation

Alkenylbenzenes are aromatic compounds found in several vegetable foods that can cause genotoxicity upon bioactivation by members of the cytochrome P450 (CYP) family, forming 1'-hydroxy metabolites. These intermediates act as proximate carcinogens and can be further converted into reactive 1'-sulfooxy metabolites, which are the ultimate carcinogens responsible for genotoxicity. Safrole, a member of this class, has been banned as a food or feed additive in many countries based on its genotoxicity and carcinogenicity. However, it can still enter the food and feed chain. There is limited information about the toxicity of other alkenylbenzenes that may be present in safrole-containing foods, such as myristicin, apiole, and dillapiole. In vitro studies showed safrole as mainly bioactivated by CYP2A6 to form its proximate carcinogen, while for myristicin this is mainly done by CYP1A1. However, it is not known whether CYP1A1 and CYP2A6 can activate apiole and dillapiole. The present study uses an in silico pipeline to investigate this knowledge gap and determine whether CYP1A1 and CYP2A6 may play a role in the bioactivation of these alkenylbenzenes. The study found that the bioactivation of apiole and dillapiole by CYP1A1 and CYP2A6 is limited, possibly indicating that these compounds may have limited toxicity, while describing a possible role of CYP1A1 in the bioactivation of safrole. The study expands the current understanding of safrole toxicity and bioactivation and helps understand the mechanisms of CYPs involved in the bioactivation of alkenylbenzenes. This information is essential for a more informed analysis of alkenylbenzenes toxicity and risk assessment.

Improvements to in silico skin sensitisation predictions through privacy-preserving data sharing

In silico models are often built solely on publicly available data which may mean that they are less predictive for proprietary chemical space. Data sharing initiatives can improve the performance of such models, but organisations are often unable to share their data due to the need to protect their business interests and maintain the confidentiality of the chemicals in their research and development programmes. In silico models like Derek Nexus, which use expert knowledge to develop structural alerts based on chemical toxicity, can use proprietary data to identify new areas of chemical space and/or refine existing alerts whilst still preserving the privacy of the confidential data. Five hundred and thirty seven proprietary chemicals with skin sensitisation data were shared which led to the implementation of 7 new alerts and 5 modified alerts, with a concomitant 19% increase in sensitivity and 3% increase in specificity of the model.

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. It has been suggested, intuitively, that combining data obtained from across these sources might serve to enhance overall confidence in derived judgment. This concept may be formalised in the “tiered assessment” approach, whereby evidence gathered through a sequential NAM testing strategy is exploited so to infer the properties of a compound of interest. Our intention has been to provide an illustration of how such a scheme might be developed and applied within a practical setting—adopting for this purpose the endpoint of rat acute oral lethality. Bayesian statistical inference is drawn upon to enable quantification of degree of confidence that a substance might ultimately belong to one of five LD50-associated toxicity categories. Informing this is evidence acquired both from existing in silico and in vitro resources, alongside a purposely-constructed random forest model and structural alert set. Results indicate that the combination of in silico methodologies provides moderately conservative estimations of hazard, conducive for application in safety assessment, and for which levels of certainty are defined. Accordingly, scope for potential extension of approach to further toxicological endpoints is demonstrated.

Using VEGAHUB Within a Weight-of-Evidence Strategy

Industrial needs and regulatory requirements have played a significant role in accelerating the use of nontesting methods including in silico tools as alternatives to animal testing. The main interest is not solely on the use of in silico tools, or in read-across, but on better toxicological safety assessment of substances, and for this purpose more advanced, integrated strategies have to be implemented. VEGAHUB wants to promote this broader view, not necessarily focused on a specific approach. Applying multiple tools and complementary approaches instead of one technique may provide more elements for a more robust evaluation, but at the same time it is important to have a conceptual scheme to integrate multiple, heterogeneous lines of evidence. We will show how the user can benefit from the diversity of tools available within the platform VEGAHUB for assessing the biological properties of chemical substances on an example of (non)mutagenicity.

