Foresight Review on Design for Safety

This review explores how a culture of design for safety can enhance the safety of the world around us. Design for safety goes beyond legislation, regulations and standards. These all play an important role for established products and services but their limited scope often leads to missed opportunities to enhance safety by taking a broader perspective. Design is applied to both mature industries (which have many years of experience and a good understanding of risks and how to reduce them) and emerging industries (that use new technologies requiring new ways of controlling risk which may not yet be known or understood). An example of an emerging risk is the internet that is enabling rapid innovation of new products which generate data. This data is widely shared across the internet and the risks associated with this are as yet not fully understood by the public. A design for safety culture takes a holistic approach to understanding the influences that affect safety. Such influences are varied and take into account the broader environment within which design operates, including complex interactions, behaviour and culture. It goes beyond traditional design methods and focuses on the goal of a safer design. Implementing design for safety requires an understanding of the challenges and the methods to address them. It needs multidisciplinary teams that bring together people with the relevant skills to understand the challenges and a collaborative approach of ‘designing with’ rather than the more traditional approach of ‘designing for’. This can be achieved through an international diverse community that works together to identify and share best practices

Role of the progesterone receptor for paclitaxel resistance in primary breast cancer

Paclitaxel plays an important role in the treatment of primary breast cancer. However, a substantial proportion of patients treated with paclitaxel does not appear to derive any benefit from this therapy. We performed a prospective study using tumour cells isolated from 50 primary breast carcinomas. Sensitivity of primary tumour cells to paclitaxel was determined in a clinically relevant range of concentrations (0.85–27.2 μg ml−1 paclitaxel) using an ATP assay. Chemosensitivity data were used to study a possible association with immunohistochemically determined oestrogen and progesterone receptor (ER and PR) status, as well as histopathological parameters. Progesterone receptor (PR) mRNA expression was also determined by quantitative RT–PCR. We observed a clear association of the PR status with chemosensitivity to paclitaxel. Higher levels of immunohistochemically detected PR expression correlated with decreased chemosensitivity (P=0.008). Similarly, high levels of PR mRNA expression were associated with decreased paclitaxel chemosensitivity (P=0.007). Cells from carcinomas with T-stages 3 and 4 were less sensitive compared to stages 1 and 2 (P=0.013). Multiple regression analysis identified PR receptor status and T-stage as independent predictors of paclitaxel chemosensitivity, whereas the ER, N-stage, grading and age were not influential. In conclusion, in vitro sensitivity to paclitaxel was higher for PR-negative compared with PR-positive breast carcinoma cells. Thus, PR status should be considered as a possible factor of influence when designing new trials and chemotherapy protocols

Identification of genomic biomarkers for anthracycline-induced cardiotoxicity in human iPSC-derived cardiomyocytes: an in vitro repeated exposure toxicity approach for safety assessment.

The currently available techniques for the safety evaluation of candidate drugs are usually cost-intensive and time-consuming and are often insufficient to predict human relevant cardiotoxicity. The purpose of this study was to develop an in vitro repeated exposure toxicity methodology allowing the identification of predictive genomics biomarkers of functional relevance for drug-induced cardiotoxicity in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). The hiPSC-CMs were incubated with 156 nM doxorubicin, which is a well-characterized cardiotoxicant, for 2 or 6 days followed by washout of the test compound and further incubation in compound-free culture medium until day 14 after the onset of exposure. An xCELLigence Real-Time Cell Analyser was used to monitor doxorubicin-induced cytotoxicity while also monitoring functional alterations of cardiomyocytes by counting of the beating frequency of cardiomyocytes. Unlike single exposure, repeated doxorubicin exposure resulted in long-term arrhythmic beating in hiPSC-CMs accompanied by significant cytotoxicity. Global gene expression changes were studied using microarrays and bioinformatics tools. Analysis of the transcriptomic data revealed early expression signatures of genes involved in formation of sarcomeric structures, regulation of ion homeostasis and induction of apoptosis. Eighty-four significantly deregulated genes related to cardiac functions, stress and apoptosis were validated using real-time PCR. The expression of the 84 genes was further studied by real-time PCR in hiPSC-CMs incubated with daunorubicin and mitoxantrone, further anthracycline family members that are also known to induce cardiotoxicity. A panel of 35 genes was deregulated by all three anthracycline family members and can therefore be expected to predict the cardiotoxicity of compounds acting by similar mechanisms as doxorubicin, daunorubicin or mitoxantrone. The identified gene panel can be applied in the safety assessment of novel drug candidates as well as available therapeutics to identify compounds that may cause cardiotoxicity

