108 research outputs found

    Lessons Learned from the Grouping of Chemicals to Assess Risks to Human Health

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    In analogy to the periodic system that groups elements by their similarity in structure and chemical properties, the hazard of chemicals can be assessed in groups of similar structures and similar toxicological properties. Here we review case studies of grouping strategies that supported the assessment of hazard, exposure, and risk to human health. By the EU-REACh and the US-TSCA New Chemicals Program, structural similarity is commonly used as the basis for grouping, but that criterion is not always adequate and sufficient. Based on the lessons learned, we derive ten principles for grouping, including: transparency of the purpose, criteria and boundaries of the group; adequacy of methods used to justify the group; inclusion or exclusion of substances in the group by toxicological properties. These principles apply to initial grouping to prioritize further actions as well as to definitive grouping to generate data for risk assessment. Both can expedite effective risk management

    Toward Realistic Dosimetry In Vitro: Determining Effective Concentrations of Test Substances in Cell Culture and Their Prediction by an In Silico Mass Balance Model

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    Nominal concentrations (CNom) in cell culture media are routinely used to define concentration–effect relationships in the in vitro toxicology. The actual concentration in the medium (CMedium) can be affected by adsorption processes, evaporation, or degradation of chemicals. Therefore, we measured the total and free concentration of 12 chemicals, covering a wide range of lipophilicity (log KOW −0.07–6.84), in the culture medium (CMedium) and cells (CCell) after incubation with Balb/c 3T3 cells for up to 48 h. Measured values were compared to predictions using an as yet unpublished in silico mass balance model that combined relevant equations from similar models published by others. The total CMedium for all chemicals except tamoxifen (TAM) were similar to the CNom. This was attributed to the cellular uptake of TAM and accumulation into lysosomes. The free (i.e., unbound) CMedium for the low/no protein binding chemicals were similar to the CNom, whereas values of all moderately to highly protein-bound chemicals were less than 30% of the CNom. Of the 12 chemicals, the two most hydrophilic chemicals, acetaminophen (APAP) and caffeine (CAF), were the only ones for which the CCell was the same as the CNom. The CCell for all other chemicals tended to increase over time and were all 2- to 274-fold higher than CNom. Measurements of CCytosol, using a digitonin method to release cytosol, compared well with CCell (using a freeze–thaw method) for four chemicals (CAF, APAP, FLU, and KET), indicating that both methods could be used. The mass balance model predicted the total CMedium within 30% of the measured values for 11 chemicals. The free CMedium of all 12 chemicals were predicted within 3-fold of the measured values. There was a poorer prediction of CCell values, with a median overprediction of 3- to 4-fold. In conclusion, while the number of chemicals in the study is limited, it demonstrates the large differences between CNom and total and free CMedium and CCell, which were also relatively well predicted by the mass balance model

    Genotoxicity testing of nanomaterials

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    Nanomaterials have outstanding and unprecedented advantageous material properties but may also cause adverse effects in humans upon exposure. Testing nanomaterials for genotoxic properties is challenging because traditional testing methods were designed for small, soluble molecules and may not be easily applicable without modifications. This review critically examines available genotoxicity tests for use with nanomaterials, including DNA damage tests such as the comet assay, gene mutation tests such as the mouse lymphoma and hprt assay, and chromosome mutation tests such as the micronucleus test and the chromosome aberration test. It presents arguments for the relative usefulness of various tests, such as preferring the micronucleus test over the chromosome aberration test for scoring chromosome mutations and preferring mammalian cell gene mutation tests because the Ames test has limited utility. Finally, it points out the open questions and further needs in adapting genotoxicity tests for nanomaterials, such as validation, reference nanomaterials, and the selection of top test concentrations, as well as the relevance and applicability of test systems and the need to define testing strategies. This article is categorized under: Toxicology and Regulatory Issues in Nanomedicine > Toxicology of Nanomaterials Toxicology and Regulatory Issues in Nanomedicine > Regulatory and Policy Issues in Nanomedicin

    In memoriam Thomas Gebel

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    Lehren aus dem Gruppieren von Chemikalien zur Bewertung der Risiken für die Gesundheit des Menschen