Molecular docking and dynamics simulation to screen interactive potency and stability of fungicide tebuconazole with thyroid and sex hormone-binding globulin: Implications of endocrine and reproductive interruptions

Tebuconazole is a widely used fungicide in agriculture, and it may easily enter in the human food chain. In addition, tebuconzaol skin permeation coefficient (Log Kp) is -5.55 cm/s and it does not violate Lipinski's rule. It may mimic as a ligand for various endocrine and reproductive receptor leading to toxicological response or disease manifestation. We studied interactive potential of tebuconazole with thyroid and sex hormone-binding globulin. The main methods for this in silico analyses are molecular docking and molecular dynamic (MD) simulation. This paper explores how agriculture fungicide tebuconzaol exposure can be a risk for endocrine and reprotoxicity due to its stable interactive potency with thyroid and sex hormone-binding globulin (2CEO and 1D2S). Thyroid impairment is one of the most common endocrine issues in human health. In molecular docking analyses, tebuconazole exhibited binding potency of -6.28 kcal/mol with 2CEO compared to its native ligand thyroxin and inhibitor propylthiouracil which had the binding potency of -9.9 and -4.49 kcal/mol, respectively. The binding score of tebuconzaol with 1D2S was found to be -7.54 kcal/mol compared to native ligand dihydrotestosteron and inhibitor aminoglutethimide which exhibited the binding score of -6.84 and -11.41 kcal/mol, respectively. Therefore, each complex was subjected to MD simulation for comparative assessment of physical movement. The root mean square deviation (RMSD), root mean square fluctuation (RMSF), Radius of Gyration and hydrogen bonding exhibited that fluconazole had stable binding pattern with 2CEO and 1D2S which was almost similar to native ligand and its inhibitor. Study revealed that tebuconazole may lead to potent endocrine and reproductive disruptions.

Investigating DNA adduct formation by flavor chemicals and tobacco byproducts in electronic nicotine delivery system (ENDS) using in silico approaches

The presence of flavors is one of the commonly cited reasons for use of e-cigarettes by youth; however, the potential harms from inhaling these chemicals and byproducts have not been extensively studied. One mechanism of interest is DNA adduct formation, which may lead to carcinogenesis. We identified two chemical classes of flavors found in tobacco products and byproducts, alkenylbenzenes and aldehydes, documented to form DNA adducts. Using in silico toxicology approaches, we identified structural analogs to these chemicals without DNA adduct information. We conducted a structural similarity analysis and also generated in silico model predictions of these chemicals for genotoxicity, mutagenicity, carcinogenicity, and skin sensitization. The empirical and in silico data were compared, and we identified strengths and limitations of these models. Good concordance (80-100%) was observed between DNA adduct formation and models predicting mammalian mutagenicity (mouse lymphoma sassy L5178Y) and skin sensitization for both chemical classes. On the other hand, different prediction profiles were observed for the two chemical classes for the modeled endpoints, unscheduled DNA synthesis and bacterial mutagenicity. These results are likely due to the different mode of action between the two chemical classes, as aldehydes are direct acting agents, while alkenylbenzenes require bioactivation to form electrophilic intermediates, which form DNA adducts. The results of this study suggest that an in silico prediction for the mouse lymphoma assay L5178Y, may serve as a surrogate endpoint to help predict DNA adduct formation for chemicals found in tobacco products such as flavors and byproducts.

Skin sensitization in silico protocol

The assessment of skin sensitization has evolved over the past few years to include in vitro assessments of key events along the adverse outcome pathway and opportunistically capitalize on the strengths of in silico methods to support a weight of evidence assessment without conducting a test in animals. While in silico methods vary greatly in their purpose and format; there is a need to standardize the underlying principles on which such models are developed and to make transparent the implications for the uncertainty in the overall assessment. In this contribution, the relationship between skin sensitization relevant effects, mechanisms, and endpoints are built into a hazard assessment framework. Based on the relevance of the mechanisms and effects as well as the strengths and limitations of the experimental systems used to identify them, rules and principles are defined for deriving skin sensitization in silico assessments. Further, the assignments of reliability and confidence scores that reflect the overall strength of the assessment are discussed. This skin sensitization protocol supports the implementation and acceptance of in silico approaches for the prediction of skin sensitization.

An in silico structural approach to characterize human and rainbow trout estrogenicity of mycotoxins: Proof of concept study using zearalenone and alternariol

The mycotoxins zearalenone and alternariol may contaminate food and feed raising toxicological concerns due to their estrogenicity. Inter-species differences in their toxicokinetics and toxicodynamics may occur depending on evolution of taxa-specific traits. As a proof of principle, this manuscript investigates the comparative toxicodynamics of zearalenone, its metabolites (alpha-zearalenol and beta-zearalenol), and alternariol with regards to estrogenicity in humans and rainbow trout. An in silico structural approach based on docking simulations, pharmacophore modeling and molecular dynamics was applied and computational results were analyzed in comparison with available experimental data. The differences of estrogenicity among species of zearalenone and its metabolites have been structurally explained. Also, the low estrogenicity of alternariol in trout has been characterized here for the first time. This approach can provide a powerful tool for the characterization of interspecies differences in mycotoxin toxicity for a range of protein targets and relevant compounds for the food- and feed-safety area.

