Development and Beta Testing of the Toxmatch Similarity Tool

Toxmatch was developed as a result of a proposal approved within the JRC Innovation Project Competition in 2005. The aim of the project proposal was to develop the prototype of a software tool for supporting the risk assessment of chemical substances. Such a tool will be useful for scientific researchers, for end-users in industry, for regulatory authorities, and in the future EU Chemicals Agency. Toxmatch (Ideaconsult Ltd.) is a flexible user-friendly, computer-based open source application specifically commissioned by ECB which is accessible via internet. It encodes and applies a range of different structural and descriptor based chemical similarity indices. The novelty of this software lies in its ability to calculate similarity measures that are tailored for specific activities/toxicities. Thus, relevant chemical representations can be selected for a given activity and the chemicals of interest can hence be classified into toxicity classes. The present document summarises the beta testing of Toxmatch, reporting general comments and suggestions for further improvement.JRC.I.3-Toxicology and chemical substance

A Similarity Based Approach for Chemical Category Classification

This report aims to describe the main outcomes of an IHCP Exploratory Research Project carried out during 2005 by the European Chemicals Bureau (Computational Toxicology Action). The original aim of this project was to develop a computational method to facilitate the classification of chemicals into similarity-based chemical categories, which would be both useful for building (Q)SAR models (research application) and for defining chemical category proposals (regulatory application).JRC.I-Institute for Health and Consumer Protection (Ispra

Review of Computational approaches for predicting the physicochemical and biological properties of nanoparticles

In the growing field of nanotechnology there is a need to determine the physicochemical and potential toxicological properties of nanomaterials since many industrial, medical and consumer applications are based on an understanding of these properties and on a controlled exposure to the materials. This document provides a literature review on the current status of computational studies aimed at predicting the physicochemical properties and biological effects (including toxicity) of nanomaterials, with an emphasis on medical applications. Although a number of models have been published for physicochemical property prediction, very few models have been published for predicting biological effects, toxicity or the underlying mechanisms of action. This is due to two main reasons: a) nanomaterials form a colloidal phase when in contact with biological systems making the definition and calculation of properties (descriptors) suitable for the prediction of toxicity a new and challenging task, and b) nanomaterials form a very heterogeneous class of materials, not only in terms of their chemical composition, but also in terms of size, shape, agglomeration state, and surface reactivity. There is thus an urgent need to extend the traditional structure-activity paradigm to develop methods for predicting the toxicity of nanomaterials, and to make the resulting models readily available. This document concludes by proposing some lines of research to fill the gap in knowledge and predictive methodologyJRC.I.6-Systems toxicolog

The Use of Computational Methods in the Grouping and Assessment of Chemicals - Preliminary Investigations

This document presents a perspective of how computational approaches could potentially be used in the grouping and assessment of chemicals, and especially in the application of read-across and the development of chemical categories. The perspective is based on experience gained by the authors during 2006 and 2007, when the Joint Research Centre's European Chemicals Bureau was directly involved in the drafting of technical guidance on the applicability of computational methods under REACH. Some of the experience gained and ideas developed resulted from a number of research-based case studies conducted in-house during 2006 and the first half of 2007. The case studies were performed to explore the possible applications of computational methods in the assessment of chemicals and to contribute to the development of technical guidance. Not all of the methods explored and ideas developed are explicitly included in the final guidance documentation for REACH. Many of the methods are novel, and are still being refined and assessed by the scientific community. At present, many of the methods have not been tried and tested in the regulatory context. The authors therefore hope that the perspective and case studies compiled in this document, whilst not intended to serve as guidance, will nevertheless provide an input to further research efforts aimed at developing computational methods, and at exploring their potential applicability in regulatory assessment of chemicals.JRC.I.3-Toxicology and chemical substance

Chemical Similarity and Threshold of Toxicological Concern (TTC) Approaches: Report of an ECB Workshop held in Ispra, November 2005

There are many national, regional and international programmes – either regulatory or voluntary – to assess the hazards or risks of chemical substances to humans and the environment. The first step in making a hazard assessment of a chemical is to ensure that there is adequate information on each of the endpoints. If adequate information is not available then additional data is needed to complete the dataset for this substance. For reasons of resources and animal welfare, it is important to limit the number of tests that have to be conducted, where this is scientifically justifiable. One approach is to consider closely related chemicals as a group, or chemical category, rather than as individual chemicals. In a category approach, data for chemicals and endpoints that have been already tested are used to estimate the hazard for untested chemicals and endpoints. Categories of chemicals are selected on the basis of similarities in biological activity which is associated with a common underlying mechanism of action. A homologous series of chemicals exhibiting a coherent trend in biological activity can be rationalised on the basis of a constant change in structure. This type of grouping is relatively straightforward. The challenge lies in identifying the relevant chemical structural and physicochemical characteristics that enable more sophisticated groupings to be made on the basis of similarity in biological activity and hence purported mechanism of action. Linking two chemicals together and rationalising their similarity with reference to one or more endpoints has been very much carried out on an ad hoc basis. Even with larger groups, the process and approach is ad hoc and based on expert judgement. There still appears to be very little guidance about the tools and approaches for grouping chemicals systematically. In November 2005, the ECB Workshop on Chemical Similarity and Thresholds of Toxicological Concern (TTC) Approaches was convened to identify the available approaches that currently exist to encode similarity and how these can be used to facilitate the grouping of chemicals. This report aims to capture the main themes that were discussed. In particular, it outlines a number of different approaches that can facilitate the formation of chemical groupings in terms of the context under consideration and the likely information that would be required. Grouping methods were divided into one of four classes – knowledge-based, analogue-based, unsupervised, and supervised. A flowchart was constructed to attempt to capture a possible work flow to highlight where and how these approaches might be best applied.JRC.I.3-Toxicology and chemical substance

