Enhancing developmental and reproductive toxicity knowledge: A new AOP stemming from glutathione depletion.

Integrated approaches to testing and assessments (IATAs) have been proposed as a method to organise new approach methodologies in order to replace traditional animal testing for chemical safety assessments. To capture the mechanistic aspects of toxicity assessments, IATAs can be framed around the adverse outcome pathway (AOP) concept. To utilise AOPs fully in this context, a sufficient number of pathways need to be present to develop fit for purpose IATAs. In silico approaches can support IATA through the provision of predictive models and also through data integration to derive conclusions using a weight-of-evidence approach. To examine the maturity of a developmental and reproductive toxicity (DART) AOP network derived from the literature, an assessment of its coverage was performed against a novel toxicity dataset. A dataset of diverse compounds, with data from studies performed according to OECD test guidelines TG-421 and TG-422, was curated to test the performance of an in silico model based on the AOP network - allowing for the identification of knowledge gaps within the network. One such gap in the knowledge was filled through the development of an AOP stemming from the molecular initiating event 'glutathione reaction with an electrophile' leading to male fertility toxicity. The creation of the AOP provided the mechanistic rationale for the curation of pre-existing structural alerts to relevant key events. Integrating this new knowledge and associated alerts into the DART AOP network will improve its coverage of DART-relevant chemical space. In addition, broadening the coverage of AOPs for a particular regulatory endpoint may facilitate the development of, and confidence in, robust IATAs.

Enhancing global and local decision making for chemical safety assessments through increasing the availability of data.

Toxicity safety assessments are a fundamental part of the lifecycle of products and aim to protect human health and the environment from harmful exposures to chemical substances. To make decisions regarding the suitability of testing strategies, the applicability of individual tests or concluding an assessment for an individual chemical requires data. This review outlines how different forms of data sharing, from enhancing publicly-available data to extracting knowledge from commercially-sensitive data, leads to increased quantity and quality of evidence being available for safety assessors to review. This can result in more confident decisions for different use cases in the context of chemical safety assessments. Although a number of challenges remain with progressing the evolution of toxicity safety assessments, data sharing should be considered as a key approach to accelerating the development and uptake of new best practices.

Using adverse outcome pathways to contextualise (Q)SAR predictions for reproductive toxicity - A case study with aromatase inhibition.

The development and application of (quantitative) structure-activity relationship ((Q)SAR) models for reproductive toxicology remains challenging, given the complexity of the endpoint and the risks associated with subsequent decision making. Adverse outcome pathways (AOPs) organise knowledge and provide context of model outputs, aiding risk assessors' decision making. Using aromatase as an example, we demonstrate how AOPs can be used to contextualise a variety of (Q)SAR approaches. AOPs stemming from aromatase inhibition - leading to adverse outcomes of regulatory significance - were synthesised and annotated with relevant assays, assay data and (Q)SAR models. The resulting framework enabled the deployment of different types of (Q)SAR models that predict for key events along the pathway. The use of models for molecular initiating events enables relevant knowledge to span a wider area of chemical space - compared to using models trained solely on in vivo toxicity data. Utilising such methods, alongside additional assay data and exposure information, could lead to improved risk assessment strategies during compound prioritisation and labelling.

Beyond adverse outcome pathways: making toxicity predictions from event networks, SAR models, data and knowledge.

Adverse outcome pathways have shown themselves to be useful ways of understanding and expressing knowledge about sequences of events that lead to adverse outcomes (AOs) such as toxicity. In this paper we use the building blocks of adverse outcome pathways-namely key events (KEs) and key event relationships-to construct networks which can be used to make predictions of the likelihood of AOs. The networks of KEs are augmented by data from and knowledge about assays as well as by structure activity relationship predictions linking chemical classes to the observation of KEs. These inputs are combined within a reasoning framework to produce an information-rich display of the relevant knowledge and data and predictions of AOs both in the abstract case and for individual chemicals. Illustrative examples are given for skin sensitization, reprotoxicity and non-genotoxic carcinogenicity.

The importance of expert review to clarify ambiguous situations for (Q)SAR predictions under ICH M7.

The use of in silico predictions for the assessment of bacterial mutagenicity under the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) M7 guideline is recommended when two complementary (quantitative) structure-activity relationship (Q)SAR models are used. Using two systems may increase the sensitivity and accuracy of predictions but also increases the need to review predictions, particularly in situations where results disagree. During the 4th ICH M7/QSAR Workshop held during the Joint Meeting of the 6th Asian Congress on Environmental Mutagens (ACEM) and the 48th Annual Meeting of the Japanese Environmental Mutagen Society (JEMS) 2019, speakers demonstrated their approaches to expert review using 20 compounds provided ahead of the workshop that were expected to yield ambiguous (Q)SAR results. Dr. Chris Barber presented a selection of the reviews carried out using Derek Nexus and Sarah Nexus provided by Lhasa Limited. On review of these compounds, common situations were recognised and are discussed in this paper along with standardised arguments that may be used for such scenarios in future.

Important considerations for the validation of QSAR models for in vitro mutagenicity.

