Using membrane-water partition coefficients in a critical membrane burden approach to aid the identification of neutral and ionizable chemicals that induce acute toxicity below narcosis levels

The risk assessment of thousands of chemicals used in our society benefits from adequate grouping of chemicals based on the mode and mechanism of toxic action (MoA). We measure the phospholipid membrane-water distribution ratio (D_MLW) using a chromatographic assay (IAM-HPLC) for 121 neutral and ionized organic chemicals and screen other methods to derive D_MLW. We use IAM-HPLC based D_MLW as a chemical property to distinguish between baseline narcosis and specific MoA, for reported acute toxicity endpoints on two separate sets of chemicals. The first set comprised 94 chemicals of US EPA's acute fish toxicity database: 47 categorized as narcosis MoA, 27 with specific MoA, and 20 predominantly ionic chemicals with mostly unknown MoA. The narcosis MoA chemicals clustered around the median narcosis critical membrane burden (CMB_narc) of 140 mmol kg^-1 lipid, with a lower limit of 14 mmol kg^-1 lipid, including all chemicals labelled Narcosis_I and Narcosis_II. This maximum 'toxic ratio' (TR) between CMB_narc and the lower limit narcosis endpoint is thus 10. For 23/28 specific MoA chemicals a TR >10 was derived, indicative of a specific adverse effect pathway related to acute toxicity. For 10/12 cations categorized as "unsure amines", the TR <10 suggests that these affect fish via narcosis MoA. The second set comprised 29 herbicides, including 17 dissociated acids, and evaluated the TR for acute toxic effect concentrations to likely sensitive aquatic plant species (green algae and macrophytes Lemna and Myriophyllum), and non-target animal species (invertebrates and fish). For 21/29 herbicides, a TR >10 indicated a specific toxic mode of action other than narcosis for at least one of these aquatic primary producers. Fish and invertebrate TRs were mostly <10, particularly for neutral herbicides, but for acidic herbicides a TR >10 indicated specific adverse effects in non-target animals. The established critical membrane approach to derive the TR provides for useful contribution to the weight of evidence to bin a chemical as having a narcosis MoA or less likely to have acute toxicity caused by a more specific adverse effect pathway. After proper calibration, the chromatographic assay provides consistent and efficient experimental input for both neutral and ionizable chemicals to this approach.

Construction of an In Silico Structural Profiling Tool Facilitating Mechanistically Grounded Classification of Aquatic Toxicants

The performance of chemical safety assessment within the domain of environmental toxicology is often impeded by a shortfall of appropriate experimental data describing potential hazards across the many compounds in regular industrial use. In silico schemes for assigning aquatic-relevant modes or mechanisms of toxic action to substances, based solely on consideration of chemical structure, have seen widespread employment─including those of Verhaar, Russom, and later Bauer (MechoA). Recently, development of a further system was reported by Sapounidou, which, in common with MechoA, seeks to ground its classifications in understanding and appreciation of molecular initiating events. Until now, this Sapounidou scheme has not seen implementation as a tool for practical screening use. Accordingly, the primary purpose of this study was to create such a resource─in the form of a computational workflow. This exercise was facilitated through the formulation of 183 structural alerts/rules describing molecular features associated with narcosis, chemical reactivity, and specific mechanisms of action. Output was subsequently compared relative to that of the three aforementioned alternative systems to identify strengths and shortcomings as regards coverage of chemical space.

Data-driven learning of narcosis mode of action identifies a CNS transcriptional signature shared between whole organism Caenorhabditis elegans and a fish gill cell line

With the large numbers of man-made chemicals produced and released in the environment, there is a need to provide assessments on their potential effects on environmental safety and human health. Current regulatory frameworks rely on a mix of both hazard and risk-based approaches to make safety decisions, but the large number of chemicals in commerce combined with an increased need to conduct assessments in the absence of animal testing makes this increasingly challenging. This challenge is catalysing the use of more mechanistic knowledge in safety assessment from both in silico and in vitro approaches in the hope that this will increase confidence in being able to identify modes of action (MoA) for the chemicals in question. Here we approach this challenge by testing whether a functional genomics approach in C. elegans and in a fish cell line can identify molecular mechanisms underlying the effects of narcotics, and the effects of more specific acting toxicants. We show that narcosis affects the expression of neuronal genes associated with CNS function in C. elegans and in a fish cell line. Overall, we believe that our study provides an important step in developing mechanistically relevant biomarkers which can be used to screen for hazards, and which prevent the need for repeated animal or cross-species comparisons for each new chemical.

