A new approach for the speciation of metallothioneins (MT) in human brain cytosols is described. The analysis is performed by application of a newly developed coupling of capillary electrophoresis (CE) with inductively coupled plasma-sector field mass spectrometry (ICP-SFMS). Isoforms of metallothioneins are separated from 30-100 microliter sample volumes by CE and the elements Cu, Zn, Cd, and S are detected by use of ICP-SFMS. The extraction of cytosols is the first step in the analytical procedure. Tissue samples from human brain are homogenized in a buffer solution and submitted to ultra-centrifugation. The supernatant is defatted and the cytosol pre-treatment is optimized for CE separation by matrix reduction. The buffer concentration and pH used for capillary electrophoretic separation of metallothionein from rabbit liver were optimized. CE with ICP-MS detection is compared to UV detection. In the electropherograms obtained from the cytosols three peaks can be assigned to MT-1, MT-2, and MT-3. As an additional method, size-exclusion chromatography (SEC) is applied. Fractions from an SEC separation of the cytosol are collected, concentrated, and then injected into the CE. The detection of sulfur by ICP-SFMS (medium resolution mode) and quantification by isotope dilution have also been investigated as a new method for the quantification of MT isoforms. The analytical procedure developed has been used for the first time in comparative studies of the distributions of MT-1, MT-2, and MT-3 in brain samples taken from patients with Alzheimer's disease and from a control group.

This paper summarizes current challenges, the potential use of novel scientific methodologies, and ways forward in the risk assessment and risk management of mixtures. Generally, methodologies to address mixtures have been agreed; however, there are still several data and methodological gaps to be addressed. New approach methodologies can support the filling of knowledge gaps on the toxicity and mode(s) of action of individual chemicals. (Bio)Monitoring, modeling, and better data sharing will support the derivation of more realistic co-exposure scenarios. As knowledge and data gaps often hamper an in-depth assessment of specific chemical mixtures, the option of taking account of possible mixture effects in single substance risk assessments is briefly discussed. To allow risk managers to take informed decisions, transparent documentation of assumptions and related uncertainties is recommended indicating the potential impact on the assessment. Considering the large number of possible combinations of chemicals in mixtures, prioritization is needed, so that actions first address mixtures of highest concern and chemicals that drive the mixture risk. As chemicals with different applications and regulated separately might lead to similar toxicological effects, it is important to consider chemical mixtures across legislative sectors. ARTICLE HISTORY

HighlightsA workflow for an exposure driven chemical safety assessment to avoid animal testing.Hypothesis based on existing data, in silico modelling and biokinetic considerations.A tool to inform targeted and toxicologically relevant in vitro testing.

Quantitative Structure -Skin permeability Relationships http://researchonline.ljmu.ac.uk/6719/ Article LJMU has developed LJMU Research Online for users to access the research output of the University more effectively. Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Users may download and/or print one copy of any article(s) in LJMU Research Online to facilitate their private study or for non-commercial research. You may not engage in further distribution of the material or use it for any profit-making activities or any commercial gain.The version presented here may differ from the published version or from the version of the record. Please see the repository URL above for details on accessing the published version and note that access may require a subscription. This paper reviews in silico models currently available for the prediction of skin permeability with the main focus on the quantitative structure-permeability relationship (QSPR) models. A comprehensive analysis of the main achievements in the field in the last decade is provided. In addition, the mechanistic models are discussed and comparative studies that analyse different models are discussed. (to be extended to 100--200 words)

Read-across as an alternative assessment method for chemical toxicity has growing interest in both the regulatory and industrial communities. The pivotal means of acquiring acceptance of a read-across prediction is identifying and assessing uncertainties associated with it. This study has identified and summarised in a structured way the variety of uncertainties that potentially impact acceptance of a readacross argument. The main sources of uncertainty were established and divided into four main categories: i) the regulatory use of the prediction, ii) the data for the apical endpoint being assessed, iii) the readacross argumentation, and iv) the similarity justification. Specifically, the context of, and relevance to, the regulatory use of a read-across will dictate the acceptable level of uncertainties. The apical endpoint (or other) data must be of sufficient quality and relevance for data gap filling. Read-Across argumentation uncertainties include: 1) mechanistic plausibility (i.e., the knowledge of the chemical and biological mechanisms leading to toxicity), 2) completeness of the supporting evidence, 3) robustness of the supporting data, and 4) Weight-of-Evidence. In addition, similarity arguments for chemistry, physicochemical properties, toxicokinetics and toxicodynamics are linked to these read-across argumentation issues. To further progress in this area, a series of questions are proposed with the goal of addressing each type of uncertainty. Highlights: Six read-across case studies were reviewed to establish overarching uncertainties. Twelve types of uncertainties identified for read-across for toxicity prediction. Questions were formulated to assist in assessing uncertainties in read-across. Comparison with existing schemes for read-across uncertainty is given.