A review of quantitative structure-activity relationship modelling approaches to predict the toxicity of mixtures

Exposure to chemicals generally occurs in the form of mixtures. However, the great majority of the toxicity data, upon which chemical safety decisions are based, relate only to single compounds. It is currently unfeasible to test a fully representative proportion of mixtures for potential harmful effects and, as such, in silico modelling provides a practical solution to inform safety assessment. Traditional methodologies for deriving estimations of mixture effects, exemplified by principles such as concentration addition (CA) and independent action (IA), are limited as regards the scope of chemical combinations to which they can reliably be applied. Development of appropriate quantitative structure-activity relationships (QSARs) has been put forward as a solution to the shortcomings present within these techniques – allowing for the potential formulation of versatile predictive tools capable of capturing the activities of a full contingent of possible mixtures. This review addresses the current state-of-the-art as regards application of QSAR towards mixture toxicity, discussing the challenges inherent in the task, whilst considering the strengths and limitations of existing approaches. Forty studies are examined within – through reference to several characteristic elements including the nature of the chemicals and endpoints modelled, the form of descriptors adopted, and the principles behind the statistical techniques employed. Recommendations are in turn provided for practices which may assist in further advancing the field, most notably with regards to ensuring confidence in the acquired predictions.publishedVersio

In Silico Identification of Chemicals Capable of Binding to the Ecdysone Receptor

The process of molting, known alternatively as ecdysis, is a feature integral in the life cycles of species across the arthropod phylum. Regulation occurs as a function of the interaction of ecdysteroid hormones with the arthropod nuclear ecdysone receptor—a process preceding the triggering of a series of downstream events constituting an endocrine signaling pathway highly conserved throughout environmentally prevalent insect, crustacean, and myriapod organisms. Inappropriate ecdysone receptor binding and activation forms the essential molecular initiating event within possible adverse outcome pathways relating abnormal molting to mortality in arthropods. Definition of the characteristics of chemicals liable to stimulate such activity has the potential to be of great utility in mitigation of hazards posed toward vulnerable species. Thus the aim of the present study was to develop a series of rule‐sets, derived from the key structural and physicochemical features associated with identified ecdysone receptor ligands, enabling construction of Konstanz Information Miner (KNIME) workflows permitting the flagging of compounds predisposed to binding at the site. Data describing the activities of 555 distinct chemicals were recovered from a variety of assays across 10 insect species, allowing for formulation of KNIME screens for potential binding activity at the molecular initiating event and adverse outcome level of biological organization. Environ Toxicol Chem 2020;39:1438–1450. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC

Assessing the safety of cosmetic chemicals: Consideration of a flux decision tree to predict dermally delivered systemic dose for comparison with oral TTC (Threshold of Toxicological Concern)

AbstractThreshold of Toxicological Concern (TTC) aids assessment of human health risks from exposure to low levels of chemicals when toxicity data are limited. The objective here was to explore the potential refinement of exposure for applying the oral TTC to chemicals found in cosmetic products, for which there are limited dermal absorption data. A decision tree was constructed to estimate the dermally absorbed amount of chemical, based on typical skin exposure scenarios. Dermal absorption was calculated using an established predictive algorithm to derive the maximum skin flux adjusted to the actual ‘dose’ applied. The predicted systemic availability (assuming no local metabolism), can then be ranked against the oral TTC for the relevant structural class. The predictive approach has been evaluated by deriving the experimental/prediction ratio for systemic availability for 22 cosmetic chemical exposure scenarios. These emphasise that estimation of skin penetration may be challenging for penetration enhancing formulations, short application times with incomplete rinse-off, or significant metabolism. While there were a few exceptions, the experiment-to-prediction ratios mostly fell within a factor of 10 of the ideal value of 1. It can be concluded therefore, that the approach is fit-for-purpose when used as a screening and prioritisation tool

Identification and Description of the Uncertainty, Variability, Bias and Influence in Quantitative Structure-Activity Relationships (QSARs) for Toxicity Prediction