A scheme to evaluate structural alerts to predict toxicity - Assessing confidence by characterising uncertainties

Structure-activity relationships (SARs) in toxicology have enabled the formation of structural rules which, when coded as structural alerts, are essential tools in in silico toxicology. Whilst other in silico methods have approaches for their evaluation, there is no formal process to assess the confidence that may be associated with a structural alert. This investigation proposes twelve criteria to assess the uncertainty associated with structural alerts, allowing for an assessment of confidence. The criteria are based around the stated purpose, description of the chemistry, toxicology and mechanism, performance and coverage, as well as corroborating and supporting evidence of the alert. Alerts can be given a confidence assessment and score, enabling the identification of areas where more information may be beneficial. The scheme to evaluate structural alerts was placed in the context of various use cases for industrial and regulatory applications. The analysis of alerts, and consideration of the evaluation scheme, identifies the different characteristics an alert may have, such as being highly specific or generic. These characteristics may determine when an alert can be used for specific uses such as identification of analogues for read-across or hazard identification.

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Structural alerts are useful tools for predictive toxicology.

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12 criteria to evaluate structural alerts have been identified.

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A strategy to determine confidence of structural alerts is presented.

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Different use cases require different characteristics of structural alerts.

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A Scheme to Evaluate Structural Alerts to Predict Toxicity – Assessing Confidence By Characterising Uncertainties.

Development of an Enhanced Mechanistically Driven Mode of Action Classification Scheme for Adverse Effects on Environmental Species

This study developed a novel classification scheme to assign chemicals to a verifiable mechanism of (eco-)toxicological action to allow for grouping, read-across, and in silico model generation. The new classification scheme unifies and extends existing schemes and has, at its heart, direct reference to molecular initiating events (MIEs) promoting adverse outcomes. The scheme is based on three broad domains of toxic action representing nonspecific toxicity (e.g., narcosis), reactive mechanisms (e.g., electrophilicity and free radical action), and specific mechanisms (e.g., associated with enzyme inhibition). The scheme is organized at three further levels of detail beyond broad domains to separate out the mechanistic group, specific mechanism, and the MIEs responsible. The novelty of this approach comes from the reference to taxonomic diversity within the classification, transparency, quality of supporting evidence relating to MIEs, and that it can be updated readily.

Confidence in Inactive and Active Predictions from Structural Alerts

Having a measure of confidence in computational predictions of biological activity from in silico tools is vital when making predictions for new chemicals, for example, in chemical risk assessment. Where predictions of biological activity are used as an indicator of a potential hazard, false-negative predictions are the most concerning prediction; however, assigning confidence in inactive predictions is particularly challenging. How can one confidently identify the absence of activating features? In this study, we present methods for assigning confidence to both active and inactive predictions from structural alerts for protein-binding molecular initiating events (MIEs). Structural alerts were derived through an iterative statistical method. Confidence in the activity predictions is assigned by measuring the Tanimoto similarity between Morgan fingerprints of chemicals in the test set to relevant chemicals in the training set, and suitable cutoff values have been defined to give different confidence categories. To avoid a potential compound series bias in the test set and hence overestimate the performance of the method, we measured the biological activity of 27 compounds with 24 proteins, which gave us an additional 648 experimental measurements; many of the measurements are currently nonexistent in the literature and databases. This data set was complemented with newly measured biological activities published in ChEMBL25 and formed a combined independent validation data set. Applying the confidence categories to the computational predictions for the new data leads to the identification of chemicals for which one should be confident of either an inactive or active prediction, allowing model predictions to be used responsibly.

Neural network activation similarity: a new measure to assist decision making in chemical toxicology

Deep learning neural networks, constructed for the prediction of chemical binding at 79 pharmacologically important human biological targets, show extremely high performance on test data (accuracy 92.2 ± 4.2%, MCC 0.814 ± 0.093 and ROC-AUC 0.96 ± 0.04). A new molecular similarity measure, Neural Network Activation Similarity, has been developed, based on signal propagation through the network. This is complementary to standard Tanimoto similarity, and the combined use increases confidence in the computer's prediction of activity for new chemicals by providing a greater understanding of the underlying justification. The in silico prediction of these human molecular initiating events is central to the future of chemical safety risk assessment and improves the efficiency of safety decision making.

