Guidance for good practice in the application of machine learning in development of toxicological quantitative structure-activity relationships (QSARs).

Recent years have seen a substantial growth in the adoption of machine learning approaches for the purposes of quantitative structure-activity relationship (QSAR) development. Such a trend has coincided with desire to see a shifting in the focus of methodology employed within chemical safety assessment: away from traditional reliance upon animal-intensive in vivo protocols, and towards increased application of in silico (or computational) predictive toxicology. With QSAR central amongst techniques applied in this area, the emergence of algorithms trained through machine learning with the objective of toxicity estimation has, quite naturally, arisen.

Making in silico predictive models for toxicology FAIR.

In silico predictive models for toxicology include quantitative structure-activity relationship (QSAR) and physiologically based kinetic (PBK) approaches to predict physico-chemical and ADME properties, toxicological effects and internal exposure. Such models are used to fill data gaps as part of chemical risk assessment. There is a growing need to ensure in silico predictive models for toxicology are available for use and that they are reproducible.

Probabilistic modelling of developmental neurotoxicity based on a simplified adverse outcome pathway network.

In a century where toxicology and chemical risk assessment are embracing alternative methods to animal testing, there is an opportunity to understand the causal factors of neurodevelopmental disorders such as learning and memory disabilities in children, as a foundation to predict adverse effects. New testing paradigms, along with the advances in probabilistic modelling, can help with the formulation of mechanistically-driven hypotheses on how exposure to environmental chemicals could potentially lead to developmental neurotoxicity (DNT). This investigation aimed to develop a Bayesian hierarchical model of a simplified AOP network for DNT.

Determination of "fitness-for-purpose" of quantitative structure-activity relationship (QSAR) models to predict (eco-)toxicological endpoints for regulatory use.

In silico models are used to predict toxicity and molecular properties in chemical safety assessment, gaining widespread regulatory use under a number of legislations globally. This study has rationalised previously published criteria to evaluate quantitative structure-activity relationships (QSARs) in terms of their uncertainty, variability and potential areas of bias, into ten assessment components, or higher level groupings. The components have been mapped onto specific regulatory uses (i.

Mapping Ligand-Shape Space for Protein-Ligand Systems: Distinguishing Key-in-Lock and Hand-in-Glove Proteins.

Many of the recently developed methods to study the shape of molecules permit one conformation of one molecule to be compared to another conformation of the same or a different molecule: a relative shape. Other methods provide an absolute description of the shape of a conformation that does not rely on comparisons or overlays. Any absolute description of shape can be used to generate a self-organizing map (shape map) that places all molecular shapes relative to one another; in the studies reported here, the shape fingerprint and ultrafast shape recognition methods are employed to create such maps.

A Review of 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.

Comparison of the predictive nature of the Genomic Allergen Rapid Detection (GARD) assay with mammalian assays in determining the skin sensitisation potential of agrochemical active ingredients.

Alternatives to mammalian testing are highly desirable to predict the skin sensitisation potential of agrochemical active ingredients (AI). The GARD assay, a stimulated, dendritic cell-like, cell line measuring genomic signatures, was evaluated using twelve AIs (seven sensitisers and five non-sensitisers) and the results compared with historical results from guinea pig or local lymph node assay (LLNA) studies. Initial GARD results suggested 11/12 AIs were sensitisers and six concurred with mammalian data.

Quantitative adverse outcome pathway (qAOP) models for toxicity prediction.

The quantitative adverse outcome pathway (qAOP) concept is gaining interest due to its potential regulatory applications in chemical risk assessment. Even though an increasing number of qAOP models are being proposed as computational predictive tools, there is no framework to guide their development and assessment. As such, the objectives of this review were to: (i) analyse the definitions of qAOPs published in the scientific literature, (ii) define a set of common features of existing qAOP models derived from the published definitions, and (iii) identify and assess the existing published qAOP models and associated software tools.

Development and analysis of an adverse outcome pathway network for human neurotoxicity.