Improving regulatory confidence in, and acceptance of, a prediction of toxicity from a quantitative structure-activity relationship (QSAR) requires assessment of its uncertainty and determination of whether the uncertainty is acceptable. Thus, it is crucial to identify potential uncertainties fundamental to QSAR predictions. Based on expert review, sources of uncertainties, variabilities and biases, as well as areas of influence in QSARs for toxicity prediction were established. These were grouped into three thematic areas: uncertainties, variabilities, potential biases and influences associated with 1) the creation of the QSAR, 2) the description of the QSAR, and 3) the application of the QSAR, also showing barriers for their use. Each thematic area was divided into a total of 13 main areas of concern with 49 assessment criteria covering all aspects of QSAR development, documentation and use. Two case studies were undertaken on different types of QSARs that demonstrated the applicability of the assessment criteria to identify potential weaknesses in the use of a QSAR for a specific purpose such that they may be addressed and mitigation strategies can be proposed, as well as enabling an informed decision on the adequacy of the model in the considered context

The application of molecular modelling in the safety assessment of chemicals: A case study on ligand-dependent PPARγ dysregulation.

The aim of this paper was to provide a proof of concept demonstrating that molecular modelling methodologies can be employed as a part of an integrated strategy to support toxicity prediction consistent with the mode of action/adverse outcome pathway (MoA/AOP) framework. To illustrate the role of molecular modelling in predictive toxicology, a case study was undertaken in which molecular modelling methodologies were employed to predict the activation of the peroxisome proliferator-activated nuclear receptor γ (PPARγ) as a potential molecular initiating event (MIE) for liver steatosis. A stepwise procedure combining different in silico approaches (virtual screening based on docking and pharmacophore filtering, and molecular field analysis) was developed to screen for PPARγ full agonists and to predict their transactivation activity (EC50). The performance metrics of the classification model to predict PPARγ full agonists were balanced accuracy=81%, sensitivity=85% and specificity=76%. The 3D QSAR model developed to predict EC50 of PPARγ full agonists had the following statistical parameters: q(2)cv=0.610, Nopt=7, SEPcv=0.505, r(2)pr=0.552. To support the linkage of PPARγ agonism predictions to prosteatotic potential, molecular modelling was combined with independently performed mechanistic mining of available in vivo toxicity data followed by ToxPrint chemotypes analysis. The approaches investigated demonstrated a potential to predict the MIE, to facilitate the process of MoA/AOP elaboration, to increase the scientific confidence in AOP, and to become a basis for 3D chemotype development

Thresholds of Toxicological Concern for Cosmetics-Related Substances: New Database, Thresholds, and Enrichment of Chemical Space

A new dataset of cosmetics-related chemicals for the Threshold of Toxicological Concern (TTC) approach has been compiled, comprising 552 chemicals with 219, 40, and 293 chemicals in Cramer Classes I, II, and III, respectively. Data were integrated and curated to create a database of No-/Lowest-Observed-Adverse-Effect Level (NOAEL/LOAEL) values, from which the final COSMOS TTC dataset was developed. Criteria for study inclusion and NOAEL decisions were defined, and rigorous quality control was performed for study details and assignment of Cramer classes. From the final COSMOS TTC dataset, human exposure thresholds of 42 and 7.9 μg/kg-bw/day were derived for Cramer Classes I and III, respectively. The size of Cramer Class II was insufficient for derivation of a TTC value. The COSMOS TTC dataset was then federated with the dataset of Munro and colleagues, previously published in 1996, after updating the latter using the quality control processes for this project. This federated dataset expands the chemical space and provides more robust thresholds. The 966 substances in the federated database comprise 245, 49 and 672 chemicals in Cramer Classes I, II and III, respectively. The corresponding TTC values of 46, 6.2 and 2.3 μg/kg-bw/day are broadly similar to those of the original Munro dataset

Statement on Advancing the Assessment of Chemical Mixtures and their Risks for Human Health and the Environment

