Profiling Chemicals Based on Chronic Toxicity Results from the U.S. EPA ToxRef Database

Attribute filters allow enhancement and extraction of features without distorting their borders, and never introduce new image features. These are highly desirable properties in biomedical imaging, where accurate shape analysis is paramount. However, setting the attribute-threshold parameters has to date only been done manually. This paper explores simple, fast and automated methods of computing attribute threshold parameters based on image segmentation, thresholding and data clustering techniques. Though several techniques perform well on blood-vessel filtering, the choice of technique appears to depend on the imaging mode.

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

Bayesian and Frequentist Approaches for the Analysis of Multiple Endpoints Data Resulting from Exposure to Multiple Health Stressors.

In risk analysis, Benchmark dose (BMD)methodology is used to quantify the risk associated with exposure to stressors such as environmental chemicals. It consists of fitting a mathematical model to the exposure data and the BMD is the dose expected to result in a pre-specified response or benchmark response (BMR). Most available exposure data are from single chemical exposure, but living objects are exposed to multiple sources of hazards. Furthermore, in some studies, researchers may observe multiple endpoints on one subject. Statistical approaches to address multiple endpoints problem can be partitioned into a dimension reduction group and a dimension preservative group. Composite scores using desirability function is used, as a dimension reduction method, to evaluate neurotoxicity effects of a mixture of five organophosphate pesticides (OP) at a fixed mixing ratio ray, and five endpoints were observed. Then, a Bayesian hierarchical model approach, as a single unifying dimension preservative method is introduced to evaluate the risk associated with the exposure to mixtures chemicals. At a pre-specied vector of BMR of interest, the method estimates a tolerable area referred to as benchmark dose tolerable area (BMDTA) in multidimensional Euclidean plan. Endpoints defining the BMDTA are determined and model uncertainty and model selection problems are addressed by using the Bayesian Model Averaging (BMA) method

Statistical evaluation of toxicological bioassays - a review

The basic conclusions in almost all reports on new drug applications and in all publications in toxicology are based on statistical methods. However, serious contradictions exist in practice: designs with small samples sizes but use of asymptotic methods (i.e. constructed for larger sample sizes), statistically significant findings without biological relevance (and vice versa), proof of hazard vs. proof of safety, testing (e.g. no observed effect level) vs. estimation (e.g. benchmark dose), available statistical theory vs. related user-friendly software. In this review the biostatistical developments since about the year 2000 onwards are discussed, mainly structured for repeated-dose studies, mutagenicity, carcinogenicity, reproductive and ecotoxicological assays. A critical discussion is included on the unnecessarily conservative evaluation proposed in guidelines, the inadequate but almost always used proof of hazard approach, and the limitation of data-dependent decision-tree approaches

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

Expression profiling of key pathways in rat liver after a one-year feeding trial with transgenic maize MON810

In a recent one-year feeding study, we observed no adverse effects on tissue level in organs of rats fed with the genetically-modified maize MON810. Here, we assessed RNA expression levels of 86 key genes of the apoptosis-, NF-кB-, DNA-damage response (DDR)-, and unfolded-protein response (UPR) pathways by RT-qPCR in the rat liver. Male and female rats were fed either with 33% MON810 (GMO), isogenic- (ISO), or conventional maize (CONV) and RNAs were quantified from eight rats from each of the six feeding groups. Only Birc2 transcript showed a significant (p ≤ 0.05) consistent difference of ≥1.5-fold between the GMO and ISO groups in both sexes. Unsupervised cluster analysis showed a strong separation of male and female rats, but no clustering of the feeding groups. Individual analysis of the pathways did not show any clustering of the male or female feeding groups either, though transcript levels of UPR pathway-associated genes caused some clustering of the male GMO and CONV feeding group samples. These differences were not seen between the GMO and ISO control or within the female cohort. Our data therefore does not support an adverse effect on rat liver RNA expression through the long-term feeding of MON810 compared to isogenic control maize

Uncertainty and Variability Analysis in the Estimation of Human Exposure to Mercury from Seafood Consumption Using Two-Dimensional Monte Carlo Simulations