Genetic toxicology in silico protocol

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.

In Silico Approaches to Predict Drug-Transporter Interaction Profiles: Data Mining, Model Generation, and Link to Cholestasis

Transport proteins play a crucial role in drug distribution, disposition, and clearance by mediating cellular drug influx and efflux. Inhibition of these transporters may lead to drug-drug interactions or even drug-induced liver injury, such as cholestasis, which comprises a major challenge in drug development process. Thus, computer-based (in silico) models that can predict the pharmacological and toxicological profiles of these small molecules with respect to liver transporters may help in the early prioritization of compounds and hence may lower the high attrition rates. In this chapter, we provide a protocol for in silico prediction of cholestasis by generating validated predictive models. In addition to the two-dimensional molecular descriptors, we include transporter inhibition predictions as descriptors and evaluate the influence of the same on the performance of the cholestasis models.

A defined approach for predicting skin sensitisation hazard and potency based on the guided integration of in silico, in chemico and in vitro data using exclusion criteria

A decision tree-based defined approach (DA) has been designed using exclusion criteria based on applicability domain knowledge of in chemico/in vitro information sources covering key events 1-3 in the skin sensitisation adverse outcome pathway and an in silico tool predicting the adverse outcome (Derek Nexus). The hypothesis is that using exclusion criteria to de-prioritise less applicable assays and/or in silico outcomes produces a rational, transparent, and reliable DA for the prediction of skin sensitisation potential. Five exclusion criteria have been established: Derek Nexus reasoning level, Derek Nexus negative prediction, metabolism, lipophilicity, and lysine-reactivity. These are used to prioritise the most suitable information sources for a given chemical and results from which are used in a '2 out of 3' approach to provide a prediction of hazard. A potency category (and corresponding GHS classification) is then assigned using a k-Nearest Neighbours model containing human and LLNA data. The DA correctly identified the hazard (sensitiser/non-sensitiser) for 85% and 86% of a dataset with reference LLNA and human data. The correct potency category was identified for 59% and 68% of chemicals, and the GHS classification accurately predicted for 73% and 76% with reference LLNA and human data, respectively.

In silico study toward the identification of new and safe potential inhibitors of photosynthetic electron transport

To address the rising global demand for food, it is necessary to search for new herbicides that can control resistant weeds. We performed a 2D-quantitative structure-activity relationship (QSAR) study to predict compounds with photosynthesis-inhibitory activity. A data set of 44 compounds (quinolines and naphthalenes), which are described as photosynthetic electron transport (PET) inhibitors, was used. The obtained model was approved in internal and external validation tests. 2D Similarity-based virtual screening was performed and 64 compounds were selected from the ZINC database. By using the VEGA QSAR software, 48 compounds were shown to have potential toxic effects (mutagenicity and carcinogenicity). Therefore, the model was also tested using a set of 16 molecules obtained by a similarity search of the ZINC database. Six compounds showed good predicted inhibition of PET. The obtained model shows potential utility in the design of new PET inhibitors, and the hit compounds found by virtual screening are novel bicyclic scaffolds of this class.

In silico toxicology protocols

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information.

Computational prediction of immune cell cytotoxicity

Immunotoxicity, defined as adverse effects of xenobiotics on the immune system, is gaining increasing attention in the approval process of industrial chemicals and drugs. In-vivo and ex-vivo experiments have been the gold standard in immunotoxicity assessment so far, so the development of in-vitro and in-silico alternatives is an important issue. In this paper we describe a widely applicable, easy-to use computational approach which can serve as an initial immunotoxicity screen of new chemical entities. Molecular fingerprints describing chemical structure were used as parameters in a machine-learning approach based on the Naïve-Bayes learning algorithm. The model was trained using blood-cell growth inhibition data from the NCI database and validated externally with several in-house and literature-derived data sets tested in cytotoxicity assays on different types on immune cells. Both cross-validations and external validations resulted in areas under the receiver operator curves (ROC/AUC) of 75% or higher. The classification of the validation data sets occurred with excellent specificities and fair to excellent selectivities, depending on the data set. This means that the probability of actual immunotoxicity is very high for compounds classified as immunotoxic, while the fraction of false negative predictions might vary. Thus, in a multistep immunotoxicity screening scheme, the classification as immunotoxic can be accepted without additional confirmation, while compounds classified as not immunotoxic will have to be subjected to further investigation.