The number of anthropogenic chemicals, manufactured, by-products, metabolites and abiotically formed transformation products, counts to hundreds of thousands, at present. Thus, humans and wildlife are exposed to complex mixtures, never one chemical at a time and rarely with only one dominating effect. Hence there is an urgent need to develop strategies on how exposure to multiple hazardous chemicals and the combination of their effects can be assessed. A workshop, “Advancing the Assessment of Chemical Mixtures and their Risks for Human Health and the Environment” was organized in May 2018 together with Joint Research Center in Ispra, EU-funded research projects and Commission Services and relevant EU agencies. This forum for researchers and policy-makers was created to discuss and identify gaps in risk assessment and governance of chemical mixtures as well as to discuss state of the art science and future research needs. Based on the presentations and discussions at this workshop we want to bring forward the following Key Messages: We are at a turning point: multiple exposures and their combined effects require better management to protect public health and the environment from hazardous chemical mixtures. Regulatory initiatives should be launched to investigate the opportunities for all relevant regulatory frameworks to include prospective mixture risk assessment and consider combined exposures to (real-life) chemical mixtures to humans and wildlife, across sectors. Precautionary approaches and intermediate measures (e.g. Mixture Assessment Factor) can already be applied, although, definitive mixture risk assessments cannot be routinely conducted due to significant knowledge and data gaps. A European strategy needs to be set, through stakeholder engagement, for the governance of combined exposure to multiple chemicals and mixtures. The strategy would include research aimed at scientific advancement in mechanistic understanding and modelling techniques, as well as research to address regulatory and policy needs. Without such a clear strategy, specific objectives and common priorities, research, and policies to address mixtures will likely remain scattered and insufficient

Perspectives from the NanoSafety Modelling Cluster on the validation criteria for (Q)SAR models used in nanotechnology

Nanotechnology and the production of nanomaterials have been expanding rapidly in recent years. Since many types of engineered nanoparticles are suspected to be toxic to living organisms and to have a negative impact on the environment, the process of designing new nanoparticles and their applications must be accompanied by a thorough exposure risk analysis. (Quantitative) Structure-Activity Relationship ([Q]SAR) modelling creates promising options among the available methods for the risk assessment. These in silico models can be used to predict a variety of properties, including the toxicity of newly designed nanoparticles. However, (Q)SAR models must be appropriately validated to ensure the clarity, consistency and reliability of predictions. This paper is a joint initiative from recently completed European research projects focused on developing (Q)SAR methodology for nanomaterials. The aim was to interpret and expand the guidance for the well-known “OECD Principles for the Validation, for Regulatory Purposes, of (Q)SAR Models”, with reference to nano-(Q)SAR, and present our opinions on the criteria to be fulfilled for models developed for nanoparticles

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 IMPACT OF VARIABLE SELECTION ON THE MODELLING OF OESTROGENICITY

Many oestrogenic chemicals exert their activity via specific interactions with the oestrogen receptor (ER). The objective of the present study was to identify significant descriptors associated with the ER binding affinities of a large and diverse set of compounds to drive quantitative structure–activity relationships (QSARs). To this end,a variety of statistical methods were employed for variable selection. These included stepwise regression and partial least squares (PLS) analyses, as well as a non-linear recursive partitioning method (Formal Inference-based Recursive Modelling). A total of 157 molecular descriptors including quantum mechanical, graph theoretical, indicator variables and log P were used in the study. Furthermore, cluster analysis of variables was performed to identify groups of descriptors representing similar molecular features. Hierarchical PLS analyses were performed,where the scores of the significant components of either PLS or principle component analysis (PCA), performed separately on each cluster, were used as the variables for the top model. This reduced the number of the variables representing the larger clusters, leading to a similar number of descriptors for each distinct molecular feature. The results showed that the most important molecular properties for stronger ER binding affinity are molecular size and shape, the presence of a phenol moiety as well as other aromatic groups, hydrophobicity and presence of double bonds. The best PLS model obtained, in terms of predictive ability, was a hierarchical PLS model. However,a rigorous validation study showed that the MLR model using descriptors selected by stepwise regression has greater predictive power than the PLS models