In Silico Guidance for In Vitro Androgen and Glucocorticoid Receptor ToxCast Assays

Molecular initiating events (MIEs) are key events in adverse outcome pathways that link molecular chemistry to target biology. As they are based on chemistry, these interactions are excellent targets for computational chemistry approaches to in silico modeling. In this work, we aim to link ligand chemical structures to MIEs for androgen receptor (AR) and glucocorticoid receptor (GR) binding using ToxCast data. This has been done using an automated computational algorithm to perform maximal common substructure searches on chemical binders for each target from the ToxCast dataset. The models developed show a high level of accuracy, correctly assigning 87.20% of AR binders and 96.81% of GR binders in a 25% test set using holdout cross-validation. The 2D structural alerts developed can be used as in silico models to predict these MIEs and as guidance for in vitro ToxCast assays to confirm hits. These models can target such experimental work, reducing the number of assays to be performed to gain required toxicological insight. Development of these models has also allowed some structural alerts to be identified as predictors for agonist or antagonist behavior at the receptor target. This work represents a first step in using computational methods to guide and target experimental approaches.

Structural Alerts and Random Forest Models in a Consensus Approach for Receptor Binding Molecular Initiating Events

A molecular initiating event (MIE) is the gateway to an adverse outcome pathway (AOP), a sequence of events ending in an adverse effect. In silico predictions of MIEs are a vital tool in a modern, mechanism-focused approach to chemical risk assessment. For 90 biological targets representing important human MIEs, structural alert-based models have been constructed with an automated procedure that uses Bayesian statistics to iteratively select substructures. These models give impressive average performance statistics (an average of 92% correct predictions across targets), significantly improving on previous models. Random Forest models have been constructed from physicochemical features for the same targets, giving similarly impressive performance statistics (93% correct predictions). A key difference between the models is interpretation of predictions-the structural alert models are transparent and easy to interpret, while Random Forest models can only identify the most important physicochemical features for making predictions. The two complementary models have been combined in a consensus model, improving performance compared to each individual model (94% correct predictions) and increasing confidence in predictions. Variation in model performance has been explained by calculating a modelability index (MODI), using Tanimoto coefficient between Morgan fingerprints to identify nearest neighbor chemicals. This work is an important step toward building confidence in the use of in silico tools for assessment of toxicity.

Quantitative Predictions for Molecular Initiating Events Using Three-Dimensional Quantitative Structure-Activity Relationships

The aim of human toxicity risk assessment is to determine a safe dose or exposure to a chemical for humans. This requires an understanding of the exposure of a person to a chemical and how much of the chemical is required to cause an adverse effect. To do this computationally, we need to understand how much of a chemical is required to perturb normal biological function in an adverse outcome pathway (AOP). The molecular initiating event (MIE) is the first step in an adverse outcome pathway and can be considered as a chemical interaction between a chemical toxicant and a biological molecule. Key chemical characteristics can be identified and used to model the chemistry of these MIEs. In this study, we do just this by using chemical substructures to categorize chemicals and 3D quantitative structure-activity relationships (QSARs) based on comparative molecular field analysis (CoMFA) to calculate molecular activity. Models have been constructed across a variety of human biological targets, the glucocorticoid receptor, mu opioid receptor, cyclooxygenase-2 enzyme, human ether-à-go-go related gene channel, and dopamine transporter. These models tend to provide molecular activity estimation well within one log unit and electronic and steric fields that can be visualized to better understand the MIE and biological target of interest. The outputs of these fields can be used to identify key aspects of a chemical's chemistry which can be changed to reduce its ability to activate a given MIE. With this methodology, the quantitative chemical activity can be predicted for a wide variety of MIEs, which can feed into AOP-based chemical risk assessments, and understanding of the chemistry behind the MIE can be gained.

