Next generation risk assessment for skin allergy: Decision making using new approach methodologies.

Our aim is to develop and apply next generation approaches to skin allergy risk assessment (SARA) that do not require new animal test data and better quantify uncertainties. Significant progress has been made in the development of New Approach Methodologies (NAMs), non-animal test methods, for assessment of skin sensitisation and there is now focus on their application to derive potency information for use in Next Generation Risk Assessment (NGRA). The SARA model utilises a Bayesian statistical approach to infer a human-relevant metric of sensitiser potency and a measure of risk associated with a given consumer exposure based upon any combination of human repeat insult patch test, local lymph node, direct peptide reactivity assay, KeratinoSens™, h-CLAT or U-SENS™ data. Here we have applied the SARA model within our weight of evidence NGRA framework for skin allergy to three case study materials in four consumer products. Highlighting how to structure the risk assessment, apply NAMs to derive a point of departure and conclude on consumer safety risk. NGRA based upon NAMs were, for these exposures, at least as protective as the historical risk assessment approaches. Through such case studies we are building our confidence in using NAMs for skin allergy risk assessment.

A hypothetical skin sensitisation next generation risk assessment for coumarin in cosmetic products.

Next generation Risk Assessment (NGRA) is an exposure-led, hypothesis-driven approach which integrates new approach methodologies (NAMs) to assure safety without generating animal data. This hypothetical skin allergy risk assessment of two consumer products - face cream containing 0.1% coumarin and deodorant containing 1% coumarin - demonstrates the application of our skin allergy NGRA framework which incorporates our Skin Allergy Risk Assessment (SARA) Model. SARA uses Bayesian statistics to provide a human relevant point of departure and risk metric for a given chemical exposure based upon input data that can include both NAMs and historical in vivo studies. Regardless of whether NAM or in vivo inputs were used, the model predicted that the face cream and deodorant exposures were low and high risk respectively. Using only NAM data resulted in a minor underestimation of risk relative to in vivo. Coumarin is a predicted pro-hapten and consequently, when applying this mechanistic understanding to the selection of NAMs the discordance in relative risk could be minimized. This case study demonstrates how integrating a computational model and generating bespoke NAM data in a weight of evidence framework can build confidence in safety decision making.

COSMOS next generation - A public knowledge base leveraging chemical and biological data to support the regulatory assessment of chemicals.

The COSMOS Database (DB) was originally established to provide reliable data for cosmetics-related chemicals within the COSMOS Project funded as part of the SEURAT-1 Research Initiative. The database has subsequently been maintained and developed further into COSMOS Next Generation (NG), a combination of database and in silico tools, essential components of a knowledge base. COSMOS DB provided a cosmetics inventory as well as other regulatory inventories, accompanied by assessment results and in vitro and in vivo toxicity data. In addition to data content curation, much effort was dedicated to data governance - data authorisation, characterisation of quality, documentation of meta information, and control of data use. Through this effort, COSMOS DB was able to merge and fuse data of various types from different sources. Building on the previous effort, the COSMOS Minimum Inclusion (MINIS) criteria for a toxicity database were further expanded to quantify the reliability of studies. COSMOS NG features multiple fingerprints for analysing structure similarity, and new tools to calculate molecular properties and screen chemicals with endpoint-related public profilers, such as DNA and protein binders, liver alerts and genotoxic alerts. The publicly available COSMOS NG enables users to compile information and execute analyses such as category formation and read-across. This paper provides a step-by-step guided workflow for a simple read-across case, starting from a target structure and culminating in an estimation of a NOAEL confidence interval. Given its strong technical foundation, inclusion of quality-reviewed data, and provision of tools designed to facilitate communication between users, COSMOS NG is a first step towards building a toxicological knowledge hub leveraging many public data systems for chemical safety evaluation. We continue to monitor the feedback from the user community at support@mn-am.com.

Characterisation of data resources for in silico modelling: benchmark datasets for ADME properties.

INTRODUCTION: The cost of in vivo and in vitro screening of ADME properties of compounds has motivated efforts to develop a range of in silico models. At the heart of the development of any computational model are the data; high quality data are essential for developing robust and accurate models. The characteristics of a dataset, such as its availability, size, format and type of chemical identifiers used, influence the modelability of the data. Areas covered: This review explores the usefulness of publicly available ADME datasets for researchers to use in the development of predictive models. More than 140 ADME datasets were collated from publicly available resources and the modelability of 31 selected datasets were assessed using specific criteria derived in this study. Expert opinion: Publicly available datasets differ significantly in information content and presentation. From a modelling perspective, datasets should be of adequate size, available in a user-friendly format with all chemical structures associated with one or more chemical identifiers suitable for automated processing (e.g. CAS number, SMILES string or InChIKey). Recommendations for assessing dataset suitability for modelling and publishing data in an appropriate format are discussed.

