Mechanism-based QSAR modeling of skin sensitization

Many chemicals can induce skin sensitization, and there is a pressing need for non-animal methods to give a quantitative indication of potency. Using two large published data-sets of skin sensitizers, we have allocated each sensitizing chemical to one of ten mechanistic categories, and then developed good QSAR models for the seven categories with a sufficient number of chemicals to allow modeling. Both internal and external validation checks showed that each model had good predictivity

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

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

Adverse Outcome Pathway (AOP) Informed Modeling of Aquatic Toxicology: QSARs, Read-Across, and Interspecies Verification of Modes of Action.

Alternative approaches have been promoted to reduce the number of vertebrate and invertebrate animals required for the assessment of the potential of compounds to cause harm to the aquatic environment. A key philosophy in the development of alternatives is a greater understanding of the relevant adverse outcome pathway (AOP). One alternative method is the fish embryo toxicity (FET) assay. Although the trends in potency have been shown to be equivalent in embryo and adult assays, a detailed mechanistic analysis of the toxicity data has yet to be performed; such analysis is vital for a full understanding of the AOP. The research presented herein used an updated implementation of the Verhaar scheme to categorize compounds into AOP-informed categories. These were then used in mechanistic (quantitative) structure-activity relationship ((Q)SAR) analysis to show that the descriptors governing the distinct mechanisms of acute fish toxicity are capable of modeling data from the FET assay. The results show that compounds do appear to exhibit the same mechanisms of toxicity across life stages. Thus, this mechanistic analysis supports the argument that the FET assay is a suitable alternative testing strategy for the specified mechanisms and that understanding the AOPs is useful for toxicity prediction across test systems

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

Proposal of an in silico profiler for categorisation of repeat dose toxicity data of hair dyes

This study outlines the analysis of repeat dose toxicity data taken from Scientific Committee on Consumer Safety (SCCS) opinions for commonly used hair dyes in the European Union. Structural similarity was applied to group these chemicals into categories. Subsequent mechanistic analysis suggested that toxicity to mitochondria is potentially a key driver of repeat dose toxicity for chemicals within each of the categories. The mechanistic hypothesis allowed for an in silico profiler consisting of mechanism-based structural alerts to be proposed. This in silico profiler is intended for grouping chemicals into mechanism-based categories within the Adverse Outcome Pathway paradigm

A Review of In Silico Tools as Alternatives to Animal Testing: Principles, Resources and Applications

Across the spectrum of industrial sectors, including pharmaceuticals, chemicals, personal care products, food additives and their associated regulatory agencies, there is a need to develop robust and reliable methods to reduce or replace animal testing. It is generally recognised that no single alternative method will be able to provide a one-to-one replacement for assays based on more complex toxicological endpoints. Hence, information from a combination of techniques is required. A greater understanding of the time and concentration-dependent mechanisms, underlying the interactions between chemicals and biological systems, and the sequence of events that can lead to apical effects, will help to move forward the science of reducing and replacing animal experiments. In silico modelling, in vitro assays, high-throughput screening, organ-on-a-chip technology, omics and mathematical biology, can provide complementary information to develop a complete picture of the potential response of an organism to a chemical stressor. Adverse outcome pathways (AOPs) and systems biology frameworks enable relevant information from diverse sources to be logically integrated. While individual researchers do not need to be experts across all disciplines, it is useful to have a fundamental understanding of what other areas of science have to offer, and how knowledge can be integrated with other disciplines. The purpose of this review is to provide those who are unfamiliar with predictive in silico tools, with a fundamental understanding of the underlying theory. Current applications, software, barriers to acceptance, new developments and the use of integrated approaches are all discussed, with additional resources being signposted for each of the topics

Development of a decision tree for mitochondrial dysfunction: Uncoupling of oxidative phosphorylation

Mitochondrial dysfunction is the result of a number of process 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 physico-chemical boundaries identified from a detailed study of the literature. The physico-chemical boundaries define a chemical relationship with both log P and pKa. 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 being for the grouping of chemicals into categories for chronic human health and environmental toxicological endpoints

Modelling changes in glutathione homeostasis as a function of quinone redox metabolism

Redox cycling is an understated mechanism of toxicity associated with a plethora of xenobiotics, responsible for preventing the effective treatment of serious conditions such as malaria and cardiomyopathy. Quinone compounds are notorious redox cyclers, present in drugs such as doxorubicin, which is used to treat a host of human cancers. However, the therapeutic index of doxorubicin is undermined by dose-dependent cardiotoxicity, which may be a function of futile redox cycling. In this study, a doxorubicin-specific in silico quinone redox metabolism model is described. Doxorubicin-GSH adduct formation kinetics are thermodynamically estimated from 26 its reduction potential, while the remainder of the model is parameterised using oxygen consumption rate data, indicative of hydroquinone auto oxidation. The model is then combined with a comprehensive glutathione metabolism model, facilitating the simulation of quinone redox cycling, and adduct-induced GSH depletion. Simulations suggest that glutathione pools are most sensitive to exposure duration at pharmacologically and supra-pharmacologically relevant doxorubicin concentrations. The model provides an alternative method of investigating and quantifying redox cycling induced oxidative stress, circumventing the experimental difficulties of measuring and tracking radical species. This in silico framework provides a platform from which GSH depletion can be explored as a function of a compound’s physicochemical properties