QSAR Toolbox Research Articles

In silico metodologija u toksikologiji - softveri za predviđanje toksičnosti

For over a century, one of the main toxicologists' goals was finding the relationship between the chemical structure and toxicity. The development of computer technologies has lead to the production of software models that could be applied for this purpose. However, use of in silico methodology in toxicology is very complex. Having in mind that toxic effect occurs through multiple mechanisms on different tissues and organs, creating software that can make reliable toxicity predictions is very complicated. In silico methods found the application in toxicity assessment of substances for which data obtained from traditional toxicity tests is lacking (metabolites, drug impurities, etc.). Also, in silico methods can be used in the process of chemical synthesis, with the aim of designing safer chemicals. Currently, in silico methods for toxicity predictions are most commonly used along with standard toxicological tests, with a tendency to replace in vitro and in vivo toxicity tests in the future. This paper will present the strengths and limitations of commonly used software in in silico toxicology (OECD QSAR Toolbox, Derek Nexus, ToxTree i MultiCASE).

Alert performance: A new functionality in the OECD QSAR Toolbox

Both industry and regulatory agencies use structure-activity relationships and read-across as toxicity assessment methods alternative to animal experimentation. A prerequisite for acceptance of the outcome of such in silico predictions is establishing the mechanistic similarity (i.e. a common mode of (toxicological) action) among the target substance(s) and the source substance(s). To assist in assessing this similarity, an “Alert performance” functionality has been added to the OECD QSAR Toolbox Version 4.0. This functionality is designed to provide information for evaluating the mechanistic and/or structural similarity among the analogues forming a category. This “Alert performance” informs the user on whether the identified alert(s) in the target substance or its metabolites is linked to consistent effects for the endpoint of interest for other substances triggering the same alert and for which experimental data are available. In this respect, calculation of alert performance shows the predictivity of a given alert to the selected endpoint. The predictivity of alerts is context-dependent and is affected by the structure of target chemical, targeted endpoint, selected databases, data usage and data scale. The use of alerts with high performances will lead to the formation of categories of chemicals showing similar toxicological effects, thus reducing the uncertainty when filling data gaps. This paper illustrates the workflow of the alert performance module and addresses several questions that could arise during its applications.

Tier-based skin irritation testing of hair cleansing conditioners and their constituents

Purpose/Aim The U.S. Food and Drug Administration (FDA) does not require specific testing to demonstrate the safety of personal care and cosmetic products or their ingredients. Recently, there have been reports of skin irritation associated with the use of commercially available cleansing conditioners. The goal of this study was to implement a tier-based safety assessment to evaluate the skin irritation potential of six commercially available cleansing conditioners and their ingredients. Materials and Methods The first tier of testing utilized the Organization for Economic Co-operation and Development (OECD) QSAR Toolbox to perform an in silico evaluation of the skin irritation potential of the product ingredients, and the second tier of testing utilized an OECD in vitro guideline test to evaluate the skin irritation potential of each product. Results Thirty-two ingredients were evaluated using the OECD QSAR Toolbox profiler for the tier one analysis; nine ingredients received a structural alert for skin irritation/corrosion. In the tier two in vitro analysis, the evaluated cleansing conditioner products were all classified as non-irritants. Conclusions These results provide evidence that use of the evaluated commercially available cleansing conditioners would not be expected to cause skin irritation among consumers. Additionally, this study demonstrates that the presence of structural alerts does not always accurately predict the safety of a product, as focused tier-based testing is necessary to comprehensively evaluate a product. This study illustrates a tier-based safety assessment approach, applicable to a wide variety of health endpoints, which efficiently and adequately evaluates the safety of personal care and cosmetic products and their ingredients.

