Geoff Hodges

The Role of Omics in the Application of Adverse Outcome Pathways for Chemical Risk Assessment

Abstract In conjunction with the second International Environmental Omics Symposium (iEOS) conference, held at the University of Liverpool (United Kingdom) in September 2014, a workshop was held to bring together experts in toxicology and regulatory science from academia, government and industry. The purpose of the workshop was to review the specific roles that high-content omics datasets (eg, transcriptomics, metabolomics, lipidomics, and proteomics) can hold within the adverse outcome pathway (AOP) framework for supporting ecological and human health risk assessments. In light of the growing number of examples of the application of omics data in the context of ecological risk assessment, we considered how omics datasets might continue to support the AOP framework. In particular, the role of omics in identifying potential AOP molecular initiating events and providing supportive evidence of key events at different levels of biological organization and across taxonomic groups was discussed. Areas with potential for short and medium-term breakthroughs were also discussed, such as providing mechanistic evidence to support chemical read-across, providing weight of evidence information for mode of action assignment, understanding biological networks, and developing robust extrapolations of species-sensitivity. Key challenges that need to be addressed were considered, including the need for a cohesive approach towards experimental design, the lack of a mutually agreed framework to quantitatively link genes and pathways to key events, and the need for better interpretation of chemically induced changes at the molecular level. This article was developed to provide an overview of ecological risk assessment process and a perspective on how high content molecular-level datasets can support the future of assessment procedures through the AOP framework.

Advancing the adverse outcome pathway framework—An international horizon scanning approach

Our ability to conduct whole-organism toxicity tests to understand chemical safety has been outpaced by the synthesis of new chemicals for a wide variety of commercial applications. As a result, scientists and risk assessors are turning to mechanistically based studies to increase efficiencies in chemical risk assessment and making greater use of in vitro and in silico methods to evaluate potential environmental and human health hazards. In this context, the adverse outcome pathway (AOP) framework has gained traction in regulatory science because it offers an efficient and effective means for capturing available knowledge describing the linkage between mechanistic data and the apical toxicity end points required for regulatory assessments. A number of international activities have focused on AOP development and various applications to regulatory decision-making. These initiatives have prompted dialogue between research scientists and regulatory communities to consider how best to use the AOP framework. Although expert-facilitated discussions and AOP development have been critical in moving the science of AOPs forward, it was recognized that a survey of the broader scientific and regulatory communities would aid in identifying current limitations while guiding future initiatives for the AOP framework. To that end, a global horizon scanning exercise was conducted to solicit questions concerning the challenges or limitations that must be addressed to realize the full potential of the AOP framework in research and regulatory decision-making. The questions received fell into several broad topical areas: AOP networks, quantitative AOPs, collaboration on and communication of AOP knowledge, AOP discovery and development, chemical and cross-species extrapolation, exposure/toxicokinetics considerations, and AOP applications. Expert ranking was then used to prioritize questions for each category, where 4 broad themes emerged that could help inform and guide future AOP research and regulatory initiatives. In addition, frequently asked questions were identified and addressed by experts in the field. Answers to frequently asked questions will aid in addressing common misperceptions and will allow for clarification of AOP topics. The need for this type of clarification was highlighted with surprising frequency by our question submitters, indicating that improvements are needed in communicating the AOP framework among the scientific and regulatory communities. Overall, horizon scanning engaged the global scientific community to help identify key questions surrounding the AOP framework and guide the direction of future initiatives. Environ Toxicol Chem 2017;36:1411-1421.

Database of published retention factors for immobilized artificial membrane HPLC and an assessment of the effect of experimental variability

A database was collated of published experimental logarithmic values for the relative retention factors (log k(IAM)) measured using an immobilized artificial membrane column and high-performance liquid chromatography (IAM HPLC). Log k(IAM) is an alternative measure of hydrophobicity to the octanol/water partition coefficient (log K(OW)). While there are several accepted methods to measure log K(OW), no standardized method exists to determine log k(IAM). The database of collated log k(IAM) values includes 13 key experimental parameters and contains 1,686 values for 555 compounds, which are predominantly polar organic compounds and include drug molecules and surfactants. These compounds are acidic, basic, and neutral and both ionized and un-ionized under the conditions of analysis. The data compiled demonstrated experimental variability for each experimental parameter considered, including column stationary phase, pH, temperature, and mobile phase. Reducing the experimental variability allowed for greater consistency in the datasets.