Quantum-SAR Extension of the Spectral-SAR Algorithm. Application to Polyphenolic Anticancer Bioactivity

Aiming to assess the role of individual molecular structures in the molecular mechanism of ligand-receptor interaction correlation analysis, the recent Spectral-SAR approach is employed to introduce the Quantum-SAR (QuaSAR) “wave” and “conversion factor” in terms of difference between inter-endpoint inter-molecular activities for a given set of compounds; this may account for inter-conversion (metabolization) of molecular (concentration) effects while indicating the structural (quantum) based influential/detrimental role on bio-/eco- effect in a causal manner rather than by simple inspection of measured values; the introduced QuaSAR method is then illustrated for a study of the activity of a series of flavonoids on breast cancer resistance protein

Molecular quantum similarity in QSAR: applications in computer-aided molecular design

La present tesi està centrada en l'ús de la Teoria de Semblança Quàntica per a calcular descriptors moleculars. Aquests descriptors s'utilitzen com a paràmetres estructurals per a derivar correlacions entre l'estructura i la funció o activitat experimental per a un conjunt de compostos. Els estudis de Relacions Quantitatives Estructura-Activitat són d'especial interès per al disseny racional de molècules assistit per ordinador i, en particular, per al disseny de fàrmacs. Aquesta memòria consta de quatre parts diferenciades. En els dos primers blocs es revisen els fonaments de la teoria de semblança quàntica, així com l'aproximació topològica basada en la teoria de grafs. Ambdues teories es fan servir per a calcular els descriptors moleculars. En el segon bloc, s'ha de remarcar la programació i implementació de programari per a calcular els anomenats índexs topològics de semblança quàntica. La tercera secció detalla les bases de les Relacions Quantitatives Estructura-Activitat i, finalment, el darrer apartat recull els resultats d'aplicació obtinguts per a diferents sistemes biològics.The present thesis is centred in the use of the Quantum Similarity Theory to calculate molecular descriptors. These molecular descriptors are used as structural parameters to derive correlations between the structure and the function or experimental activity for a set of compounds. Quantitative Structure-Activity Relationship studies are of special interest for the rational Computer-Aided Molecular Design and, in particular, for Computer-Aided Drug Design. The memory has been structured in four differenced parts. The two first blocks revise the foundations of quantum similarity theory, as well as the topological approximation, based in classical graph theory. These theories are used to calculate the molecular descriptors. In the second block, the programming and implementation of Topological Quantum Similarity Indices must be remarked. The third section details the basis for Quantitative Structure-Activity Relationships and, finally, the last section gathers the application results obtained for different biological systems

Mini-Review on Chemical Similarity and Prediction of Toxicity

The notion of similarity relates to a relative comparison between different systems. The process of establishing similarities and analogies by humans is heuristic and subjective. Similarity is a context dependent and a relative measure. It is only meaningful to say that x is similar to y with respect to z. In toxicology and drug design it is important to have an objective measure of similarity to compare two or more chemicals with respect to their activity or toxicity. Similarity assessment based on structures is a convenient and popular means of comparison but needs to account for each specific activity or toxicity. This mini review will start by providing an overview of the history and philosophy of similarity in general. It will then describe the different means of quantifying chemicals and how these numerical descriptors can be applied in so-called similarity indices to compare chemicals with respect to their activity or toxicity. The use of a varied wealth of similarity indices applied to the same study case is analyzed and compared throughout.JRC.I.3-Toxicology and chemical substance

Prediction of Estrogenicity: Validation of a Classification Model

(Q)SAR methods can be used to reduce animal testing as well as to minimise the testing costs. In particular, classification models have been widely used for estimating endpoints with binary activity. The aim of the present study was to develop and validate a classification-based quantitative structure-activity relationship (QSAR) model for endocrine disruption, based on interpretable mechanistic descriptors related to estrogenic gene activation. The model predicts the presence or absence of estrogenic activity as determined in a recombinant yeast assay. The experimental data was obtained from the literature. A two-descriptor classification model was developed that has the form of a decision tree. The predictivity of the model was evaluated by using an external test set and by taking into account the limitations associated with the applicability domain (AD) of the model. The AD was determined as coverage in the model descriptor space. After removing the compounds present in the training set and the compounds outside of the AD, the overall accuracy of classification of the test chemicals was used to assess the predictivity of the model. In addition, the model was shown to meet the OECD Principles for (Q)SAR Validation, making it potentially useful for regulatory purposes.JRC.I.3-Toxicology and chemical substance

Review of (Q)SAR Models for Skin and Eye Irritation and Corrosion

This paper reviews the state-of-the-art of in silico methods for assessing dermal and ocular irritation and corrosion. It is based on an in-depth review performed by the European Chemicals Bureau of the European Commission's Joint Research Centre in support of the development of technical guidance for the implementation of the REACH legislation, and is one of a series of mini-reviews in this journal. The most widely used in silico approaches are classified into methods to assess 1) skin irritation; 2) skin corrosion; and 3) eye irritation. In this review, emphasis is placed on literature-based (Q)SAR models.JRC.I.3-Toxicology and chemical substance