(Q)SARs to Predict Environmental Toxicities: Current Status and Future Needs

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

Lessons Learned from Read-Across Case Studies for Repeated-Dose Toxicity

A series of case studies designed to further acceptance of read-across predictions, especially for chronic health-related endpoints, have been evaluated with regard to the knowledge and insight they provide. A common aim of these case studies was to examine sources of uncertainty associated with read-across. While uncertainty is related to the quality and quantity of the read across endpoint data, uncertainty also includes a variety of other factors, the foremost of which is uncertainty associated with the justification of similarity and quantity and quality of data for the source chemical(s). This investigation has demonstrated that the assessment of uncertainty associated with a similarity justification includes consideration of the information supporting the scientific arguments and the data associated with the chemical, toxicokinetic and toxicodynamic similarity. Similarity in chemistry is often not enough to justify fully a read-across prediction, thus, for chronic health endpoints, toxicokinetic and/or toxicodynamic similarity is essential. Data from New Approach Methodology(ies) including high throughput screening, in vitro and in chemico assay and in silico tools, may provide critical information needed to strengthen the toxicodynamic similarity rationale. In addition, it was shown that toxicokinetic (i.e., ADME) similarity, especially metabolism, is often the driver of the overall uncertainty

Quantitative structure-activity relationships of comparative toxicity to aquatic organisms.

Quantitative Structure-Activity relationship (QSAR) attempt statistically to relate the physico-chemical properties of a molecule to its biological activity. A QSAR analysis was performed on the toxicities of up to 75 organic chemicals to two aquatic species, Photobacterium phospherum (known as the Microtox test), and the fathead minnow. To model the toxicities 49 physico-chemical and structural parameters were produced including measures of hydrophobicity, molecular size and electronic effects from techniques such as computational chemistry and the use of molecular connectivity indices. These were reduced to a statistically more manageable number by cluster analysis, principal component analysis, factor analysis, and canonical correlation analysis. The de-correlated data were then used to form relationships with the toxicities. All the techniques were validated using a testing set. Some good predictions of toxicity came from regression analysis of the original de-correlated variables. Although successful in simplifying the complex data matrix, principal component analysis, factor analysis, and canonical content analysis were disappointing as predictors of toxicity. The performance of each of the statistical techniques is discussed. The inter-species relationships of toxicity between four Commonly utilised aquatic endpoints, fathead minnow 96 hour IC50, Microtox 5 minute EC50, Daphnia magna 48 hour IC50, and Tetrahymena pyriformis 60 hour IG50, were investigated. Good relationships was found between the fathead minnow and both T. pyriformis and D. magna toxicities indicating that these species could be used to model fish toxicity. The outliers from individual relationships were assessed in order to elucidate if any molecular features may be causing greater relative toxicity in one species as compared to another. It is concluded that in addition to the intrinsic differences between species, the greater length of the test time for any species may result in increases bioaccumulation, metabolism, and detoxification of certain chemical classes. The relationships involving fish toxicity were moderately improved by the addition of a hydrophobic parameter

Interpretation of QSAR Models: Mining Structural Patterns Taking into Account Molecular Context.

The study focused on QSAR model interpretation. The goal was to develop a workflow for the identification of molecular fragments in different contexts important for the property modelled. Using a previously established approach - Structural and physicochemical interpretation of QSAR models (SPCI) - fragment contributions were calculated and their relative influence on the compounds' properties characterised. Analysis of the distributions of these contributions using Gaussian mixture modelling was performed to identify groups of compounds (clusters) comprising the same fragment, where these fragments had substantially different contributions to the property studied. SMARTSminer was used to detect patterns discriminating groups of compounds from each other and visual inspection if the former did not help. The approach was applied to analyse the toxicity, in terms of 40 hour inhibition of growth, of 1984 compounds to Tetrahymena pyriformis. The results showed that the clustering technique correctly identified known toxicophoric patterns: it detected groups of compounds where fragments have specific molecular context making them contribute substantially more to toxicity. The results show the applicability of the interpretation of QSAR models to retrieve reasonable patterns, even from data sets consisting of compounds having different mechanisms of action, something which is difficult to achieve using conventional pattern/data mining approaches

Development of an In Silico Profiler for Respiratory Sensitisation

In this article, we outline work that led the QSAR and Molecular Modelling Group at Liverpool John Moores University to be jointly awarded the 2013 Lush Science Prize. Our research focuses around the development of in silico profilers for category formation within the Adverse Outcome Pathway paradigm. The development of a well-defined chemical category allows toxicity to be predicted via read-across. This is the central approach used by the OECD QSAR Toolbox. The specific work for which we were awarded the Lush Prize was for the development of such an in silico profiler for respiratory sensitisation. The profiler was developed by an analysis of the mechanistic chemistry associated with covalent bond formation in the lung. The data analysed were collated from clinical reports of occupational asthma in humans. The impact of the development of in silico profilers on the Three Rs is also discussed

Investigation of the Verhaar scheme for predicting acute aquatic toxicity: improving predictions obtained from Toxtree ver. 2.6

