approaches in carcinogenicity hazard assessment: case study of pregabalin, a nongenotoxic mouse carcinogen.

toxicology protocols are meant to support computationally-based assessments using principles that ensure that results can be generated, recorded, communicated, archived, and then evaluated in a uniform, consistent, and reproducible manner. We investigated the availability of models to predict the carcinogenic potential of pregabalin using the ten key characteristics of carcinogens as a framework for organizing mechanistic studies. Pregabalin is a single-species carcinogen producing only one type of tumor, hemangiosarcomas in mice via a nongenotoxic mechanism.

Approaches In Carcinogenicity Hazard Assessment: Current Status and Future Needs.

Historically, identifying carcinogens has relied primarily on tumor studies in rodents, which require enormous resources in both money and time. models have been developed for predicting rodent carcinogens but have not yet found general regulatory acceptance, in part due to the lack of a generally accepted protocol for performing such an assessment as well as limitations in predictive performance and scope. There remains a need for additional, improved carcinogenicity models, especially ones that are more human-relevant, for use in research and regulatory decision-making.

Using VEGAHUB Within a Weight-of-Evidence Strategy.

Industrial needs and regulatory requirements have played a significant role in accelerating the use of nontesting methods including in silico tools as alternatives to animal testing. The main interest is not solely on the use of in silico tools, or in read-across, but on better toxicological safety assessment of substances, and for this purpose more advanced, integrated strategies have to be implemented. VEGAHUB wants to promote this broader view, not necessarily focused on a specific approach.

SpheraCosmolife: a new tool for the risk assessment of cosmetic products.

A new, freely available software for cosmetic products has been designed that considers the regulatory framework for cosmetics. The software allows an overall toxicological evaluation of cosmetic ingredients without the need for additional testing and, depending on the product type, it applies defined exposure scenarios to derive risk for consumers. It takes regulatory thresholds into account and uses either experimental values, if available, or predictions.

Prediction of the Partition Coefficient between Adipose Tissue and Blood for Environmental Chemicals: From Single QSAR Models to an Integrated Approach.

The adipose tissue:blood partition coefficient is a key-endpoint to predict the pharmacokinetics of chemicals in humans and animals, since other organ:blood affinities can be estimated as a function of this parameter. We performed a search in the literature to select all the available rat in vivo data. This approach resulted into two improvements to existing models: a homogeneous definition of the endpoint and an expanded data collection.

Value and limitation of structure-based profilers to characterize developmental and reproductive toxicity potential.

The uncertainty regarding the safety of chemicals leaching from food packaging triggers attention. In silico models provide solutions for screening of these chemicals, since many are toxicologically uncharacterized. For hazard assessment, information on developmental and reproductive toxicity (DART) is needed.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity.

Background: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) approaches and computational modeling.

Development, validation and integration of in silico models to identify androgen active chemicals.

Humans are exposed to large numbers of environmental chemicals, some of which potentially interfere with the endocrine system. The identification of potential endocrine disrupting chemicals (EDCs) has gained increasing priority in the assessment of environmental hazards. The U.

QSAR Modeling of ToxCast Assays Relevant to the Molecular Initiating Events of AOPs Leading to Hepatic Steatosis.

Nonalcoholic hepatic steatosis is a worldwide epidemiological concern since it is among the most prominent hepatic diseases. Indeed, research in toxicology and epidemiology has gathered evidence that exposure to endocrine disruptors can perturb cellular homeostasis and cause this disease. Therefore, assessing the likelihood of a chemical to trigger hepatic steatosis is a matter of the utmost importance.

Integrating computational methods to predict mutagenicity of aromatic azo compounds.

Azo dyes have several industrial uses. However, these azo dyes and their degradation products showed mutagenicity, inducing damage in environmental and human systems. Computational methods are proposed as cheap and rapid alternatives to predict the toxicity of azo dyes.

Integrated strategy for mutagenicity prediction applied to food contact chemicals.

Food contamination due to unintentional leakage of chemicals from food contact materials (FCM) is a source of increasing concern. Since for many of these substances, only limited or no toxicological data are available, the development of alternative methodologies to establish rapidly and cost-efficiently level of safety concern is critical to ensure adequate consumer protection. Computational toxicology methods are considered the most promising solutions to cope with this data gap.

Nano-QSAR Model for Predicting Cell Viability of Human Embryonic Kidney Cells.

Traditional Quantitative Structure-Activity Relationships (QSAR) models based on molecular descriptors as translators of chemical information show some drawbacks in predicting toxicity of nanomaterials due to their unique properties and to their nonhomogeneous structure.This chapter provides instructions on how to use CORAL, freely available software for building nano-QSAR models. CORAL makes use of descriptors based on "quasi-SMILES" representing physicochemical features and/or experimental conditions as an alternative to traditional SMILES encoding chemical structure to build up predictive nano-QSAR models for cytotoxicity.

(Q)SAR tools for priority setting: A case study with printed paper and board food contact material substances.