Prioritization of pharmaceuticals in drinking water exposure based on toxicity and environmental fate assessment by in silico tools: An integrated and transparent ranking

The concentration of pharmaceuticals in water and health risk to the human population is a topic of concern among the scientific community, regulators, and the general population. Many recent studies have suggested different, even conflicting, criteria for the definition of priority pharmaceuticals for drinking-water exposure. In this work, we assessed 10 endpoints of environmental fate and human toxicity for 39 active pharmaceutical ingredients (APIs) by in silico models and data review. A visual and transparent Toxicological Prioritization Index (ToxPi) was proposed based on in silico, in chemico, in vitro, and in vivo results. Predictions of environmental fate and toxicity were made through in silico models of EPISuiteTM, PBT Profiler v.2, TEST v4.2 and QSAR Toolbox v.3.2. The software ToxPi GUI was used to create a multicriteria-based ranking with environmental fate and human toxicity endpoints to identify priority compounds. Our multicriteria ranking (fate and hazard-based) suggested that the some of the most concerning APIs were metronidazole (ToxPi: 5.67; CI95%: 5.51–5.83), nitrofurantoin (ToxPi: 5.22 CI95%: 5.21–5.22) and ethinylestradiol (ToxPi: 5.20; CI95%: 4.70–5.79). Different APIs may represent high level of concern by different characteristics of environmental exposure and/or toxicity, and our visual muticriteria ranking can support targeted decision-making. Computational tools may be important for predicting environmental fate and toxicity as well as visually identifying priority compounds by multicriteria-based rankings and predicting compounds with high priority to support more targeted multicriteria decision-making, risk assessment, and monitoring steps.

Integrating bioassays, chemical analysis and in silico techniques to identify genotoxicants in surface water

Identification of hazardous compounds, as the first step of water protection and regulation, is still challenged by the difficulty to establish a linkage between toxic effects and suspected contaminants. Genotoxic compounds are one type of highly relevant toxicants in surface water, which may attack the DNA and lead to cancer in individual organism, or even damaged germ cells to be passed on to future generations. Thus, the establishment of a linkage between genotoxic effects and genotoxicant is important for environmental toxicologists and chemists. For this purpose, in the present study in silico methods were integrated with bioassays, chemical analysis and literature information to identify genotoxicants in surface water. Large volume water samples from 22 sampling sites of the Danube were collected and subjected to biological and chemical analysis. Samples from the most toxic sites (JDS32, JDS44 and JDS63) induced significant genotoxic effects in the micronucleus assay, and two of them caused mutagenicity in the Ames fluctuation assay. Chemical analysis showed that 68 chemicals were detected in these most toxic samples. Literature findings and in silico techniques using the OECD QSAR Toolbox and the ChemProp software package revealed genotoxic potentials for 29 compounds out of 68 targeted chemicals. To confirm the integrative technical data, the micronucleus assay and the Ames fluctuation assay were applied with artificial mixtures of those compounds and the raw water sample extracts. The results showed that 18 chemicals explained 48.5% of the genotoxicity in the micronucleus assay. This study highlights the capability of in silico techniques in linking adverse biological effect to suspicious hazardous compounds for the identification of toxicity drivers, and demonstrates the genotoxic potential of pollutants in the Danube.

Development of a Decision Tree for Mitochondrial Dysfunction: Uncoupling of Oxidative Phosphorylation.

Mitochondrial dysfunction is the result of a number of processes including the uncoupling of oxidative phosphorylation. This study outlines the development of a decision tree-based profiling scheme capable of assigning chemicals to one of six confidence-based categories. The decision tree is based on a set of structural alerts and physicochemical boundaries identified from a detailed study of the literature. The physicochemical boundaries define a chemical relationship with both log P and p Ka. The study also outlines how the decision tree can be used to profile databases through an analysis of the publically available databases in the OECD QSAR Toolbox. This analysis enabled a set of additional structural alerts to be identified that are of concern for protonophoric ability. The decision tree will be incorporated in the OECD QSAR Toolbox V4.3. The intended usage is to group the chemicals into categories of chronic human health and environmental toxicological end points.

In silico identification of protein targets for chemical neurotoxins using ToxCast in vitro data and read-across within the QSAR toolbox.

