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

Development of Knowledge Within a Chemical-Toxicological Database to Formulate Novel Computational Approaches for Predicting Repeated Dose Toxicity of Cosmetics-Related Compounds

The European Union (EU) Cosmetics Regulation established the ban on animal testing for cosmetics ingredients. This ban does not assume that all cosmetics ingredients are safe, but that the non-testing procedures (in vitro and in silico) have to be applied for their safety assessment. To this end, the SEURAT-1 cluster was funded by EU 7th Framework Programme and Cosmetics Europe. The COSMOS (Integrated In Silico Models for the Prediction of Human Repeated Dose Toxicity of COSMetics to Optimise Safety) project was initiated as one of the seven consortia of the cluster, with the purpose of facilitating the prediction of human repeated dose toxicity associated with exposure to cosmetics-related compounds through in silico approaches. A critical objective of COSMOS was to address the paucity of publicly available data for cosmetics ingredients and related chemicals. Therefore a database was established containing (i) an inventory of cosmetics ingredients and related structures; (ii) skin permeability/absorption data (route of exposure relevant to cosmetics); and (iii) repeated dose toxicity data. This thesis describes the process of “knowledge discovery from the data”, including collation of the content of the COSMOS database and its subsequent application for developing tools to support the prediction of repeated dose toxicity of cosmetics and related compounds. A rigorous strategy of curation and quality control of chemical records was applied in developing the database (as documented in the Standard Operating Procedure, chapter 2). The chemical space of the cosmetics-related compounds was compared to food-related compounds from the U.S. FDA CFSAN PAFA database using the novel approach combining the analysis of structural features (ToxPrint chemotypes) and physicochemical properties. The cosmetics- and food- specific structural classes related to particular use functions and manifested by distinct physicochemical properties were identified (chapter 3). The novel COSMOS Skin Permeability Database containing in vivo and in vitro skin permeability/absorption data was developed by integrating existing databases and enriching them with new data for cosmetics harvested from regulatory documents and scientific literature (chapter 4). Compounds with available data on human in vitro maximal flux (JMAX) were subsequently extracted from the developed database and analysed in terms of their structural features (ToxPrint chemotypes) and physicochemical properties. The profile of compounds exhibiting low or high skin permeability potential was determined. The results of this analysis can support rapid screening and classification of the compounds without experimental data (chapter 5). The new COSMOS oral repeated dose toxicity database was established through consolidation of existing data sources and harvesting new regulatory documents and scientific literature. The unique data structure of the COSMOS oRepeatToxDB allows capturing all toxicological effects observed at particular dose levels and sites, which are hierarchically differentiated as organs, tissues, and cells (chapter 6). Such design of this database enabled the development of liver toxicity ontology, followed by mechanistic mining of in vivo data (chapter 7). As a result, compounds associated with liver steatosis, steatohepatitis and fibrosis phenotypic effects were identified and further analysed. The probable mechanistic reasoning for toxicity (Peroxisome Proliferator-Activated Receptor gamma (PPAR ) activation) was formulated for two hepatotoxicants, namely 1,3-bis-(2,4-diaminophenoxy)-propane and piperonyl butoxide. Key outcomes of this thesis include an extensive curated database, Standard Operating Procedures, skin permeability potential classification rules, and the set of structural features associated with liver steatosis. Such knowledge is particularly important in the light of the 21st Century Toxicology (NRC, 2007) and the ongoing need to move away from animal toxicity testing to non-testing alternatives

Relationship Between Adverse Outcome Pathways and Chemistry-Based in Silico Models to Predict Toxicity