While high-level performance metrics generated from the validation of quantitative structure-activity relationship (QSAR) systems can provide valuable information on how well these models perform and where they need to be improved, they require appropriate interpretation. There is no universal performance metric which will answer all of the questions a user might ask relating to a model, and therefore, a combination of metrics should usually be considered. Furthermore, results may vary according to the chemical space being used to validate a model, and, in some cases, it may be the validation data which is lacking or ambiguous rather than the prediction being made. Finally, users also need to consider the interpretability of the predictions being made, alongside the accuracy of the predictions. In this paper, we will discuss these important considerations in more detail within the context of the results obtained at Lhasa Limited as part of the National Institute of Health Sciences (NIHS) QSAR challenge project.

A Semi-automated Approach to Create Purposeful Mechanistic Datasets from Heterogeneous Data: Data Mining Towards the in silico Predictions for Oestrogen Receptor Modulation and Teratogenicity.

The need to find an alternative to costly animal studies for developmental and reproductive toxicity testing has shifted the focus considerably to the assessment of in vitro developmental toxicology models and the exploitation of pharmacological data for relevant molecular initiating events. We hereby demonstrate how automation can be applied successfully to handle heterogeneous oestrogen receptor data from ChEMBL. Applying expert-derived thresholds to specific bioactivities allowed an activity call to be attributed to each data entry. Human intervention further improved this mechanistic dataset which was mined to develop structure-activity relationship alerts and an expert model covering 45 chemical classes for the prediction of oestrogen receptor modulation. The evaluation of the model using FDA EDKB and Tox21 data was quite encouraging. This model can also provide a teratogenicity prediction along with the additional information it provides relevant to the query compound, all of which will require careful assessment of potential risk by experts.

Carbamates and ICH M7 classification: Making use of expert knowledge.

Carbamates are widely used in the chemical industry so understanding their toxicity is important to safety assessment. Carbamates have been associated with certain toxicities resulting in publication of structural alerts, including alerts for mutagenicity. Structural alerts for bacterial mutagenicity can be used in combination with statistical systems to enable ICH M7 classification, which allows assessment of the genotoxic risk posed by pharmaceutical impurities. This study tested a hypothetical bacterial mutagenicity alert for carbamates and examined the impact it would have on ICH M7 classifications using (Q)SAR predictions from the expert rule-based system Derek Nexus and the statistical-based system Sarah Nexus. Public datasets have a low prevalence of mutagenic carbamates, which highlighted that systems containing an alert for carbamates perform poorly for achieving correct ICH M7 classifications. Carbamates are commonly used as protecting groups and proprietary datasets containing such compounds were also found to have a low prevalence of mutagenic compounds. Expert review of the mutagenic compounds established that mutagenicity was often only observed under certain (non-standard) conditions and more generally that the Ames test may be a poor predictor for the risk of carcinogenicity posed by chemicals in this class. Overall a structural alert for the in vitro bacterial mutagenesis of carbamates does not benefit workflows for assigning ICH M7 classification to impurities.

Design, Synthesis, and Evaluation of Triazole Derivatives That Induce Nrf2 Dependent Gene Products and Inhibit the Keap1-Nrf2 Protein-Protein Interaction.

The transcription factor Nrf2 regulates the expression of a large network of cytoprotective and metabolic enzymes and proteins. Compounds that directly and reversibly inhibit the interaction between Nrf2 and its main negative regulator Keap1 are potential pharmacological agents for a range of disease types including neurodegenerative conditions and cancer. We describe the development of a series of 1,4-diphenyl-1,2,3-triazole compounds that inhibit the Nrf2-Keap1 protein-protein interaction (PPI) in vitro and in live cells and up-regulate the expression of Nrf2-dependent gene products.

PMI: a ΔΨm independent pharmacological regulator of mitophagy.

Mitophagy is central to mitochondrial and cellular homeostasis and operates via the PINK1/Parkin pathway targeting mitochondria devoid of membrane potential (ΔΨm) to autophagosomes. Although mitophagy is recognized as a fundamental cellular process, selective pharmacologic modulators of mitophagy are almost nonexistent. We developed a compound that increases the expression and signaling of the autophagic adaptor molecule P62/SQSTM1 and forces mitochondria into autophagy. The compound, P62-mediated mitophagy inducer (PMI), activates mitophagy without recruiting Parkin or collapsing ΔΨm and retains activity in cells devoid of a fully functional PINK1/Parkin pathway. PMI drives mitochondria to a process of quality control without compromising the bio-energetic competence of the whole network while exposing just those organelles to be recycled. Thus, PMI circumvents the toxicity and some of the nonspecific effects associated with the abrupt dissipation of ΔΨm by ionophores routinely used to induce mitophagy and represents a prototype pharmacological tool to investigate the molecular mechanisms of mitophagy.

(Nitrato-kappa2O,O')bis(triethanolamine-kappa4N,O,O',O'')lanthanum(III) dinitrate.

The title compound, [La(NO3)(C6H15NO3)2](NO3)2, contains a network of [La(NO3)(C6H15NO3)2]2+ cations and nitrate counter-ions. The crystal packing is influenced by cation-to-anion O-H...O hydrogen bonds, resulting in a structure with one-dimensional character. The ten-coordinate La atom and a nitrate anion have site symmetry 2. The fact that triethanolamine can bind to such diverse cations as Li+ and La3+ militates against possible applications that require selective binding of ligand to metal.