Over the last years, more stringent safety requirements for an increasing number of chemicals across many regulatory fields (e.g. industrial chemicals, pharmaceuticals, food, cosmetics, …) have triggered the need for an efficient screening strategy to prioritize the substances of highest concern.

[The Ormond's disease: an intriguing obstructive nephrouropathy].

Idiopathic retroperitoneal fibrosis also known as Ormonds disease is a rare disorder characterized by the development of fibrotic tissue in the retroperitoneum involving the abdominal aorta and iliac arteries, ureters and the inferior vena cava. The aberrant tissue may compress ureters leading to obstructive nephrouropathy and renal failure, which are the most common clinical manifestations of this condition. The nephrologist is often consulted to make differential diagnosis for acute renal failure and obstructive uropathy.

Comparing the CORAL and Random Forest approaches for modelling the in vitro cytotoxicity of silica nanomaterials.

Nanotechnology is one of the most important technological developments of the 21st century. In silico methods to predict toxicity, such as quantitative structure-activity relationships (QSARs), promote the safe-by-design approach for the development of new materials, including nanomaterials. In this study, a set of cytotoxicity experimental data corresponding to 19 data points for silica nanomaterials were investigated, to compare the widely employed CORAL and Random Forest approaches in terms of their usefulness for developing so-called 'nano-QSAR' models.

A knowledge-based expert rule system for predicting mutagenicity (Ames test) of aromatic amines and azo compounds.

Cancer is one of the main causes of death in Western countries, and a major issue for human health. Prolonged exposure to a number of chemicals was observed to be one of the primary causes of cancer in occupationally exposed persons. Thus, the development of tools for identifying hazardous chemicals and the increase of mechanistic understanding of their toxicity is a major goal for scientific research.

New Quantitative Structure-Activity Relationship Models Improve Predictability of Ames Mutagenicity for Aromatic Azo Compounds.

Existing Quantitative Structure-Activity Relationship (QSAR) models have limited predictive capabilities for aromatic azo compounds. In this study, 2 new models were built to predict Ames mutagenicity of this class of compounds. The first one made use of descriptors based on simplified molecular input-line entry system (SMILES), calculated with the CORAL software.

Use of Read-Across Tools.

Read-across has become popular since the introduction of regulations, such as the European REACH regulation. This chapter provides instructions on how to use ToxRead, new freely available software for read-across analysis, and on how to interpret its output predictions for mutagenicity assessments.This tool offers two seminal sources: a set of rules/structural alerts, which may explain the toxicity, and a similarity tool, associated with a large database of chemicals with their properties.

[Management of antirheumatic drugs in kidney failure].

The nephrologist deals with the management of patients with rheumatic disease, both diagnostically and therapeutically. He must determine whether the renal pathology is related to the rheumatologic disease, mostly through the use of the renal biopsy. In the second case, he must know the nephrotoxic potential of the drugs prescribed and adjust their use to the degree of renal impairment.

QSAR model for predicting cell viability of human embryonic kidney cells exposed to SiO₂ nanoparticles.

A predictive model for the viability (%) of cultured human embryonic kidney cells (HEK293) exposed to 20 and 50 nm silica nanoparticles was built using 'optimal descriptors' as mathematical functions of size, concentration and exposure time. The calculation was carried out with CORAL software (http://www.insilico.

Hierarchical Rules for Read-Across and In Silico Models of Mutagenicity.

A broad set of rules has been implemented within the ToxRead software for read-across of chemicals for bacterial mutagenicity. These rules were obtained by manually analyzing more than 6000 chemicals and the associated chemical classes. A hierarchy of rules was established to identify those most specifically relating to the target compounds, linked in sequence to the other, more generic ones, which may match with the target compound.

Synthesis, biological evaluation, and docking studies of PAR2-AP-derived pseudopeptides as inhibitors of kallikrein 5 and 6.

A series of protease activated receptor 2 activating peptide (PAR2-AP) derivatives (1-15) were designed and synthesized. The obtained compounds were tested on a panel of human kallikreins (hKLK1, hKLK2, hKLK5, hKLK6, and hKLK7) and were found completely inactive toward hKLK1, hKLK2, and hKLK7. Aiming to investigate the mode of interaction between the most interesting compounds and the selected hKLKs, docking studies were performed.

Identification of a pepducin acting as S1P3 receptor antagonist.

Sphingosine-1-phosphate (S1P) is a bioactive lipid with key functions in the immune, inflammatory, and cardiovascular systems. S1P exerts its action through the interaction with a family of five known G protein-coupled receptors, named S1P(1-5). Among them, S1P(3) has been implicated in the pathological processes of a number of diseases, including sepsis and cancer.

Validation of quantitative structure-activity relationship models to predict water-solubility of organic compounds.

Water-solubility is an important physicochemical property in pharmaceutical and environmental studies. We assessed the performance of five predictive computer models: ACD/PhysChem History, ADMET Predictor, T.E.