Testing for toxicities is an important activity in drug development. In an ideal world the tests applied would be definitive. In reality this is seldom the case. There are two types of power associated with a test. A test's discriminatory power is characterized by its sensitivity and specificity and tells the investigator the probability of obtaining a test positive in the presence (sensitivity) or a test negative in the absence (specificity) of the toxicity. A test's discriminatory power is an attribute of the test itself. The investigator is, however, more interested in a test's predictive power, which is the probability that the toxicity is present or absent in a novel molecule given the test result. A test's predictive power is a consequence of the test's discriminatory power and the context of its application. Unlike its discriminatory power, the predictive power of a test is not ‘fixed’ and varies with testing context. This means that tests and test context must be taken together to enable an investigator to achieve their desired predictive power. Our intent is to illustrate a broadly applicable approach to testing schemes designed to maximize a test's positive or negative predictive power. Rather than hypothetical tests and toxicities, we use as examples tests available for the prediction of a substance's liability to cause the cardiac arrhythmia torsade de pointes.Owing to intense focus over the last two decades, the discriminatory powers of a number of tests for predicting a torsade de pointes liability are publicly available. Having randomly chosen an initial test (random although plausible as an early screening assessment), the inter-relationship between the prevalence of torsadogenic liability and the discriminatory power of potential follow-on tests were explored in a probability framework, based on Bayes Theorem, to show how testing schemes could be developed based on odds and likelihood ratios. Uncertainty around the prevalence of torsade liability and the discriminatory power of a test were addressed by varying these values and examining their impact on the test's predictive power.Overall, the analysis demonstrates that tests can be strategically combined to reach a desired level of predictive power. This is generally more easily achieved for negative predictive power given a low prevalence of the toxicity under scrutiny. For this work, we used a base prevalence of 10% for a substance to carry a tordsadogenic liability. Given uncertainty around a test's discriminatory power, a probabilistic rather than deterministic approach was recommended. Such an approach necessarily requires the investigator to define distributions around test characteristics as well as their desired probability of attaining a given predictive power.The proposed approach is easily implemented deterministically since values of the discriminatory power of the tests are readily and publicly available. The probabilistic implementation is also easily implemented, but requires that the uncertainty around the test performance and prevalence, and the targets for probability of attaining the desired predictive value all be made explicit rather than remain implicit as is often the case in ‘integrated risk assessment’ or ‘totality of evidence’ presentations. This general approach could form a basis for testing and decision-making that can be communicated and discussed in a consistent manner between scientists as well as between sponsors and regulators.

As part of a joint project between the Research Institute for Fragrance Materials (RIFM) and Creme Global, a Monte Carlo model (here named the Creme RIFM model) has been developed to estimate consumer exposure to ingredients in personal care products. Details of the model produced in Phase 1 of the project have already been published. Further data on habits and practises have been collected which enable the model to estimate consumer exposure from dermal, oral and inhalation routes for 25 product types.. In addition, more accurate concentration data have been obtained which allow levels of fragrance ingredients in these product types to be modelled. Described is the use of this expanded model to estimate aggregate systemic exposure for eight fragrance ingredients. Results are shown for simulated systemic exposure (expressed as mu g/kg bw/day) for each fragrance ingredient in each product type, along with simulated aggregate exposure. Highest fragrance exposure generally occurred from use of body lotions, body sprays and hydroalcoholic products. For the fragrances investigated, aggregate exposure calculated using this model was 11.5-25 fold lower than that calculated using deterministic methodology. The Creme RIFM model offers a very comprehensive and powerful tool for estimating aggregate exposure to fragrance ingredients. (C) 2017 Published by Elsevier Inc.

In order to accurately assess aggregate exposure to a fragrance material in consumers, data are needed on consumer habits and practices, as well as the concentration of the fragrance material in those products. The present study describes the development of Phase 2 Creme RIFM model by expanding the previously developed Phase 1 model to include an additional six product types. Using subject-matching algorithms, the subjects in the Phase 1 Creme RIFM database were paired with subjects in the SUPERB and BodyCare surveys based on age and gender. Consumption of the additional products was simulated to create a seven day diary allowing full data integration in a consistent format.The inhalation route was also included for air care and other products where a fraction of product used is inhaled, derived from the RIFM 2-box model. The expansion of the Phase 1 Creme RIFM model has resulted in a more extensive and refined model, which covers a broader range of product categories and now, includes all relevant routes of exposure. An evaluation of the performance of the model has been carried out in an accompanying publication to this one.

The study used a person-oriented approach to identify different profiles relating to the self- and co-regulation of learning as well as university students' processes of understanding. Altogether 33 participating university students were interviewed. The data were analysed using qualitative inductive and deductive content analysis. Three student profiles were identified: 1) self-regulated students not using co-regulation, 2) actively co-regulating students with average self-regulation skills, and 3) students with self-regulation problems relying on co-regulation. Self-regulated students not using co-regulation showed evidence of excellent self-regulation skills and deep-level processing, whereas actively co-regulating students with average self-regulation skills showed an active use of co-regulation of learning, some problems in self-regulation, and challenges in deep processing. Students with self-regulation problems relying on co-regulation emphasised other students' support when studying, and exhibited surface-level processing. The results imply that by recognising the profiles of university students it is possible to identify those who may be at risk and in need of more support in their studying.