Assessment of the potential of compounds to cause harm to the aquatic environment is an integral part 8 of the REACH legislation. To reduce the number of vertebrate and invertebrate animals required for 9 this analysis alternative approaches have been promoted. Category formation and read-across have 10 been applied widely to predict toxicity. A key approach to grouping for environmental toxicity is the 11 Verhaar scheme which uses rules to classify compounds into one of four mechanistic categories. 12 These categories provide a mechanistic basis for grouping and any further predictive modelling. A 13 computational implementation of the Verhaar scheme is available in Toxtree v2.6. The work 14 presented herein demonstrates how modifications to the implementation of Verhaar between version 15 1.5 and 2.6 of Toxtree have improved performance by reducing the number of incorrectly classified 16 compounds. However, for the datasets used in this analysis, version 2.6 classifies more compounds as 17 outside of the domain of the model. Further amendments to the classification rules have been 18 implemented here using a post-processing filter encoded as a KNIME workflow. This results in fewer 19 compounds being classified as outside of the model domain, further improving the predictivity of the 20 scheme. The utility of the modification described herein is demonstrated through building quality, 21 mechanism-specific Quantitative Structure Activity Relationship (QSAR) models for the compounds 22 within specific mechanistic categories

Validation of a fragment-based profiler for thiol reactivity for the prediction of toxicity: skin sensitisation and tetrahymena pyriformis

This study outlines the use of a recently developed fragment-based thiol reactivity profiler for Michael acceptors to predict toxicity towards Tetrahymena pyriformis and skin sensitisation potency as determined in the Local Lymph Node Assay (LLNA). The results showed that the calculated reactivity parameter from the profiler, -log RC50(calc), was capable of predicting toxicity for both endpoints with excellent statistics. However, the study highlighted the importance of a well-defined applicability domain for each endpoint. In terms of Tetrahymena pyriformis this domain was defined in terms of how fast or slowly a given Michael acceptor reacts with thiol leading to two separate quantitative structure-activity models. The first, for fast reacting chemicals required only –Log RC50(calc) as a descriptor, whilst the second required the addition of a descriptor for hydrophobicity. Modelling of the LLNA required only a single descriptor, -log RC50(calc), enabling potency to be predicted. The applicability domain excluded chemicals capable of undergoing polymerisation and those that were predicted to be volatile. The modelling results for both endpoints, using the –log RC50(calc) value from the profiler, were in keeping with previously published studies that have utilised experimentally determined measurements of reactivity. This results demonstrate the output from the fragment-based thiol reactivity profiler can be used to develop quantitative structure-activity relationship models where reactivity towards thiol is a driver of toxicity

Development of a Fragment-Based in Silico Profiler for Michael Addition Thiol Reactivity

The Adverse Outcome Pathway (AOP) paradigm details the existing knowledge that links the initial interaction between a chemical and a biological system, termed the molecular initiating event (MIE), through a series of intermediate events, to an adverse effect. An important example of a well-defined MIE is the formation of a covalent bond between a biological nucleophile and an electrophilic compound. This particular MIE has been associated with various toxicological end points such as acute aquatic toxicity, skin sensitization, and respiratory sensitization. This study has investigated the calculated parameters that are required to predict the rate of chemical bond formation (reactivity) of a dataset of Michael acceptors. Reactivity of these compounds toward glutathione was predicted using a combination of a calculated activation energy value (Eact, calculated using density functional theory (DFT) calculation at the B3YLP/6-31G+(d) level of theory, and solvent-accessible surface area values (SAS) at the α carbon. To further develop the method, a fragment-based algorithm was developed enabling the reactivity to be predicted for Michael acceptors without the need to perform the time-consuming DFT calculations. Results showed the developed fragment method was successful in predicting the reactivity of the Michael acceptors excluding two sets of chemicals: volatile esters with an extended substituent at the β-carbon and chemicals containing a conjugated benzene ring as part of the polarizing group. Additionally the study also demonstrated the ease with which the approach can be extended to other chemical classes by the calculation of additional fragments and their associated Eact and SAS values. The resulting method is likely to be of use in regulatory toxicology tools where an understanding of covalent bond formation as a potential MIE is important within the AOP paradigm

Novel approach for efficient predictions properties of large pool of nanomaterials based on limited set of species: nano-read-across

Creating suitable chemical categories and developing read-across methods, supported by quantum mechanical calculations, can be an effective solution to solving key problems related to current scarcity of data on the toxicity of various nanoparticles. This study has demonstrated that by applying a nano-read-across, the cytotoxicity of nano-sized metal oxides could be estimated with a similar level of accuracy as provided by quantitative structure-activity relationship for nanomaterials (nano-QSAR model(s)). The method presented is a suitable computational tool for the preliminary hazard assessment of nanomaterials. It also could be used for the identification of nanomaterials that may pose potential negative impact to human health and the environment. Such approaches are especially necessary when there is paucity of relevant and reliable data points to develop and validate nano-QSAR model