There are many mechanisms of neurotoxicity that are initiated by the interaction of chemicals with different neurological targets. Under the U.S. Environmental Protection Agency's ToxCast program, the biological activity of thousands of chemicals was screened in biochemical and cell-based assays in a high-throughput manner. Two hundred sixteen assays in the ToxCast screening database were identified as targeting a total of 123 proteins having neurological functions according to the Gene Ontology database. Data from these assays were imported into the Organization for Economic Co-operation and Development QSAR Toolbox and used to predict neurological targets for chemical neurotoxins. Two sets of data were generated: one set was used to classify compounds as active or inactive and another set, composed of AC50s for only active compounds, was used to predict AC50 values for unknown chemicals. Chemical grouping and read-across within the QSAR Toolbox were used to identify neurologic targets and predict interactions for pyrethroids, a class of compounds known to elicit neurotoxic effects in humans. The classification prediction results showed 79% accuracy while AC50 predictions demonstrated mixed accuracy compared with the ToxCast screening data.

The OECD QSAR Toolbox Starts Its Second Decade.

The OECD QSAR Toolbox is a computer software designed to make pragmatic qualitative and quantitative structure-activity relationship methods-based predictions of toxicity, including read-across, available to the user in a comprehensible and transparent manner. The Toolbox, provide information on chemicals in structure-searchable, standardized files that are associated with chemical and toxicity data to ensure that proper structural analogs can be identified. This chapter describes the advantages of the Toolbox, the aims, approach, and workflow of it, as well as reviews its history. Additionally, key functional elements of it use are explained and features new to Version 4.1 are reported. Lastly, the further development of the Toolbox, likely needed to transform it into a more comprehensive Chemical Management System, is considered.

Predicting neurological targets for chemical neurotoxins using ToxCast in vitro data and read-across within QSAR Toolbox

Transportation fuels consist of a large number of species that belong to different families of compounds. Surrogate fuel representations have been formulated to better understand their fundamental chemical composition and to emulate combustion properties. These descriptions are formulated using experiments or through computations, which has thus led to the existence of two different notions of surrogates. There is further distinction of concepts through the use of physical and chemical surrogates, which are designed to emulate those specific properties. Although several surrogate design methodologies have been proposed in literature, they do not incorporate information on experimental uncertainty. By addressing this issue, it is shown that this information is crucial for the reliable construction of surrogates through computations. To incorporate physical fuel properties, a consistent approach through the use of the recent ASTM D2887 distillation curve standard is discussed. Then, a formal computational procedure is presented that incorporates information of experimental uncertainties into the surrogate description. It is shown that surrogates then describe a feasible region and are hence not unique. Both physical and chemical properties are utilized as combustion property targets (CPTs) and consistency with experimental formulations is demonstrated for JP-8 and Jet-A (POSF 4658) surrogates. In addition, the use of convex optimization puts existing concepts for surrogate representation on a more rigorous basis and several conclusions are drawn, particularly on the importance of specific CPTs and weighting factors of regression-based approaches. Also, the effect of using simplified models for the evaluation of CPTs on the final surrogate composition is shown by considering the example of linear blending rules for ignition delay. Finally, the surrogate representation problem is connected to multi-parametric optimization and bounds on surrogate compositions are calculated as a function of the experimental uncertainty along with comparisons against experimental results.

Derivation of freshwater water quality criteria for dibutyltin dilaurate from measured data and data predicted using interspecies correlation estimate models

Dibutyltin dilaurate (DBTL) is an organotin compound that has been widely detected in aquatic environments. However, the ecological risk assessment for DBTL is hard to perform due to the absence of water quality criteria (WQC) and lack of toxicity data. In this study, toxicity data were obtained from acute and chronic toxicity tests using aquatic species resident in China. Based on the species sensitivity distributions (SSDs) constructed from these test data, the acute water quality criterion (AWQC) and chronic water quality criterion (CWQC) were then derived to be 132 μg/L and 31.9 μg/L for DBTL. In order to valid the predicted data in the application to the protective criteria development and hazard assessment, the web-based interspecies correlation estimation (Web-ICE) system was used to estimate acute toxicities. Here two acute toxicity data groups for surrogate species were chosen from experimental data and QSAR Toolbox-predicted data separately, giving estimated data for prediction species using Web-ICE software. According to Kolmogorov–Smirnov test results, Web-ICE-based SSDs and measurement-based SSD were not significantly different. The results showed that the differences between measurement-based (132 μg/L) and Web-ICE-based AWQCs (39.7 μg/L and 58.5 μg/L) were statistically acceptable. Under certain conditions, it is therefore worth considering using the QSAR Toolbox plus Web-ICE method here to provide a beneficial supplement for developing preliminarily screen level toxicities in WQC when little or no relevant information are available. Besides, the geographic distribution of species was demonstrated not to be a significant factor in SSD establishment and WQC derivation.