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    Analog zum Periodensystem, das Elemente nach ihrer Ähnlichkeit der Strukturen und chemischen Eigenschaften gruppiert, kann die Gefahr chemischer Stoffe in Gruppen mit ähnlichen Strukturen und ähnlichen toxikologischen Eigenschaften bewertet werden. Im Folgenden werden Fallbeispiele zu Gruppierungsstrategien vorgestellt, welche die Bewertungen von Gefahr, Exposition und Risiko für die menschliche Gesundheit unterstützen. Sowohl unter EU‐REACh als auch im US‐TSCA New Chemicals Programm ist in der Regel die strukturelle Ähnlichkeit die Grundlage für eine Gruppierungt. Allerdings ist dieses Kriterium nicht immer angemessen und ausreichend. Auf der Grundlage der gewonnenen Erkenntnisse leiten wir zehn Grundsätze für die Gruppierung ab, darunter: die transparente Darstellung des Zwecks der Gruppierung, die Definition der Kriterien für eine Gruppierung und die Grenzen der Gruppen, eine dieEinbeziehung oder der Ausschluss eines Stoffs in oder aus einer Gruppe aufgrund toxikologischer Eigenschaften und eine Begründung der Zuordnung durch robuste Daten aus angemessenen Methoden. Diese Grundsätze gelten sowohl für eine erste Gruppierung zur Priorisierung weiterer Maßnahmen als auch für die endgültige Gruppierung zur Gewinnung von Daten für die Risikobewertung. Beides kann ein effektives Risikomanagement forcieren

    An in vitro alveolar macrophage assay for predicting the short-term inhalation toxicity of nanomaterials

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    Additional file 1: Table S1. Comparison of significant in vitro LOAECs (significant as compared to the negative benchmark material corundum) to NOAECs and LOAECs recorded in rat STISs. Table S2. Bioactivity of four types of CeO2 NMs in rat STISs as compared to cellular effects recorded in the in vitro NR8383 AM assay

    Feasibility Assessment of Micro-Electrode Chip Assay as a Method of Detecting Neurotoxicity in vitro

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    Detection and characterization of chemically induced toxic effects in the nervous system represent a challenge for the hazard assessment of chemicals. In vivo, neurotoxicological assessments exploit the fact that the activity of neurons in the central and peripheral nervous system has functional consequences. And so far, no in vitro method for evaluating the neurotoxic hazard has yet been validated and accepted for regulatory purpose. The micro-electrode array (MEA) assay consists of a culture chamber into which an integrated array of micro-electrodes is capable of measuring extracellular electrophysiology (spikes and bursts) from electro-active tissues. A wide variety of electrically excitable biological tissues may be placed onto the chips including primary cultures of nervous system tissue. Recordings from this type of in vitro cultured system are non-invasive, give label free evaluations and provide a higher throughput than conventional electrophysiological techniques. In this paper, 20 substances were tested in a blinded study for their toxicity and dose–response curves were obtained from fetal rat cortical neuronal networks coupled to MEAs. The experimental procedure consisted of evaluating the firing activity (spiking rate) and modification/reduction in response to chemical administration. Native/reference activity, 30 min of activity recording per dilution, plus the recovery points (after 24 h) were recorded. The preliminary data, using a set of chemicals with different mode-of-actions (13 known to be neurotoxic, 2 non-neuroactive and not toxic, and 5 non-neuroactive but toxic) show good predictivity (sensitivity: 0.77; specificity: 0.86; accuracy: 0.85). Thus, the MEA with a neuronal network has the potency to become an effective tool to evaluate the neurotoxicity of substances in vitro

    KnowTox: pipeline and case study for confident prediction of potential toxic effects of compounds in early phases of development

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    Risk assessment of newly synthesised chemicals is a prerequisite for regulatory approval. In this context, in silico methods have great potential to reduce time, cost, and ultimately animal testing as they make use of the ever-growing amount of available toxicity data. Here, KnowTox is presented, a novel pipeline that combines three different in silico toxicology approaches to allow for confident prediction of potentially toxic effects of query compounds, i.e. machine learning models for 88 endpoints, alerts for 919 toxic substructures, and computational support for read-across. It is mainly based on the ToxCast dataset, containing after preprocessing a sparse matrix of 7912 compounds tested against 985 endpoints. When applying machine learning models, applicability and reliability of predictions for new chemicals are of utmost importance. Therefore, first, the conformal prediction technique was deployed, comprising an additional calibration step and per definition creating internally valid predictors at a given significance level. Second, to further improve validity and information efficiency, two adaptations are suggested, exemplified at the androgen receptor antagonism endpoint. An absolute increase in validity of 23% on the in-house dataset of 534 compounds could be achieved by introducing KNNRegressor normalisation. This increase in validity comes at the cost of efficiency, which could again be improved by 20% for the initial ToxCast model by balancing the dataset during model training. Finally, the value of the developed pipeline for risk assessment is discussed using two in-house triazole molecules. Compared to a single toxicity prediction method, complementing the outputs of different approaches can have a higher impact on guiding toxicity testing and de-selecting most likely harmful development-candidate compounds early in the development process
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