An adverse outcome pathway (AOP) network is an attempt to represent the complexity of systems toxicology. This study illustrates how an AOP network can be derived and analysed in terms of its topological features to guide research and support chemical risk assessment. A four-step workflow describing general design principles and applied design principles was established and implemented.

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.

A Mechanistic Framework for Integrating Chemical Structure and High-Throughput Screening Results to Improve Toxicity Predictions.

Adverse Outcome Pathways (AOPs) establish a connection between a molecular initiating event (MIE) and an adverse outcome. Detailed understanding of the MIE provides the ideal data for determining chemical properties required to elicit the MIE. This study utilized high-throughput screening data from the ToxCast program, coupled with chemical structural information, to generate chemical clusters using three similarity methods pertaining to nine MIEs within an AOP network for hepatic steatosis.

A critical review of adverse effects to the kidney: mechanisms, data sources, and in silico tools to assist prediction.

The kidney is a major target for toxicity elicited by pharmaceuticals and environmental pollutants. Standard testing which often does not investigate underlying mechanisms has proven not to be an adequate hazard assessment approach. As such, there is an opportunity for the application of computational approaches that utilize multiscale data based on the Adverse Outcome Pathway (AOP) paradigm, coupled with an understanding of the chemistry underpinning the molecular initiating event (MIE) to provide a deep understanding of how structural fragments of molecules relate to specific mechanisms of nephrotoxicity.

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.

Turbocharging Matched Molecular Pair Analysis: Optimizing the Identification and Analysis of Pairs.

We have applied the two most commonly used methods for automatic matched pair identification, obtained the optimum settings, and discovered that the two methods are synergistic. A turbocharging approach to matched pair analysis is advocated in which a first round (a conservative categorical approach that uses an analogy with coin flips, heads corresponding to an increase in a measured property, tails to a decrease, and a biased coin to a structural change that reliably causes a change in that property) provides the settings for a second round (which uses the magnitude of the change in properties). Increased chemical specificity allows reliable knowledge to be extracted from smaller sets of pairs, and an assay-specific upper limit can be placed on the number of pairs required before adequate sampling of variability has been achieved.

Prediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects.

methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs) as well as grouping (category formation) allowing for read-across. A challenging area for modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself.

Validation of a Fragment-Based Profiler for Thiol Reactivity for the Prediction of Toxicity: Skin Sensitization and Tetrahymena pyriformis.

This study outlines the use of a recently developed fragment-based thiol reactivity profiler for Michael acceptors to predict toxicity toward Tetrahymena pyriformis and skin sensitization potency as determined in the Local Lymph Node Assay (LLNA). The results showed that the calculated reactivity parameter from the profiler, -log RC(calc), was capable of predicting toxicity for both end points with excellent statistics. However, the study highlighted the importance of a well-defined applicability domain for each end point.

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.

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.

Development of an in Silico Profiler for Mitochondrial Toxicity.

This study outlines the analysis of mitochondrial toxicity for a variety of pharmaceutical drugs extracted from Zhang et al. ((2009) Toxicol. In Vitro, 23, 134-140).

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.

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.

Methods for assigning confidence to toxicity data with multiple values--Identifying experimental outliers.

The assessment of data quality is a crucial element in many disciplines such as predictive toxicology and risk assessment. Currently, the reliability of toxicity data is assessed on the basis of testing information alone (adherence to Good Laboratory Practice (GLP), detailed testing protocols, etc.).

Towards a Fuzzy Expert System on Toxicological Data Quality Assessment.

Quality assessment (QA) requires high levels of domain-specific experience and knowledge. QA tasks for toxicological data are usually performed by human experts manually, although a number of quality evaluation schemes have been proposed in the literature. For instance, the most widely utilised Klimisch scheme1 defines four data quality categories in order to tag data instances with respect to their qualities; ToxRTool2 is an extension of the Klimisch approach aiming to increase the transparency and harmonisation of the approach.

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).

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.