THE INTEGRATED USE OF PHYSICOCHEMICAL AND IN VITRO DATA FOR PREDICTING CHEMICAL TOXICITY

The general aim of this PhD project was to investigate the integrated use of physicochemical and in vitro data for predicting the toxicological hazard of chemicals in animals. This was achieved in two stages: firstly, by developing two types of model for acute dermal and ocular toxicity - structure-activity relationships (SARs) based on easily calculated physicochemical properties, and prediction models (PMs) based on experimentally derived physicochemical or in vitro data; and secondly, by evaluating the tiered testing approach to hazard classification, in which different classification models (CMs) are applied sequentially before animal testing is conducted. The thesis therefore reports the development and assessment of CMs for skin irritation, skin corrosion and eye irritation, as well as the outcome of simulations in which these models were incorporated into tiered testing strategies for these toxicological endpoints. The results show that the tiered testing approach to hazard classification provides a reliable means of reducing and refining the use of animals, without compromising the ability to classify chemicals. In addition to developing the above-mentioned CMs, regression models for corneal permeability were developed, and the relationship between corneal permeability and eye irritation was investigated. The thesis also describes the development and assessment of a novel statistical method called embedded cluster modelling (ECM), which generates elliptic models of biological activity from embedded data sets. The combined use of this method with the existing method of cluster significance analysis (CSA) is illustrated through the development of SARs for eye irritation potential. Finally, novel applications of the bootstrap resampling method were investigated. In particular, algorithms based on this method are shown to provide a means of assessing: a) the minimal variability associated with the Draize rabbit tests for skin and eye irritation; and b) the variability in Cooper statistics (commonly used to summarise the performance of two-group CMs) that arises from chemical variation

Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs

This article provides an overview of methods for reliability assessment of quantitative structure–activity relationship (QSAR) models in the context of regulatory acceptance of human health and environmental QSARs. Useful diagnostic tools and data analytical approaches are highlighted and exemplified. Particular emphasis is given to the question of how to define the applicability borders of a QSAR and how to estimate parameter and prediction uncertainty. The article ends with a discussion regarding QSAR acceptability criteria. This discussion contains a list of recommended acceptability criteria, and we give reference values for important QSAR performance statistics. Finally, we emphasize that rigorous and independent validation of QSARs is an essential step toward their regulatory acceptance and implementation. Key words: QSAR acceptability criteria, QSAR applicability domain, QSAR reliability, QSAR uncertainty estimation, QSAR validation

Use of QSARs in international decision-making frameworks to predict ecologic effects and environmental fate of chemical substances

This article is a review of the use, by regulatory agencies and authorities, of quantitative structure–activity relationships (QSARs) to predict ecologic effects and environmental fate of chemicals. For many years, the U.S. Environmental Protection Agency has been the most prominent regulatory agency using QSARs to predict the ecologic effects and environmental fate of chemicals. However, as increasing numbers of standard QSAR methods are developed and validated to predict ecologic effects and environmental fate of chemicals, it is anticipated that more regulatory agencies and authorities will find them to be acceptable alternatives to chemical testing

Experimental and numerical investigation into the propagation of entropy waves

Entropy waves are an important source of indirect combustion noise and potentially contribute to the generation of thermoacoustic instabilities in gas-turbine combustors. Entropy fluctuations generated by unsteady combustion are known to disperse and diffuse as they convect toward the combustor exit. In this work, the propagation of entropy waves is investigated by means of experiments in a newly developed entropy rig and numerical simulations based on the large-eddy simulation approach. Both experimental and numerical results demonstrate that the amplitude of entropy fluctuations decays as a function of wave parameters and propagation distance, and it scales well with a local Helmholtz number $\textit{He}$. A new theoretical model for the computation of the entropy transfer function suitable for inclusion in low-order models for combustion instabilities is proposed. Assessment against numerical and experimental results shows the capability of the model to give a proper representation of the decay of entropy waves in terms of both magnitude and phase of the entropy transfer function. Furthermore, by comparison with the large-eddy simulation results, it is shown that, at low Helmholtz numbers, the contribution of the differential convection to the decay of entropy waves is dominant; whereas for high values of the Helmholtz number, the turbulent mixing and diffusion also become important.The research leading to these results received funding from the European Community’s Seventh Framework Programme (FP7/2007- 2013)under grant agreement no.265586 and was conducted within the Intelligent Design Methodologies for Low Pollutant Combustors for Aero-Engines project