The current landscape of Adverse Outcome Pathways (AOPs) provides a means of organising information relating to the adverse effects elicited following exposure to chemicals. As such, AOPs are an excellent driver for the development and application of in silico models for predictive toxicology allowing for the direct relationship between chemistry and adverse effects to be established. Information may be extracted from AOPs to support the creation of (quantitative) structure-activity relationships ((Q)SARs) as well as to increase confidence in grouping and read-across. Any part of an AOP can be linked to these various types of in silico models. There is, however, an emphasis on using information from known Molecular Initiating Events (MIEs) to create models including 2D and 3D structural alerts, SARs and QSARs. MIEs can be classified according to the nature of the interaction e.g. covalent reactivity, oxidative stress, phototoxicity, chronic receptor mediated, acute enzyme inhibition, unspecific, physical and other effects. Different types of MIEs require different approaches to their in silico modelling. Modelling Key Events and Key Event Relationships is useful if they represent the rate limiting step or key determinant of toxicity. Modelling of metabolism and chemical interactions will become part of AOP networks, which are also driving species-specific extrapolation and respective adaptation of models. With more information and data being captured, in silico approaches will increasingly support the application of knowledge from AOPs to build weight of evidence and support risk assessment, e.g. in the context of Integrated Assessment and Testing Approaches (IATAs)

Update of the Cancer Potency Database (CPDB) to enable derivations of Thresholds Of Toxicological Concern (TTC) for cancer potency

The purpose of this study was to update the existing Cancer Potency Database (CPDB) in order to support the development of a dataset of compounds, with associated points of departure (PoDs), to enable a review and update of currently applied values for the Threshold of Toxicological Concern (TTC) for cancer endpoints. This update of the current CPDB, last reviewed in 2012, includes the addition of new data (44 compounds and 158 studies leading to additional 359 dose-response curves). Strict inclusion criteria were established and applied to select compounds and studies with relevant cancer potency data. PoDs were calculated from dose-response modeling, including the benchmark dose (BMD) and the lower 90% confidence limits (BMDL) at a specified benchmark response (BMR) of 10%. The updated full CPDB database resulted in a total of 421 chemicals which had dose-response data that could be used to calculate PoDs. This candidate dataset for cancer TTC is provided in a transparent and adaptable format for further analysis of TTC to derive cancer potency thresholds

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

In Silico Resources to Assist in the Development and Evaluation of Physiologically-Based Kinetic Models

Since their inception in pharmaceutical applications, physiologically-based kinetic (PBK) models are increasingly being used across a range of sectors, such as safety assessment of cosmetics, food additives, consumer goods, pesticides and other chemicals. Such models can be used to construct organ-level concentration-time profiles of xenobiotics. These models are essential in determining the overall internal exposure to a chemical and hence its ability to elicit a biological response. There are a multitude of in silico resources available to assist in the construction and evaluation of PBK models. An overview of these resources is presented herein, encompassing all attributes required for PBK modelling. These include predictive tools and databases for physico-chemical properties and absorption, distribution, metabolism and elimination (ADME) related properties. Data sources for existing PBK models, bespoke PBK software and generic software that can assist in model development are also identified. On-going efforts to harmonise approaches to PBK model construction, evaluation and reporting that would help increase the uptake and acceptance of these models are also discussed

Potential of ToxCast data in the safety assessment of food chemicals

Tox21 and ToxCast are high-throughput in vitro screening (HTS) programmes coordinated by the U.S. National Toxicology Program and the U.S. Environmental Protection Agency, respectively, with the goal of forecasting biological effects in vivo based on bioactivity profiling. The present study investigated whether mechanistic insights in the biological targets of food-relevant chemicals can be obtained from ToxCast results, when the chemicals are grouped according to structural similarity. Starting from the 556 direct additives that have been identified in the ToxCast database by Karmaus et al. (2017), the results showed that, despite the limited number of assays in which the chemical groups have been tested, sufficient results are available within so-called “DNA binding” and “nuclear receptor” target families to profile the biological activities of the defined chemical groups for these targets. The most obvious activity identified was the estrogen receptor (ER)-mediated actions of the chemical group containing parabens and structurally related gallates, as well the chemical group containing genistein and daidzein (the latter particularly towards ERβ as potential health beneficial target). These group effects, as well as the biological activities of other chemical groups, was evaluated in a series of case studies. Overall, the results of the present study suggest HTS data could add to the evidence considered for regulatory risk assessments for food chemicals and to the evaluation of desirable effects of nutrients and phytonutrients. The data will be particularly useful for providing mechanistic information and to fill data gaps with read-across

Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across

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