Vision of a near future: Bridging the human health-environment divide. Toward an integrated strategy to understand mechanisms across species for chemical safety assessment

There is a growing recognition that application of mechanistic approaches to understand cross-species shared molecular targets and pathway conservation in the context of hazard characterization, provide significant opportunities in risk assessment (RA) for both human health and environmental safety. Specifically, it has been recognized that a more comprehensive and reliable understanding of similarities and differences in biological pathways across a variety of species will better enable cross-species extrapolation of potential adverse toxicological effects. Ultimately, this would also advance the generation and use of mechanistic data for both human health and environmental RA. A workshop brought together representatives from industry, academia and government to discuss how to improve the use of existing data, and to generate new NAMs data to derive better mechanistic understanding between humans and environmentally-relevant species, ultimately resulting in holistic chemical safety decisions. Thanks to a thorough dialogue among all participants, key challenges, current gaps and research needs were identified, and potential solutions proposed. This discussion highlighted the common objective to progress toward more predictive, mechanistically based, data-driven and animal-free chemical safety assessments. Overall, the participants recognized that there is no single approach which would provide all the answers for bridging the gap between mechanism-based human health and environmental RA, but acknowledged we now have the incentive, tools and data availability to address this concept, maximizing the potential for improvements in both human health and environmental RA.

Hg-supported phospholipid monolayer as rapid screening device for low molecular weight narcotic compounds in water

This study positions the fabricated Pt/Hg-supported phospholipid sensor element in the context of more conventional biomembrane-based screening platforms. The technology has been used together with immobilised artificial membrane (IAM) chromatography and COSMOmic simulation methods to screen the interaction of a series of low molecular weight narcotic organic compounds in water with phosphatidylcholine (PC) membranes. For these chemicals it is shown that toxicity to aquatic species is related to compound hydrophobicity which is associated with compound accumulation in the phospholipid membrane as modelled by IAM chromatography measurements and COSMOmic simulations. In contrast, the Hg-supported dioleoyl phosphatidylcholine (DOPC) sensor element records membrane damage/modification which is indirectly related to general toxicity and directly related to compound structure. Electrochemical limit of detection (LoD) values depend on molecular structure and range from 20 μmolL^-1 for substituted phenols to 23 mmolL^-1 for aliphatics. Rapid cyclic voltammetry (RCV) "fingerprints" showed that the major structural classes of compounds: alkyl/chlorobenzenes, substituted phenols, quaternary ammonium compounds and neutral amines interacted distinctively with the DOPC on Hg and that these observations correlated with and supported those predicted by the COSMOmic simulations of the compound/DMPC association. In addition, the compatibility of the electrochemical and COSMOmic methods validates the electrochemical device as a meaningful high throughput technology to screen compounds in water and report on the mechanistic details of their interaction with phospholipid layers.

Using 2D Structural Alerts to Define Chemical Categories for Molecular Initiating Events

Molecular initiating events (MIEs) are important concepts for in silico predictions. They can be used to link chemical characteristics to biological activity through an adverse outcome pathway (AOP). In this work, we capture chemical characteristics in 2D structural alerts, which are then used as models to predict MIEs. An automated procedure has been used to identify these alerts, and the chemical categories they define have been used to provide quantitative predictions for the activity of molecules that contain them. This has been done across a diverse group of 39 important pharmacological human targets using open source data. The alerts for each target combine into a model for that target, and these models are joined into a tool for MIE prediction with high average model performance (sensitivity = 82%, specificity = 93%, overall quality = 93%, Matthews correlation coefficient = 0.57). The result is substantially improved from our previous study (Allen, T. E. H., Goodman, J. M., Gutsell, S., and Russell, P. J. 2016. A history of the molecular initiating event. Chem. Res. Toxicol. 29, 2060-2070) for which the mean sensitivity for each target was only 58%. This tool provides the first step in an AOP-based risk assessment, linking chemical structure to toxicity endpoint.

Using Transition State Modeling To Predict Mutagenicity for Michael Acceptors

The Ames mutagenicity assay is a long established in vitro test to measure the mutagenicity potential of a new chemical used in regulatory testing globally. One of the key computational approaches to modeling of the Ames assay relies on the formation of chemical categories based on the different electrophilic compounds that are able to react directly with DNA and form a covalent bond. Such approaches sometimes predict false positives, as not all Michael acceptors are found to be Ames-positive. The formation of such covalent bonds can be explored computationally using density functional theory transition state modeling. We have applied this approach to mutagenicity, allowing us to calculate the activation energy required for α,β-unsaturated carbonyls to react with a model system for the guanine nucleobase of DNA. These calculations have allowed us to identify that chemical compounds with activation energies greater than or equal to 25.7 kcal/mol are not able to bind directly to DNA. This allows us to reduce the false positive rate for computationally predicted mutagenicity assays. This methodology can be used to investigate other covalent-bond-forming reactions that can lead to toxicological outcomes and learn more about experimental results.