Improving confidence in (Q)SAR predictions under Canada’s Chemicals Management Plan – a chemical space approach

One of the key challenges of Canada’s Chemicals Management Plan (CMP) is assessing chemicals with limited/no empirical hazard data for their risk to human health. In some instances, these chemicals have not been tested broadly for their toxicological potency; as such, limited information exists on their potential to induce human health effects following exposure. Although (quantitative) structure activity relationship ((Q)SAR) models are able to generate predictions to address data gaps for certain toxicological endpoints, the confidence in predictions also needs to be addressed. One way to address this issue is to apply a chemical space approach. This approach uses international toxicological databases, for example, those available in the Organisation for Economic Co-operation and Development (OECD) QSAR Toolbox. The approach,assesses a model’s ability to predict the potential hazards of chemicals that have limited hazard data that require assessment under the CMP when compared to a larger, data-rich chemical space that is structurally similar to chemicals of interest. This evaluation of a model’s predictive ability makes (Q)SAR analysis more transparent and increases confidence in the application of these predictions in a risk-assessment context. Using this approach, predictions for such chemicals obtained from four (Q)SAR models were successfully classified into high, medium and low confidence levels to better inform their use in decision-making.

The Adverse Outcome Pathway for Skin Sensitisation: Moving Closer to Replacing Animal Testing

This article outlines the work of the Organisation for Economic Co-operation and Development (OECD) that led to being jointly awarded the 2015 Lush Black Box Prize. The award-winning work centred on the development of 'The Adverse Outcome Pathway for Skin Sensitisation Initiated by Covalent Binding to Proteins'. This Adverse Outcome Pathway (AOP) has provided the mechanistic basis for the integration of skin sensitisation-related information. Recent developments in integrated approaches to testing and assessment, based on the AOP, are summarised. The impact of the AOP on regulatory policy and on the Three Rs are discussed. An overview of the next generation of the skin sensitisation AOP module in the OECD QSAR Toolbox, based on more-recent work at the Laboratory of Mathematical Chemistry, is also presented.

Evaluation of the OECD QSAR toolbox in the screening of chemical sensitizer

Evaluation of the OECD QSAR toolbox in the screening of chemical sensitizer

The adverse outcome pathway concept: A basis for developing regulatory decision-making tools.

The Adverse Outcome Pathway (AOP) concept is expected to guide risk assessors in their work to use all existing information on the effects of chemicals on humans and wildlife, and to target the generation of additional information to the regulatory objective. AOPs will therefore be used in the Organisation for Economic Co-operation and Development (OECD) chemical safety programme, as underlying scientific rationales for the development of alternative methods for hazard assessment, such as read-across, in vitro test methods or the development of integrated testing strategies that have the potential to replace animal tests. As a proof-of-concept, the OECD has developed an AOP for skin sensitisation, and as a follow-up has: a) implemented the AOP into the OECD QSAR Toolbox, so that information related to the Key Events (KEs) in the AOP can be used to group chemicals that are expected to act by the same mechanism and hence have the same skin sensitisation potential; b) developed alternative test methods for the KEs, so that ultimately chemicals can be tested for skin sensitisation without the use of animal tests. The development of integrated testing strategies based on the AOP is ongoing. Building on this proof-of-concept, the OECD has launched an AOP development programme with a first batch of AOPs published in 2016. A number of IT tools, which together form an AOP Knowledge Base, are at various stages of development, and support the construction of AOPs and their use in the development of integrated approaches for testing and assessment. Following the publication of the first batch of AOPs, OECD member countries will decide on priorities for their use in supporting the development of tools for regulatory use.

Estrogen Receptor Binding Affinity of Food Contact Material Components Estimated by QSAR.

