The Use of Computational Methods in the Toxicological Assessment of Chemicals in Food: Current Status and Future Prospects

A wide range of chemicals are intentionally added to, or unintentially found in, food products, often in very small amounts. Depending on the situation, the experimental data needed to complete a dietary risk assessment, which is the scientific basis for protecting human health, may not be available or obtainable, for reasons of cost, time and animal welfare. For example, toxicity data are often lacking for the metabolites and degradation products of pesticide active ingredients. There is therefore an interest in the development and application of efficient and effective non-animal methods for assessing chemical toxicity, including Quantitative Structure-Activity Relationship (QSAR) models and related computational methods. This report gives an overview of how computational methods are currently used in the field of food safety by national regulatory bodies, international advisory organisations and the food industry. On the basis of an international survey, a comprehensive literature review and a detailed QSAR analysis, a range of recommendations are made with the long-term aim of promoting the judicious use of suitable QSAR methods. The current status of QSAR methods is reviewed not only for toxicological endpoints relevant to dietary risk assessment, but also for Absorption, Distribution, Metabolism and Excretion (ADME) properties, which are often important in discriminating between the toxicological profiles of parent compounds and their reaction products. By referring to the concept of the Threshold of Toxicological Concern (TTC), the risk assessment context in which QSAR methods can be expected to be used is also discussed. This Joint Research Centre (JRC) Reference Report provides a summary and update of the findings obtained in a study carried out by the JRC under the terms of a contract awarded by the European Food Safety Authority (EFSA).JRC.DG.I.6-Systems toxicolog

Review of QSAR Models and Software Tools for Predicting of Genotoxicity and Carcinogenicity

This review of QSARs for genotoxicity and carcinogenicity was performed in a broad sense, considering both models available in software tools and models that are published in the literature. The review considered the potential applicability of diverse models to pesticides as well as to other types of regulated chemicals and pharmaceuticals. The availability of models and information on their applicability is summarised in tables, and a range of illustrative or informative examples are described in more detail in the text. In many cases, promising models were identified but they are still at the research stage. For routine application in a regulatory setting, further efforts will be needed to explore the applicability of such models for specific purposes, and to implement them in a practically useful form (i.e. user-friendly software). It is also noted that a range of software tools are research tools suitable for model development, and these require more specialised expertise than other tools that are aimed primarily at end-users such as risk assessors. It is concluded that the most useful models are those which are implemented in software tools and associated with transparent documentation on the model development and validation process. However, it is emphasised that the assessment of model predictions requires a reasonable amount of QSAR knowledge, even if it is not necessary to be a QSAR practitioner.JRC.DG.I.6-Systems toxicolog

Quantitative structure activity relationships in computer aided molecular design

The drug development process requires the complete evaluation and identification of the chosen substance as well as its properties. It involves extensive chemical examination to achieve the best therapeutic effects which demands huge expenditure both in terms of time and money. Computer aided molecular design (CAMD) allows the production of new substances with pre-decided properties. Additionally, in order to illustrate and determine the interrelationship between the chemical structure of a compound and its biological activity, Quantitative Structure Activity Relationship (QSAR) is applied by employing a mathematical model and arranging molecular descriptors. This paper presents review of CAMD and QSAR techniques. The most common chemometric techniques are also emphasized. CAMD and QSAR are considered to be extremely efficient instruments in molecular design and accelerate the initial steps of drug development process. Furthermore, they enhance the effectiveness and reduce the cost of newly developed drugs

Model-to-Model Interface for Multiscale Materials Modeling

A low-level model-to-model interface is presented that will enable independent models to be linked into an integrated system of models. The interface is based on a standard set of functions that contain appropriate export and import schemas that enable models to be linked with no changes to the models themselves. These ideas are presented in the context of a specific multiscale material problem that couples atomistic-based molecular dynamics calculations to continuum calculations of fluid flow. These simulations will be used to examine the influence of interactions of the fluid with an adjacent solid on the fluid flow. The interface will also be examined by adding it to an already existing modeling code, Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) and comparing it with our own molecular dynamics code

Building Enterprise Architecture Agility and Sustenance with SOA

Service-Oriented Architecture(SOA) is primarily regarded as a technical architecture consisting of tools and service specification to build loosely coupled applications. At another level it is also a means to leverage flexibility and agility to system services as it offers a hierarchical framework to coordinate simultaneous business process design and implementations using loosely coupled service infrastructures. SOA has been debated both in the academy and industry and misinterpretations of its nature impede its adoption. We summarize its historical origins and current evolutions. We review technologies that underlie SOA. In particular, we address how to integrate SOA initiatives with current technology platforms, and how to enforce reuse during the design of loosely coupled systems. We also analyze SOA design methodologies and platforms, and what are their roles in the application integration. Finally we outline challenges and future research directions for SOA

Model Transformation Technologies in the Context of Modelling Software Systems

Programming technologies have improved continuously during the last decades, but from an Information Systems perspective, some well-known problems associated to the design and implementation of an Information Systems persists. Object-Oriented Methods, Formal Specification Languages, Component-Based Software Production... This is just a very short list of technologies proposed to solve a very old and, at the same time, very well-known problem: how to produce software of quality. Programming has been the key task during the last 40 years, and the results have not been successful yet. This work will explore the need of facing a sound software production process from a different perspective: the non-programming perspective, where by non-programming we mainly mean modeling. Instead of talking about Extreme Programming, we will introduce a Extreme Non-Programming (Extreme Modeling-Oriented) approach. We will base our ideas on the intensive work done during the last years, oriented to the objective of generating code from a higher-level system specification, normally represented as a Conceptual Schema. Nowadays, though, the hip around MDA has given a new push to these strategies. New methods propose sound model transformations which cover all the different steps of a sound software production process from an Information Systems Engineering point of view. This must include Organizational Modeling, Requirements Engineering, Conceptual Modeling and Model-Based Code Generation techniques. In this context, it seems that the time of Model Transformation Technologies is finally here..

Use of QSARs in international decision-making frameworks to predict health effects of chemical substances

This article is a review of the use of quantitative (and qualitative) structure-activity relationships (QSARs and SARs) by regulatory agencies and authorities to predict acute toxicity, mutagenicity, carcinogenicity, and other health effects. A number of SAR and QSAR applications, by regulatory agencies and authorities, are reviewed. These include the use of simple QSAR analyses, as well as the use of multivariate QSARs, and a number of different expert system approaches

Review of QSAR Models and Software Tools for Predicting Developmental and Reproductive Toxicity

This report provides a state-of-the-art review of available computational models for developmental and reproductive toxicity, including Quantitative Structure-Activity Relationship (QSARs) and related estimation methods such as decision tree approaches and expert systems. At present, there are relatively few models for developmental and reproductive toxicity endpoints, and those available have limited applicability domains. This situation is partly due to the biological complexity of the endpoint, which covers many incompletely understood mechanisms of action, and partly due to the paucity and heterogeneity of high quality data suitable for model development. In contrast, there is an extensive and growing range of software and literature models for predicting endocrine-related activities, in particular models for oestrogen and androgen activity. There is a considerable need to further develop and characterise in silico models for developmental and reproductive toxicity, and to explore their applicability in a regulatory setting.JRC.DG.I.6-Systems toxicolog

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