In silico toxicology protocols

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information.

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

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.

Predicting the phospholipophilicity of monoprotic positively charged amines

The sorption affinity of eighty-six charged amine structures to phospholipid monolayers (log K_IAM) was determined using immobilized artificial membrane high-performance liquid chromatography (IAM-HPLC). The amine compounds covered the most prevalent types of polar groups, widely ranged in structural complexity, and included forty-seven pharmaceuticals, as well as several narcotics and pesticides. Amine type specific corrective increments were used to align log K_IAM data with bilayer membrane sorption coefficients (K_MW(IAM)). Using predicted sorption affinities of neutral amines, we evaluated the difference (scaling factor Δ_MW) with the measured log K_MW(IAM) for cationic amines. The Δ_MW values were highly variable, ranging from -2.37 to +2.3 log units. For each amine type, polar amines showed lower Δ_MW values than hydrocarbon based amines (C_xH_yN⁺). COSMOmic software was used to directly calculate the partitioning coefficient of ionic structures into a phospholipid bilayer (K_{DMPC-W,cation}), including quaternary ammonium compounds. The resulting root mean square error (RMSE) between log K_{DMPC-W,cation} and log K_MW(IAM) was 0.83 for all eighty-six polar amines, and 0.47 for sixty-eight C_xH_yN⁺ amines. The polar amines were then split into five groups depending on polarity and structural complexity, and corrective increments for each group were defined to improve COSMOmic predictions. Excluding only the group with sixteen complex amine structures (≥4 polar groups, M_w > 400, including several macrolide antibiotics), the resulting RMSE for corrected K_{DMPC-W,cation} values improved to 0.45 log units for the remaining set of 138 polar and C_xH_yN⁺ amines.

A History of the Molecular Initiating Event

The adverse outcome pathway (AOP) framework provides an alternative to traditional in vivo experiments for the risk assessment of chemicals. AOPs consist of a number of key events (KEs) linked by key event relationships across a range of biological organization backed by scientific evidence. The first KE in the pathway is the molecular initiating event (MIE)-the initial chemical trigger that starts an AOP. Over the past 3 years the AOP conceptual framework has gained a large amount of momentum in toxicology as an alternative to animal methods, and so the MIE has come into the spotlight. What is an MIE? How can MIEs be measured or predicted? What research is currently contributing to our understanding of MIEs? In this Perspective we outline answers to these key questions.

Adverse Outcome Pathways for Regulatory Applications: Examination of Four Case Studies With Different Degrees of Completeness and Scientific Confidence

Adverse outcome pathways (AOPs) offer a pathway-based toxicological framework to support hazard assessment and regulatory decision-making. However, little has been discussed about the scientific confidence needed, or how complete a pathway should be, before use in a specific regulatory application. Here we review four case studies to explore the degree of scientific confidence and extent of completeness (in terms of causal events) that is required for an AOP to be useful for a specific purpose in a regulatory application: (i) Membrane disruption (Narcosis) leading to respiratory failure (low confidence), (ii) Hepatocellular proliferation leading to cancer (partial pathway, moderate confidence), (iii) Covalent binding to proteins leading to skin sensitization (high confidence), and (iv) Aromatase inhibition leading to reproductive dysfunction in fish (high confidence). Partially complete AOPs with unknown molecular initiating events, such as 'Hepatocellular proliferation leading to cancer', were found to be valuable. We demonstrate that scientific confidence in these pathways can be increased though the use of unconventional information (eg, computational identification of potential initiators). AOPs at all levels of confidence can contribute to specific uses. A significant statistical or quantitative relationship between events and/or the adverse outcome relationships is a common characteristic of AOPs, both incomplete and complete, that have specific regulatory uses. For AOPs to be useful in a regulatory context they must be at least as useful as the tools that regulators currently possess, or the techniques currently employed by regulators.

