In Silico Models for Hepatotoxicity.

In this chapter, we review the state of the art of predicting human hepatotoxicity using in silico techniques. There has been significant progress in this area over the past 20 years but there are still some challenges ahead. Principally, these challenges are our partial understanding of a very complex biochemical system and our ability to emulate that in a predictive capacity. Here, we provide an overview of the published modeling approaches in this area to date and discuss their design, strengths and weaknesses. It is interesting to note the diversity in modeling approaches, whether they be statistical algorithms or evidenced-based approaches including structural alerts and pharmacophore models. Irrespective of modeling approach, it appears a common theme of access to appropriate, relevant, and high-quality data is a limitation to all and is likely to continue to be the focus of future research.

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.

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

Ensuring confidence in predictions: A scheme to assess the scientific validity of in silico models.

The use of in silico tools within the drug development process to predict a wide range of properties including absorption, distribution, metabolism, elimination and toxicity has become increasingly important due to changes in legislation and both ethical and economic drivers to reduce animal testing. Whilst in silico tools have been used for decades there remains reluctance to accept predictions based on these methods particularly in regulatory settings. This apprehension arises in part due to lack of confidence in the reliability, robustness and applicability of the models. To address this issue we propose a scheme for the verification of in silico models that enables end users and modellers to assess the scientific validity of models in accordance with the principles of good computer modelling practice. We report here the implementation of the scheme within the Innovative Medicines Initiative project "eTOX" (electronic toxicity) and its application to the in silico models developed within the frame of this project.

A review of the use of in silico methods to predict the chemistry of molecular initiating events related to drug toxicity.

INTRODUCTION: Drug toxicity pathways can be extremely complex and difficult to fully understand. However, understanding specific parts of the pathway may be simpler. Every toxicity pathway starts with a molecular initiating event (MIE). If an MIE is well understood then it becomes possible to predict which compounds can partake in that particular MIE using in silico techniques. AREAS COVERED: This review aims to describe how the use of structural alerts and the measurement/calculation of certain physicochemical properties can identify chemicals with a given MIE. For example, structural alerts can be used to identify chemicals able to form a covalent bond with a biological macromolecule. How chemistry-related MIEs relate to toxicity end points, such as hepatotoxicity, is also discussed. EXPERT OPINION: It is emphasised that predicting that a compound can cause an MIE is not a direct prediction of toxicity. Predicting whether a compound will be toxic requires a comparison with similar compounds which cause the same MIE and that are associated with known toxicological data. It is possible to form categories of compounds that are all thought to act via the same MIE and then use read-across within the category to make a toxicity prediction.

The use of a chemistry-based profiler for covalent DNA binding in the development of chemical categories for read-across for genotoxicity.

An important molecular initiating event for genotoxicity is the ability of a compound to bind covalently with DNA. However, not all compounds that can undergo covalent binding mechanisms will result in genotoxicity. One approach to solving this problem, when in silico prediction techniques are being used, is to develop tools that allow chemicals to be grouped into categories based on their ability to bind covalently to DNA. For this analysis to take place, compounds need to be placed within categories where the trend in toxicity can be explained by simple descriptors, such as hydrophobicity. However, this can occur only when the compounds within a category are structurally and mechanistically similar. Chemistry-based profilers have the ability to screen compounds and highlight those with similar structures to a target compound, and are thus likely to act via a similar mechanism of action. Here, examples are reported to highlight how structure-based profilers can be used to form categories and hence fill data gaps. The importance of developing a well-defined and robust category is discussed in terms of both mechanisms of action and structural similarity.

Using In Silico Tools in a Weight of Evidence Approach to Aid Toxicological Assessment.

Integrated testing strategies are an important and useful approach to reduce animal usage in toxicity testing. Increased usage of integrated testing strategies is foreseen in current chemical legislation, e.g. REACH. Skin sensitisation is a well studied endpoint and many in silico models have been developed for the prediction of the skin sensitising potential of chemicals. This paper discusses the use of the OECD (Q)SAR Application Toolbox, Derek for Windows, the CAESAR global model and SMARTS rules for reactivity within a weight of evidence approach to predict skin sensitisation. Conclusions drawn from a weight of evidence approach can be used within an integrated testing strategy to reduce the requirement for in vivo tests. Using all four models in this manner enabled 76% of the conclusive predictions made regarding the test data to be in agreement with the observed toxicities. In addition, using all four models in conjunction identified areas where further information is required, as confounding results were produced. The actual data requirements for an integrated testing strategy are discussed along with what considerations need to be made for the remaining compounds that were misclassified or for which the programs contradicted one another and a definitive conclusion could not be reached.

Definition of the applicability domains of knowledge-based predictive toxicology expert systems by using a structural fragment-based approach.

The applicability domain of a (quantitative) structure-activity relationship ([Q]SAR) must be defined, if a model is to be used successfully for toxicity prediction, particularly for regulatory purposes. Previous efforts to set guidelines on the definition of applicability domains have often been biased toward quantitative, rather than qualitative, models. As a result, novel techniques are still required to define the applicability domains of structural alert models and knowledge-based systems. By using Derek for Windows as an example, this study defined the domain for the skin sensitisation structural alert rule-base. This was achieved by fragmenting the molecules within a training set of compounds, then searching the fragments for those created from a test compound. This novel method was able to highlight test chemicals which differed from those in the training set. The information was then used to designate chemicals as being either within or outside the domain of applicability for the structural alert on which that training set was based.