Mechanism-Based QSAR Modeling of Skin Sensitization.

Many chemicals can induce skin sensitization, and there is a pressing need for non-animal methods to give a quantitative indication of potency. Using two large published data sets of skin sensitizers, we have allocated each sensitizing chemical to one of 10 mechanistic categories and then developed good QSAR models for the seven categories that have a sufficient number of chemicals to allow modeling. Both internal and external validation checks showed that each model had good predictivity.

Hepatotoxicity: a scheme for generating chemical categories for read-across, structural alerts and insights into mechanism(s) of action.

The ability of a compound to cause adverse effects to the liver is one of the most common reasons for drug development failures and the withdrawal of drugs from the market. Such adverse effects can vary tremendously in severity, leading to an array of possible drug-induced liver injuries (DILIs). As a result, it is not surprising that drug development has evolved into a complex and multifaceted process including methods aiming to identify potential liver toxicities. Unfortunately, hepatotoxicity remains one of the most complex and poorly understood areas of human toxicity; thus it is a significant challenge to identify potential hepatotoxins. The performance of existing methods to identify hepatotoxicity requires improvement. The current study details a scheme for generating chemical categories and the development of structural alerts able to identify potential hepatotoxins. The study utilized a diverse 951-compound dataset and used structural similarity methods to produce a number of structurally restricted categories. From these categories, 16 structural alerts associated with observed human hepatotoxicity were developed. Furthermore, the mechanism(s) by which these compounds cause hepatotoxicity were investigated and a mechanistic rationale was proposed, where possible, to yield mechanistically supported structural alerts. Alerts of this nature have the potential to be used in the screening of compounds to highlight potential hepatotoxicity, whilst the chemical categories themselves are important in applying read-across approaches. The scheme presented in this study also has the potential to act as a knowledge generator serving as an excellent starting platform from which to conduct additional toxicological studies.

Assessing toxicological data quality: basic principles, existing schemes and current limitations.

Existing toxicological data may be used for a variety of purposes such as hazard and risk assessment or toxicity prediction. The potential use of such data is, in part, dependent upon their quality. Consideration of data quality is of key importance with respect to the application of chemicals legislation such as REACH. Whether data are being used to make regulatory decisions or build computational models, the quality of the output is reflected by the quality of the data employed. Therefore, the need to assess data quality is an important requirement for making a decision or prediction with an appropriate level of confidence. This study considers the biological and chemical factors that may impact upon toxicological data quality and discusses the assessment of data quality. Four general quality criteria are introduced and existing data quality assessment schemes are discussed. Two case study datasets of skin sensitization data are assessed for quality providing a comparison of existing assessment methods. This study also discusses the limitations and difficulties encountered during quality assessment, including the use of differing quality schemes and the global versus chemical-specific assessments of quality. Finally, a number of recommendations are made to aid future data quality assessments.

Syntheses, structure, and reactivity of chiral titanium compounds: procatalysts for olefin polymerization.

Titanium complexes with chelating alkoxo ligands have been synthesised with the aim to investigate titanium active centres in catalytic ethylene polymerisation. The titanium complexes cis-[TiCl2(eta2-maltolato)2] (1, 89%), and cis-[TiCl2(eta2-guaiacolato)2] (2, 80%) were prepared by direct reaction of TiCl4 with maltol and guaiacol in toluene. The addition of maltol to [Ti(OiPr)4] in THF results in the formation of species [Ti(OiPr)2(maltolato)2] (3, 82%). The titanium compound cis-[Ti(OEt)2(eta2-maltolato)2] (4, 74%) was obtained by the transesterification reaction of species 3 with CH3CO2Et. When compound 4 is dissolved in THF a dinuclear species [Ti2(mu-OEt)2(OEt)4-(eta2-maltolato)2] (5, 45%) is formed. Reaction of [Ti(OiPr)4] with crude guaiacol in THF yields a solid, which after recrystallisation from acetonitrile gives [Ti4(mu-O)4(eta2-guaiacolato)] x 4CH3CN (6, 55%). In contrast, reaction of TiCl4 with crude guaiacol in tetrahydrofuran affords [Ti2(mu-O)Cl2(eta2-guaiacolato)4] (7, 82%). Crystallographic and electrochemical analyses of these complexes demonstrate that maltolato and guaiacolato ligands can be used as a valuable alternative for the cyclopentadienyl ring. These complexes have been shown to be active catalysts upon combination with the appropriate activator.