Phospholipophilicity of CxHyN(+) amines: chromatographic descriptors and molecular simulations for understanding partitioning into membranes

Using immobilized artificial membrane high-performance liquid chromatography (IAM-HPLC) the sorption affinity of 70 charged amine structures to phospholipids was determined. The amines contained only 1 charged moiety and no other polar groups, the rest of the molecule being aliphatic and/or aromatic hydrocarbon groups. We systematically evaluated the influence of the amine type (1°, 2°, 3° amines and quaternary ammonium), alkyl chain branching, phenyl ring positioning, charge positioning (terminal vs. central in the molecule) on the phospholipid-water partitioning coefficient (KPLIPW). These experimental results were compared with quantum-chemistry based three-dimensional (3D) molecular simulations of the partitioning of charged amines, including the most likely solute conformers, using a hydrated phospholipid bilayer in the COSMOmic module of COSMOtherm software. Both IAM-HPLC retention data and the simulations suggest that the molecular orientation of charged amines at the location in the bilayer with the lowest calculated Gibbs free energy exerts a strong influence over the partitioning within the membrane. The most favourable position of charged amines coincides with the region where the phosphate anions in the phospholipid bilayer are most abundant. Hydrocarbon units oriented in this layer are located more towards the aqueous phase and contribute less to the overall membrane affinity than hydrocarbon units extending into the more hydrophobic core of the bilayer. COSMOmic simulations explain most of the trends between the structural differences observed in IAM-HPLC based KPLIPW. For this set of cationic structures, the mean absolute difference between COSMOmic simulations and IAM-HPLC data, accounting only for amine type corrective increments, is 0.31 log units.

It is time to develop ecological thresholds of toxicological concern to assist environmental hazard assessment

The threshold of toxicological concern (TTC) concept is well established for assessing human safety of food-contact substances and has been reapplied for a variety of endpoints, including carcinogenicity, teratogenicity, and reproductive toxicity. The TTC establishes an exposure level for chemicals below which no appreciable risk to human health or the environment is expected, based on a de minimis value for toxicity identified for many chemicals. Threshold of toxicological concern approaches have benefits for screening-level risk assessments, including the potential for rapid decision-making, fully utilizing existing knowledge, reasonable conservativeness for chemicals used in lower volumes (low production volume chemicals (e.g., < 1 t/yr), and reduction or elimination of unnecessary animal tests. Higher production volume chemicals (>1 t/yr) would in principle always require specific information because of the presumed higher exposure potential. The TTC approach has found particular favor in the assessment of chemicals used in cosmetics and personal care products, as well as other chemicals traditionally used in low volumes. Use of the TTC in environmental safety is just beginning, and initial attempts are being published. Key questions focus on hazard extrapolation of diverse taxa across trophic levels, importance of mode of action, and whether safe concentrations for ecosystems estimated from acute or chronic toxicity data are equally useful and in what contexts. The present study provides an overview of the theoretical basis for developing an ecological (eco)-TTC, with an initial exploration of chemical assessment and boundary conditions for use. An international collaboration under the International Life Sciences Institute Health and Environmental Sciences Institute has been established to address challenges related to developing and applying useful eco-TTC concepts.

Establishing best practise in the application of expert review of mutagenicity under ICH M7

The ICH M7 guidelines for the assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals allows for the consideration of in silico predictions in place of in vitro studies. This represents a significant advance in the acceptance of (Q)SAR models and has resulted from positive interactions between modellers, regulatory agencies and industry with a shared purpose of developing effective processes to minimise risk. This paper discusses key scientific principles that should be applied when evaluating in silico predictions with a focus on accuracy and scientific rigour that will support a consistent and practical route to regulatory submission.

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.

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.

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.

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.

Practical methods for the measurement of logP for surfactants

Hydrophobicity is a commonly used parameter in quantitative structure activity relationships. The ability to determine the octanol-water partition coefficient (logP) empirically for non-ionizing, non-surfactant type chemicals using traditional stir-flask methods has been successful and well documented. In comparison the ability to measure logP for surfactants is considered impractical due to their amphiphilic nature, which gives them a tendency to form micelles and reside at the octanol-water interface. In this study we have shown that working with compounds below their critical micelle concentrations (CMC), at the experimental concentrations, it is possible to obtain experimental logP values for a series of sulphobetaines using the stir-flask method coupled with reversed phase-high performance liquid chromatography (RP-HPLC). Until now the ability to verify calculated logP values for surfactants has been limited. Measuring logP as described here can now be applied to other surfactants to validate existing and new modifications to the fragment method.