Automated workflows for modelling chemical fate, kinetics and toxicity.

Automation is universal in today's society, from operating equipment such as machinery, in factory processes, to self-parking automobile systems. While these examples show the efficiency and effectiveness of automated mechanical processes, automated procedures that support the chemical risk assessment process are still in their infancy. Future human safety assessments will rely increasingly on the use of automated models, such as physiologically based kinetic (PBK) and dynamic models and the virtual cell based assay (VCBA). These biologically-based models will be coupled with chemistry-based prediction models that also automate the generation of key input parameters such as physicochemical properties. The development of automated software tools is an important step in harmonising and expediting the chemical safety assessment process. In this study, we illustrate how the KNIME Analytics Platform can be used to provide a user-friendly graphical interface for these biokinetic models, such as PBK models and VCBA, which simulates the fate of chemicals in vivo within the body and in vitro test systems respectively

Predicting toxicity through computers: a changing world

The computational approaches used to predict toxicity are evolving rapidly, a process hastened on by the emergence of new ways of describing chemical information. Although this trend offers many opportunities, new regulations, such as the European Community's 'Registration, Evaluation, Authorisation and Restriction of Chemicals' (REACH), demand that models be ever more robust

Quantitative Structure - Skin permeability Relationships

This paper reviews in silico models currently available for the prediction of skin permeability with the main focus on the quantitative structure-permeability relationship (QSPR) models. A comprehensive analysis of the main achievements in the field in the last decade is provided. In addition, the mechanistic models are discussed and comparative studies that analyse different models are discussed

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 an essential tool 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

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)

HIV infection and sexual risk among men who have sex with men and women (MSMW): A systematic review and meta-analysis

Objectives: To estimate the number of men who have sex with men and women who are HIV-positive in the United States, and to compare HIV prevalence rates between men who have sex with men and women, men who have sex with men only, and men who have sex with women exclusively. Methods: Following PRISMA guidelines, we conducted a systematic review and meta-analysis of reports referencing HIV prevalence and men who have sex with men and women. We searched PubMed and Ovid PsycINFO for peer-reviewed, U.S.-based articles reporting on HIV prevalence among men who have sex with men and women. We conducted event rate, effect size, moderation and sensitivity analyses. Results: We estimate that 1.0% of U.S. males are bisexually-behaving, and that 121,800 bisexually-behaving men are HIV-positive. Men who have sex with men and women are less than half as likely to be HIV-positive as men who have sex with men only (16.9% vs. 33.3%; OR = 0.41, 95% CI: 0.31, 0.54), but more than five times as likely to be HIV-positive as men who have sex with women exclusively (18.3% vs. 3.5%; OR = 5.71, 95% CI: 3.47, 9.39). They are less likely to engage in unprotected receptive anal intercourse than men who have sex with men only (15.9% vs. 35.0%; OR = 0.36, 95% CI: 0.28, 0.46). Men who have sex with men and women in samples with high racial/ethnic minority proportions had significantly higher HIV prevalence than their counterparts in low racial/ethnic minority samples. Conclusions: This represents the first meta-analysis of HIV prevalence in the U.S. between men who have sex with men and women and men who have sex with men only. Data collection, research, and HIV prevention and care delivery specifically tailored to men who have sex with men and women are necessary to better quantify and ameliorate this population's HIV burden. © 2014 Friedman et al