The presented work characterized components of food contact materials (FCM) with potential to bind to estrogen receptor (ER) and cause adverse effects in the human organism. The QSAR Toolbox, software application designed to identify and fill toxicological data gaps for chemical hazard assessment, was used. Estrogen receptors are much less of a lock-and-key interaction than highly specific ones. The ER is nonspecific enough to permit binding with a diverse array of chemical structures. There are three primary ER binding subpockets, each with different requirements for hydrogen bonding. More than 900 compounds approved as of FCM components were evaluated for their potential to bind on ER. All evaluated chemicals were subcategorized to five groups with respect to the binding potential to ER: very strong, strong, moderate, weak binder, and no binder to ER. In total 46 compounds were characterized as potential disturbers of estrogen receptor. Among the group of selected chemicals, compounds with high and even very high affinity to the ER binding subpockets were found. These compounds may act as gene activators and cause adverse effects in the organism, particularly during pregnancy and breast-feeding. It should be considered to carry out further in vitro or in vivo tests to confirm their potential to disturb the regulation of physiological processes in humans by abnormal ER signaling and subsequently remove these chemicals from the list of approved food contact materials.

Identifying unknown by-products in drinking water using comprehensive two-dimensional gas chromatography–quadrupole mass spectrometry and in silico toxicity assessment

Improvements in extraction and detection technologies have increased our abilities to identify new disinfection by-products (DBPs) over the last 40 years. However, most previous studies combined DBP identification and measurement efforts with toxicology to address concerns on a few expected DBPs, making it difficult to better define the health risk from the individual DBPs. In this study, a nontargeted screening method involving comprehensive two-dimensional gas chromatography-quadrupole mass spectrometry (GC × GC-qMS) combined with OECD QSAR Toolbox Ver. 3.2 was developed for identifying and prioritizing of volatile and semi-volatile DBPs in drinking water. The method was successfully applied to analyze DBPs formed during chlorination, chloramination or ozonation of the raw water. Over 500 compounds were tentatively identified in each sample, showing the superior performance of this analytical technique. A total of 170 volatile and semi-volatile DBPs representing fourteen chemical classes were then identified, according to the criteria that the DBP was presented in the duplicate treated samples. The genotoxicity and carcinogenicity of the DBPs were evaluated using Toolbox, and 58 DBPs were found to be actual or potential genotoxicants. The accuracy of the compound identification was determined by comparing 47 identified compounds with commercially available standards. About 90% (41 of the 47) of the compounds that were automatically identified using the library were correct. The results show that GC×GC-qMS coupled with a quantitative structure–activity relationship model is a powerful and fast nontargeted screening technique for compounds. The method and results provide us a new idea for identification and prioritization of DBPs.

Qspr Model for Regulatory Purpose: from Development to Integration Into the Qsar Toolbox

Quantitative Structure-Property Relationships (QSPR) are predictive methods of macroscopic properties of substances based on their only molecular structures. If these methods were initially mainly devoted to biology and toxicology, they are nowadays increasingly used for the prediction of physico-chemical properties.In the framework of the European REACH regulation (2006), their developments are encouraged as alternative to experimental tests. Indeed, this regulation requires the evaluation of the physico-chemical properties of a large number of existing substances (143 000 pre-registered substances in 2008) in order to allow their use before 2018. Due to the number of substances and properties, the timing, the economic costs, the feasibility at the R&D level and the risks for the manipulator, in particular for the characterization of the dangerous physico-chemical properties (explosibility, flammability), the complete gathering of the data solely based on experimental measurements is not realistic. Thus, the use of such alternative predictive methods was recommended by REACH for the evaluation of the properties of substances.In this context, INERIS developed QSPR models for the prediction of hazardous physico-chemical properties of chemical substances like explosibility of nitro compounds, thermal stability of organic peroxides or flammability of amines. To this end, quantum chemical tools allowed calculating relevant molecular descriptors (notably issued from Conceptual DFT) and to evidence subjacent chemical mechanisms. These models were developed according to the five validation principles of QSPR models proposed by OECD for regulatory use. During the French PREDIMOL (molecular modeling prediction of physico-chemical properties of products) project (2015) funded by ANR (National Research Agency), it was demonstrated that molecular modeling, notably the use of QSPR models, was a credible alternative approach to experimental characterization to access, in a reliable and fast manner, physico-chemical properties of substances required by REACH (annexes VII and IX).This paper presents an overview of existing validated QSPR models for hazardous physico-chemical properties including those developed recently by INERIS. Then, the different ways towards the acceptance and use of QSPR in a regulatory assessment of chemicals are discussed and exemplified with the first QSPR model implemented into the QSAR Toolbox of ECHA and OECD (2015) (in its version 3.3 of December 2014) for the prediction of a hazardous physico-chemical property. This model is a simple multilinear model (based only on constitutional descriptors) dedicated to the prediction of one property of explosibility which is the impact sensitivity of nitroaliphatics (Prana et al. 2012).

