Biomedicals from Bone

The realm of biomaterials, under which biomedical materials can be categorised, has a broad definition base and recognises materials that are synthesized or naturally sourced. Biomaterials are normally those that come into contact with live tissue and physiological fluids. They have applications as prostheses to replace lost function of joints or to replace bone tissue, for diagnosing medical conditions, as a form of therapy, or as a storage unit. The diversity and scope of biomaterials science research, and especially its application to the improvement of trauma, disease, and congenital defects in the human condition, are making this branch of science increasingly dominant and topical in many countries. An exciting aspect is that such research is interdisciplinary. The varied problems of the human condition that biomaterials research addresses occupy the efforts not only of medical doctors who act as the end users of such technology, but also those of chemists, physicists, engineers, and biologists in creating the technological advances. Chemistry, in particular, plays a major role in such research, after all it is the foundation stone on which biomaterials polymer science and biomedical scaffold materials are built

A Recurrent Cooperative/Competitive Field for Segmentation of Magnetic Resonance Brain Imagery

The Grey-White Decision Network is introduced as an application of an on-center, off-surround recurrent cooperative/competitive network for segmentation of magnetic resonance imaging (MRI) brain images. The three layer dynamical system relaxes into a solution where each pixel is labeled as either grey matter, white matter, or "other" matter by considering raw input intensity, edge information, and neighbor interactions. This network is presented as an example of applying a recurrent cooperative/competitive field (RCCF) to a problem with multiple conflicting constraints. Simulations of the network and its phase plane analysis are presented

In Silico Prediction of Physicochemical Properties

This report provides a critical review of computational models, and in particular(quantitative) structure-property relationship (QSPR) models, that are available for the prediction of physicochemical properties. The emphasis of the review is on the usefulness of the models for the regulatory assessment of chemicals, particularly for the purposes of the new European legislation for the Registration, Evaluation, Authorisation and Restriction of CHemicals (REACH), which entered into force in the European Union (EU) on 1 June 2007. It is estimated that some 30,000 chemicals will need to be further assessed under REACH. Clearly, the cost of determining the toxicological and ecotoxicological effects, the distribution and fate of 30,000 chemicals would be enormous. However, the legislation makes it clear that testing need not be carried out if adequate data can be obtained through information exchange between manufacturers, from in vitro testing, and from in silico predictions. The effects of a chemical on a living organism or on its distribution in the environment is controlled by the physicochemical properties of the chemical. Important physicochemical properties in this respect are, for example, partition coefficient, aqueous solubility, vapour pressure and dissociation constant. Whilst all of these properties can be measured, it is much quicker and cheaper, and in many cases just as accurate, to calculate them by using dedicated software packages or by using (QSPRs). These in silico approaches are critically reviewed in this report.JRC.I.3-Toxicology and chemical substance

Review of Data Sources, QSARs and Integrated Testing Strategies for Skin Sensitisation

This review collects information on sources of skin sensitisation data and computational tools for the estimation of skin sensitisation potential, such as expert systems and (quantitative) structure-activity relationship (QSAR) models. The review also captures current thinking of what constitutes an integrated testing strategy (ITS) for this endpoint. The emphasis of the review is on the usefulness of the models for the regulatory assessment of chemicals, particularly for the purposes of the new European legislation for the Registration, Evaluation, Authorisation and Restriction of CHemicals (REACH), which entered into force on 1 June 2007. Since there are no specific databases for skin sensitisation currently available, a description of experimental data found in various literature sources is provided. General (global) models, models for specific chemical classes and mechanisms of action and expert systems are summarised. This review was prepared as a contribution to the EU funded Integrated Project, OSIRIS.JRC.I.3-Consumer products safety and qualit

Assessment of dynamic and long-term performance of an innovative multi-story timber building via structural monitoring and dynamic testing

Peer reviewedPostprin

Analysis of the Cramer classification scheme for oral systemic toxicity - implications for its implementation in Toxtree

In the application of the Threshold of Toxicological Concern (TTC) concept to non-cancer endpoints, the decision tree proposed by Cramer, Ford and Hall in 1978, commonly referred to as the Cramer scheme, is probably the most widely used approach for classifying and ranking chemicals according to their expected level of oral systemic toxicity. The decision tree categorises chemicals, mainly on the basis of chemical structure and reactivity, into three classes indicating a high (Class III), medium (Class II) or low (Class I) level of concern. Each Cramer class is associated with a specified human exposure level, below which chemicals are considered to present a negligible risk to human health. In the absence of experimental hazard data, these exposure threshold (TTC) values have formed the basis of priority setting in the risk assessment process. To facilitate the application of the TTC approach, the original Cramer scheme, and an extended version, have been implemented in Toxtree, a freely available software tool for predicting toxicological effects and mechanisms of action. Building on previous work by Patlewicz and coworkers, this report provides some suggestions for improving the Cramer scheme based on a review of the scientific literature, a survey of Toxtree users, and an analysis of lists of body and food components incorporated in Toxtree.JRC.DG.I.6-Systems toxicolog

Review of Software Tools for Toxicity Prediction

When assessing the properties of chemicals, the easiest and most consistent way of applying (Quantitative) Structure-Activity Relationship ([Q]SAR) models is to use ready-made software that implements the models via a user-friendly interface. A wide range of software tools are available for predicting physicochemical properties, toxicological endpoints and other biological effects, as well as fate in the environment and biological organisms. Typically, a given software package predicts multiple properties and endpoints, and some are extensible, allowing the user to develop new models or include new knowledge. In addition to (Q)SAR models and rulebases that are incorporated in software tools, there is a growing scientific literature which reports thousands of (Q)SARs. In this report, we give an overview of the software packages that are commonly used in the assessment of chemical toxicity. These software packages are potentially useful in the hazard and risk assessment of chemicals, including for regulatory purposes. However, the applicability of any given software tool needs to be carefully evaluated and documented.JRC.DG.I.6 - Systems toxicolog

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

Review of QSAR Models and Software Tools for predicting Biokinetic Properties

In the assessment of industrial chemicals, cosmetic ingredients, and active substances in pesticides and biocides, metabolites and degradates are rarely tested for their toxicologcal effects in mammals. In the interests of animal welfare and cost-effectiveness, alternatives to animal testing are needed in the evaluation of these types of chemicals. In this report we review the current status of various types of in silico estimation methods for Absorption, Distribution, Metabolism and Excretion (ADME) properties, which are often important in discriminating between the toxicological profiles of parent compounds and their metabolites/degradation products. The review was performed in a broad sense, with emphasis on QSARs and rule-based approaches and their applicability to estimation of oral bioavailability, human intestinal absorption, blood-brain barrier penetration, plasma protein binding, metabolism and. This revealed a vast and rapidly growing literature and a range of software tools. While it is difficult to give firm conclusions on the applicability of such tools, it is clear that many have been developed with pharmaceutical applications in mind, and as such may not be applicable to other types of chemicals (this would require further research investigation). On the other hand, a range of predictive methodologies have been explored and found promising, so there is merit in pursuing their applicability in the assessment of other types of chemicals and products. Many of the software tools are not transparent in terms of their predictive algorithms or underlying datasets. However, the literature identifies a set of commonly used descriptors that have been found useful in ADME prediction, so further research and model development activities could be based on such studies.JRC.DG.I.6-Systems toxicolog