Toxicology: a discipline in need of academic anchoring--the point of view of the German Society of Toxicology

The paper describes the importance of toxicology as a discipline, its past achievements, current scientific challenges, and future development. Toxicological expertise is instrumental in the reduction of human health risks arising from chemicals and drugs. Toxicological assessment is needed to evaluate evidence and arguments, whether or not there is a scientific base for concern. The immense success already achieved by toxicological work is exemplified by reduced pollution of air, soil, water, and safer working places. Predominantly predictive toxicological testing is derived from the findings to assess risks to humans and the environment. Assessment of the adversity of molecular effects (including epigenetic effects), the effects of mixtures, and integration of exposure and biokinetics into in vitro testing are emerging challenges for toxicology. Toxicology is a translational science with its base in fundamental science. Academic institutions play an essential part by providing scientific innovation and education of young scientists.

OpenVirtualToxLab--a platform for generating and exchanging in silico toxicity data

The VirtualToxLab is an in silico technology for estimating the toxic potential--endocrine and metabolic disruption, some aspects of carcinogenicity and cardiotoxicity--of drugs, chemicals and natural products. The technology is based on an automated protocol that simulates and quantifies the binding of small molecules towards a series of currently 16 proteins, known or suspected to trigger adverse effects: 10 nuclear receptors (androgen, estrogen α, estrogen β, glucocorticoid, liver X, mineralocorticoid, peroxisome proliferator-activated receptor γ, progesterone, thyroid α, thyroid β), four members of the cytochrome P450 enzyme family (1A2, 2C9, 2D6, 3A4), a cytosolic transcription factor (aryl hydrocarbon receptor) and a potassium ion channel (hERG). The toxic potential of a compound--its ability to trigger adverse effects--is derived from its computed binding affinities toward these very proteins: the computationally demanding simulations are executed in client-server model on a Linux cluster of the University of Basel. The graphical-user interface supports all computer platforms, allows building and uploading molecular structures, inspecting and downloading the results and, most important, rationalizing any prediction at the atomic level by interactively analyzing the binding mode of a compound with its target protein(s) in real-time 3D. Access to the VirtualToxLab is available free of charge for universities, governmental agencies, regulatory bodies and non-profit organizations.

Comprehension of drug toxicity: software and databases

Quantitative structure-property/activity relationships (QSPRs/QSARs) are a tool (in silico) to rapidly predict various endpoints in general, and drug toxicity in particular. However, this dynamic evolution of experimental data (expansion of existing experimental data on drugs toxicity) leads to the problem of critical estimation of the data. The carcinogenicity, mutagenicity, liver effects and cardiac toxicity should be evaluated as the most important aspects of the drug toxicity. The toxicity is a multidimensional phenomenon. It is apparent that the main reasons for the increase in applications of in silico prediction of toxicity include the following: (i) the need to reduce animal testing; (ii) computational models provide reliable toxicity prediction; (iii) development of legislation that is related to use of new substances; (iv) filling data gaps; (v) reduction of cost and time; (vi) designing of new compounds; (vii) advancement of understanding of biology and chemistry. This mini-review provides analysis of existing databases and software which are necessary for use of robust computational assessments and robust prediction of potential drug toxicities by means of in silico methods.

Establishing the level of safety concern for chemicals in food without the need for toxicity testing

There is demand for methodologies to establish levels of safety concern associated with dietary exposures to chemicals for which no toxicological data are available. In such situations, the application of in silico methods appears promising. To make safety statement requires quantitative predictions of toxicological reference points such as no observed adverse effect level and carcinogenic potency for DNA-reacting chemicals. A decision tree (DT) has been developed to aid integrating exposure information and predicted toxicological reference points obtained with quantitative structure activity relationship ((Q)SAR) software and read across techniques. The predicted toxicological values are compared with exposure to obtain margins of exposure (MoE). The size of the MoE defines the level of safety concern and should account for a number of uncertainties such as the classical interspecies and inter-individual variability as well as others determined on a case by case basis. An analysis of the uncertainties of in silico approaches together with results from case studies suggest that establishing safety concern based on application of the DT is unlikely to be significantly more uncertain than based on experimental data. The DT makes a full use of all data available, ensuring an adequate degree of conservatism. It can be used when fast decision making is required.