Ensuring the toxicological safety of fragrance ingredients used in personal care and cosmetic products is essential in product development and design, as well as in the regulatory compliance of the products. This requires an accurate estimation of consumer exposure which, in turn, requires an understanding of consumer habits and use of products. Where ingredients are used in multiple product types, it is important to take account of aggregate exposure in consumers using these products. This publication investigates the use of a newly developed probabilistic model, the Creme RIFM model, to estimate aggregate exposure to fragrance ingredients using the example of 2-phenylethanol (PEA). The output shown demonstrates the utility of the model in determining systemic and dermal exposure to fragrances from individual products, and aggregate exposure. The model provides valuable information not only for risk assessment, but also for risk management. It should be noted that data on the concentrations of PEA in products used in this article were obtained from limited sources and not the standard, industry wide surveys typically employed by the fragrance industry and are thus presented here to illustrate the output and utility of the newly developed model. They should not be considered an accurate representation of actual exposure to PEA.

Vitamin A, a fat soluble compound is an essential nutrient for fish. The present study evaluated the effects of dietary vitamin A administered to juvenile silver catfish (Rhamdia quelen). Fish were fed semi-purified diets formulated to meet the protein and energy requirements of the species (37% CP and 3400 kcal kg− 1 DE). Five vitamin A levels were tested: 1090, 1180, 1750, 2610 or 2880 IU kg− 1 of diet. The growth, body composition, metabolic and blood parameters and oxidative stress biomarkers of juvenile silver catfish were studied after 70 days. Fish growth increased with an increase in vitamin A. The fish that were fed a 2610 IU kg− 1 vitamin A diet had better quality fillets, with more protein and less fat. No effects on hematological and biochemical parameters were noted. The high levels (1750, 2610 and 2880 IU kg− 1) of vitamin A improved the fish antioxidant defense system. Lower activity was observed for enzymes that act as antioxidants because retinoids and carotenoids acted as scavengers and donated electrons to reactive oxygen species, conferring enzymatic protection and contributing to the animal's energy economy. Based on the growth and fillet quality and oxidative results, we recommend a 2610 IU kg− 1 vitamin A diet for juvenile R. quelen.Important to assess the nutrition and health of the silver catfish.

Exposure of fragrance ingredients in cosmetics and personal care products to the population can be determined by way of a detailed and robust survey. The frequency and combinations of products used at specific times during the day will allow the estimation of aggregate exposure for an individual consumer, and to the sample population. In the present study, habits and practices of personal care and cosmetic products have been obtained from market research data for 36,446 subjects across European countries and the United States in order to determine the exposure to fragrance ingredients. Each subject logged their product uses, time of day and body application sites in an online diary for seven consecutive days. The survey data did not contain information on the amount of product used per occasion or body measurements, such as weight and skin surface area. Nevertheless, this was found from the literature where the likely amount of product used per occasion or body measurement could be probabilistically chosen from distributions of data based on subject demographics. The daily aggregate applied consumer product exposure was estimated based on each subject's frequency of product use, and Monte Carlo simulations of their likely product amount per use and body measurements. Statistical analyses of the habits and practices and consumer product exposure are presented, which show the robustness of the data and the ability to estimate aggregate consumer product exposure. Consequently, the data and modelling methods presented show potential as a means of performing ingredient safety assessments for personal care and cosmetics products.

Many cosmetic products are available in spray form. Even though the principal targets of these products are the skin and hair, spraying leads to the partitioning of the product between the target and the surrounding air. In the previous COLIPA study (Hall et al., 2007) the daily use of deodorant/antiperspirant (Deo/AP) in spray form was quantified in terms of the amount of product dispensed from the spray can, without specifically quantifying the product fraction reaching the skin during use. Results of the present study provide this additional information, necessary for a reliable safety assessment of sprayed Deo/AP products. In a novel experimental approach the information obtained from real-life movement analysis (automated motion imaging) of volunteers using their own products was integrated with the aerosol cloud sampling data obtained from the same products, leading to the computation of the product deposited on the skin. The 90th percentile values, expressed as percent deposition relative to the can weight loss after spraying, are 23.5% and 11.4% for ethanol-based and non-ethanol-based products, respectively. Additionally, the study has generated data on the skin area covered by the products, spray duration time, spray angle and spray distance from the skin.

Access to reliable exposure data is essential to evaluate the toxicological safety of ingredients in cosmetic products. This study was carried out by European cosmetic manufacturers acting within the trade association Colipa, with the aim to construct a probabilistic European population model of exposure. The study updates, in distribution form, the current exposure data on daily quantities of six cosmetic products. Data were collected using a combination of market information databases and a controlled product use study. In total 44,100 households and 18,057 individual consumers in five European countries provided data using their own products. All product use occasions were recorded, including those outside of home. The raw data were analysed using Monte Carlo simulation and a European Statistical Population Model of exposure was constructed. A significant finding was an inverse correlation between frequency of product use and quantity used per application for body lotion, facial moisturiser, toothpaste and shampoo. Thus it is not appropriate to calculate daily exposure to these products by multiplying the maximum frequency value by the maximum quantity per event value. The results largely confirm the exposure parameters currently used by the cosmetic industry. Design of this study could serve as a model for future assessments of population exposure to chemicals in products other than cosmetics.

In this study, we describe the statistical analysis of the usage profile of the European population to seven cosmetic products. The aim of the study was to construct a reliable model of exposure of the European population from use of the selected products: body lotion, shampoo, deodorant spray, deodorant non-spray, facial moisturiser, lipstick and toothpaste.The first step in this process was to gather reliable data on consumer usage patterns of the products. These data were sourced from a combination of market information databases and a controlled product use study by the trade association Colipa. The market information study contained a large number of subjects, in total 44,100 households and 18,057 habitual users (males and females) of the studied products, in five European countries.The data sets were then combined to generate a realistic distribution of frequency of use of each product, combined with distribution of the amount of product used at each occasion using the CREMe software. A Monte Carlo method was used to combine the data sets. This resulted in a new model of European exposure to cosmetic products being constructed.