Peptide reactivity associated with skin sensitization: The QSAR Toolbox and TIMES compared to the DPRA

The molecular initiating event (MIE) of skin sensitization is the binding of a hapten to dermal proteins. This can be assessed using the in chemico direct peptide reactivity assay (DPRA) or in silico tools such as the QSAR Toolbox and TIMES SS. In this study, the suitability of these methods was analyzed by comparing their results to in vivo sensitization data of LLNA and human studies.Compared to human data, 84% of non-sensitizers and sensitizers yielded consistent results in the DPRA. In silico tools resulted in ‘no alert’ for 83%–100% of the non-sensitizers, but alerted only 55%–61% of the sensitizers. The inclusion of biotic and abiotic transformation simulations yielded more alerts for sensitizers, but simultaneously dropped the number of non-alerted non-sensitizers. In contrast to the DPRA, in silico tools were more consistent with results of the LLNA than human data. Interestingly, the new “DPRA profilers” (QSAR Toolbox) provided unsatisfactory results.Additionally, the results were combined in the ‘2 out of 3’ prediction model with in vitro data derived from LuSens and h-CLAT. Using DPRA results, the model identified 90% of human sensitizers and non-sensitizers; using in silico results (including abiotic and biotic activations) instead of DPRA results led to a comparable high predictivity.

QSAR Toolbox – workflow and major functionalities

The OECD QSAR Toolbox is a software application intended to be used by governments, the chemical industry and other stakeholders in filling gaps in (eco)toxicity data needed for assessing the hazards of chemicals. The development and release of the Toolbox is a cornerstone in the computerization of hazard assessment, providing an ‘all inclusive’ tool for the application of category approaches, such as read-across and trend analysis, in a single software application, free of charge. The Toolbox incorporates theoretical knowledge, experimental data and computational tools from various sources into a logical workflow. The main steps of this workflow are substance identification, identification of relevant structural characteristics and potential toxic mechanisms of interaction (i.e. profiling), identification of other chemicals that have the same structural characteristics and/or mechanism (i.e. building a category), data collection for the chemicals in the category and use of the existing experimental data to fill the data gap(s). The description of the Toolbox workflow and its main functionalities is the scope of the present article.

Integrated decision strategies for skin sensitization hazard.

One of the top priorities of the Interagency Coordinating Committee for the Validation of Alternative Methods (ICCVAM) is the identification and evaluation of non-animal alternatives for skin sensitization testing. Although skin sensitization is a complex process, the key biological events of the process have been well characterized in an adverse outcome pathway (AOP) proposed by the Organisation for Economic Co-operation and Development (OECD). Accordingly, ICCVAM is working to develop integrated decision strategies based on the AOP using in vitro, in chemico and in silico information. Data were compiled for 120 substances tested in the murine local lymph node assay (LLNA), direct peptide reactivity assay (DPRA), human cell line activation test (h-CLAT) and KeratinoSens assay. Data for six physicochemical properties, which may affect skin penetration, were also collected, and skin sensitization read-across predictions were performed using OECD QSAR Toolbox. All data were combined into a variety of potential integrated decision strategies to predict LLNA outcomes using a training set of 94 substances and an external test set of 26 substances. Fifty-four models were built using multiple combinations of machine learning approaches and predictor variables. The seven models with the highest accuracy (89-96% for the test set and 96-99% for the training set) for predicting LLNA outcomes used a support vector machine (SVM) approach with different combinations of predictor variables. The performance statistics of the SVM models were higher than any of the non-animal tests alone and higher than simple test battery approaches using these methods. These data suggest that computational approaches are promising tools to effectively integrate data sources to identify potential skin sensitizers without animal testing. Published 2016. This article has been contributed to by US Government employees and their work is in the public domain in the USA.