Aquatic toxicity of cationic surfactants to Daphnia magna

Quantitative structure–activity relationship (QSAR) modelling of aquatic toxicity for cationic surfactants has received limited attention despite the fact that surfactants of this type are generally more toxic than predicted by general narcosis or polar narcosis equations. Here we report measurement of log P for three types of aromatic quaternary ammonium halides at sub-micellar concentrations, refinement of earlier rules for log P calculation, and development of a hydrophobicity based QSAR, using both calculated and measured log P values, for the aquatic toxicity of quaternary ammonium halides to Daphnia magna. The QSAR for cationics has a substantially larger intercept than the log P-based QSARs for nonionic and anionic surfactants. This is rationalised in terms of the head group interactions with membrane phospholipid in a two-dimensional partitioning model. The effect of the positive nitrogen on the log P contributions of methylene groups along alkyl chains varies, depending on the other groups bonded to the positive nitrogen. We propose a mechanistic explanation, but until these effects can be put on a more predictable quantitative basis it is recommended that, for quaternaries other than the three types discussed here, calculated log P values should not be relied on and experimental values should be determined, e.g. for prediction of toxicity by the QSAR equation reported here.

Ecotoxicological thresholds—practical application to an industrial inventory

The concept of thresholds of toxicological concern as a potentially useful tool in environmental risk assessment has been applied to the inventory of a home and personal care products company to derive a series of chemical class-based ecotoxicological threshold of concern (ecoTTC) values. Cationic chemicals of various types show notably higher toxicity than other classes and should be treated separately. Despite this, the ecoTTC for the full data set in the present study is only slightly lower than that derived previously for chemicals causing toxicity via Verhaar modes of action (MoAs) 1 to 3. Exclusion of cationic chemicals resulted in an ecoTTC value slightly higher than the MoA 1 to 3 value. These observations indicate that such data sets contain few specifically acting chemicals. The applicability of threshold approaches in environmental risk assessment has been extended to include a limited number of inorganic/organometallic chemicals, polymers, and all classes of surfactants. The use of such ecoTTC values in conjunction with mode of action-based quantitative structure-activity relationships will allow the efficient screening and prioritization of large inventories of heterogeneous chemicals, focusing resources on those chemicals that require additional information to better understand any potential risk.

Harvesting the promise of AOPs: An assessment and recommendations

Highlights • The AOP framework aims to increase efficiency of chemical safety assessments.• The stakeholder community for AOPs, however, is broader than chemical risk assessors.• There are scientific and social challenges to successfully engage all stakeholders.• Multi-faceted communication and governance strategies will address these challenges.

Invertebrate Model Species in AOP Development

In this chapter, we present the use of invertebrate model species in the development of adverse outcome pathways (AOPs), its challenges, and the current state of invertebrate toxicity studies. Invertebrates can contribute significantly towards the development of robust AOPs, providing many advantages over the use of vertebrate species. This includes a generally shorter life cycle allowing for chronic and full life cycle toxicity tests, and a wide array of powerful molecular genetic tools such as genome sequences, genomic engineering including gene knock-outs, and comprehensive bioinformatics databases. Currently, the most robustly developed invertebrate model species for toxicity testing include Daphnia, Caenorhabditis elegans, plus members of the Drosophila genus. The potential use of these and other invertebrate organisms for assessing chemical risk for most animals (including vertebrate species) is evaluated via a comparative phylogenetic approach to ecotoxicological testing, seeking to discover the evolutionary origins and distribution of toxicity pathways across the internal branches of the animal phylogeny. Comparative –omics data from cellular and developmental studies suggest a high degree of conservation in regulatory pathways in fly, worm and human. By comparing –omics studies between vertebrates and invertebrate species in toxicology, we begin to also discover coherence in pathway level responses, indicating potentially numerous overlapping responses to specific stressors, even across species that have different physiologies and ecological niches. At present, only a small number of invertebrate AOPs are informed by evidence. Perhaps the most robust of these is the Acetylcholinesterase inhibition (AChE) AOP for pesticides. We present a case study of using the AOP framework for risk assessment and discuss how the use of models, such as those using Dynamic Energy Budget theory linked to populations, can enhance the use of AOPs for understanding and predicting chemical risk.

Prediction of immobilised artificial membrane chromatography retention factors using theoretical molecular fragments and structural features

Many in silico alternatives to aquatic toxicity tests rely on hydrophobicity-based quantitative structure–activity relationships (QSARs). Hydrophobicity is often estimated as log P, where P is the octanol–water partition coefficient. Immobilised artificial membrane (IAM) high performance liquid chromatography (HPLC) may be a more biologically relevant alternative to log P. The aim of this study was to investigate the applicability of a theoretical structural fragment and feature-based method to predict log k IAM (the logarithm of the retention index determined by IAM–HPLC) values. This will allow the prediction of log k IAM based on chemical structure alone. The use of structural fragment values to predict log P was first proposed in the 1970s. The application of a similar method using fragment values to predict log k IAM is a novel approach. Values of log k IAM were determined for 22 aliphatic and 42 aromatic compounds using an optimised and robust IAM–HPLC assay. The method developed shows good predictive performance using leave-one-out cross validation and application to an external validation set not seen a priori by the training set also generated good predictive values. The ability to predict log k IAM without the need for practical measurement will allow for the increased use of QSARs based on this descriptor.