Fish and shellfish are a beneficial food source due to their high protein content, low saturated fat content, and low cholesterol content; they possess beneficial omega-3 fatty acids and antioxidants such as vitamin E and selenium. Recent studies identified a decreased risk of sudden cardiac death in seafood consumers. Per capita seafood consumption increased 27% from 1975 to 1990, with nearly 95% of the U.S. population consuming some kind of fish or shellfish product each year. But seafood consumption may pose health risks which need to be weighed against benefits. One of potential risks is the toxicity of methylmercury. Mercury accumulates dramatically in the aquatic food web, where nearly all is converted into methylmercury. Almost all fish and shellfish contain at least trace amounts of methylmercury, and seafood consumption is the source of nearly all human methylmercury exposure. Major methylmercury poisonings provide evidence that the health effect of greatest concern from methylmercury exposure is neurotoxicity in the fetus and infant. Exposure assessments are the crucial step in identifying populations at risk of deleterious effects. Although several methods are currently available to project exposure, additional techniques may be necessary to accurately characterize exposure. One major complication in assessing exposure is the fact that all contributing factors are subject to variability, uncertainty, or to both. Variability describes heterogeneity in a well-characterized population, while uncertainty accounts for partial ignorance about a characteristic. Unfortunately, current exposure estimation techniques fail to simultaneously represent the full range and probability of exposures, account for dependencies among variables, and partition effects of variability from uncertainty. Thus, an innovative statistical methodology is developed here to overcome these deficiencies. Statistical techniques are also utilized to determine those input variables that are most critical to exposure. The different methods discussed and developed here may be used to estimate contaminant exposure from seafood consumption. Among other contaminants and marine species, methylmercury exposure from the consumption of Northern lobster, Homarus americanus, is currently of interest. Lobster is one of the more commonly consumed marine species, and it has the potential to bioaccumulate high methylmercury levels

CAESAR models for developmental toxicity

<p>Abstract</p> <p>Background</p> <p>The new REACH legislation requires assessment of a large number of chemicals in the European market for several endpoints. Developmental toxicity is one of the most difficult endpoints to assess, on account of the complexity, length and costs of experiments. Following the encouragement of QSAR (<it>in silico</it>) methods provided in the REACH itself, the CAESAR project has developed several models.</p> <p>Results</p> <p>Two QSAR models for developmental toxicity have been developed, using different statistical/mathematical methods. Both models performed well. The first makes a classification based on a random forest algorithm, while the second is based on an adaptive fuzzy partition algorithm. The first model has been implemented and inserted into the CAESAR on-line application, which is java-based software that allows everyone to freely use the models.</p> <p>Conclusions</p> <p>The CAESAR QSAR models have been developed with the aim to minimize false negatives in order to make them more usable for REACH. The CAESAR on-line application ensures that both industry and regulators can easily access and use the developmental toxicity model (as well as the models for the other four endpoints).</p

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

Convex mixture regression for quantitative risk assessment

There is wide interest in studying how the distribution of a continuous response changes with a predictor. We are motivated by environmental applications in which the predictor is the dose of an exposure and the response is a health outcome. A main focus in these studies is inference on dose levels associated with a given increase in risk relative to a baseline. In addressing this goal, popular methods either dichotomize the continuous response or focus on modeling changes with the dose in the expectation of the outcome. Such choices may lead to information loss and provide inaccurate inference on dose-response relationships. We instead propose a Bayesian convex mixture regression model that allows the entire distribution of the health outcome to be unknown and changing with the dose. To balance flexibility and parsimony, we rely on a mixture model for the density at the extreme doses, and express the conditional density at each intermediate dose via a convex combination of these extremal densities. This representation generalizes classical dose-response models for quantitative outcomes, and provides a more parsimonious, but still powerful, formulation compared to nonparametric methods, thereby improving interpretability and efficiency in inference on risk functions. A Markov chain Monte Carlo algorithm for posterior inference is developed, and the benefits of our methods are outlined in simulations, along with a study on the impact of dde exposure on gestational age