Comparison of scores for bimodality of gene expression distributions and genome-wide evaluation of the prognostic relevance of high-scoring genes

<p>Abstract</p> <p>Background</p> <p>A major goal of the analysis of high-dimensional RNA expression data from tumor tissue is to identify prognostic signatures for discriminating patient subgroups. For this purpose genome-wide identification of bimodally expressed genes from gene array data is relevant because distinguishability of high and low expression groups is easier compared to genes with unimodal expression distributions.</p> <p>Recently, several methods for the identification of genes with bimodal distributions have been introduced. A straightforward approach is to cluster the expression values and score the distance between the two distributions. Other scores directly measure properties of the distribution. The kurtosis, e.g., measures divergence from a normal distribution. An alternative is the outlier-sum statistic that identifies genes with extremely high or low expression values in a subset of the samples.</p> <p>Results</p> <p>We compare and discuss scores for bimodality for expression data. For the genome-wide identification of bimodal genes we apply all scores to expression data from 194 patients with node-negative breast cancer. Further, we present the first comprehensive genome-wide evaluation of the prognostic relevance of bimodal genes. We first rank genes according to bimodality scores and define two patient subgroups based on expression values. Then we assess the prognostic significance of the top ranking bimodal genes by comparing the survival functions of the two patient subgroups. We also evaluate the global association between the bimodal shape of expression distributions and survival times with an enrichment type analysis.</p> <p>Various cluster-based methods lead to a significant overrepresentation of prognostic genes. A striking result is obtained with the outlier-sum statistic (<it>p </it>< 10^-12). Many genes with heavy tails generate subgroups of patients with different prognosis.</p> <p>Conclusions</p> <p>Genes with high bimodality scores are promising candidates for defining prognostic patient subgroups from expression data. We discuss advantages and disadvantages of the different scores for prognostic purposes. The outlier-sum statistic may be particularly valuable for the identification of genes to be included in prognostic signatures. Among the genes identified as bimodal in the breast cancer data set several have not yet previously been recognized to be prognostic and bimodally expressed in breast cancer.</p

G × E interactions as a basis for toxicological uncertainty

To transfer toxicological findings from model systems, e.g. animals, to humans, standardized safety factors are applied to account for intra-species and inter-species variabilities. An alternative approach would be to measure and model the actual compound-specific uncertainties. This biological concept assumes that all observed toxicities depend not only on the exposure situation (environment = E), but also on the genetic (G) background of the model (G × E). As a quantitative discipline, toxicology needs to move beyond merely qualitative G × E concepts. Research programs are required that determine the major biological variabilities affecting toxicity and categorize their relative weights and contributions. In a complementary approach, detailed case studies need to explore the role of genetic backgrounds in the adverse effects of defined chemicals. In addition, current understanding of the selection and propagation of adverse outcome pathways (AOP) in different biological environments is very limited. To improve understanding, a particular focus is required on modulatory and counter-regulatory steps. For quantitative approaches to address uncertainties, the concept of “genetic” influence needs a more precise definition. What is usually meant by this term in the context of G × E are the protein functions encoded by the genes. Besides the gene sequence, the regulation of the gene expression and function should also be accounted for. The widened concept of past and present “gene expression” influences is summarized here as Ge. Also, the concept of “environment” needs some re-consideration in situations where exposure timing (Et) is pivotal: prolonged or repeated exposure to the insult (chemical, physical, life style) affects Ge This implies that it changes the model system. The interaction of Ge with Et might be denoted as Ge × Et We provide here general explanations and specific examples for this concept and show how it could be applied in the context of New Approach Methodologies (NAM)

Comparing United States and Canadian population exposures from National Biomonitoring Surveys: Bisphenol A intake as a case study

The Centers for Disease Control and Prevention provides biomonitoring data in the United States as part of the National Health and Nutrition Examination Survey (NHANES). Recently, Statistics Canada initiated a similar survey — the Canadian Health Measures Survey (CHMS). Comparison of US and Canadian biomonitoring data can generate hypotheses regarding human exposures from environmental media and consumer products. To ensure that such comparisons are scientifically meaningful, it is essential to first evaluate aspects of the surveys' methods that can impact comparability of data. We examined CHMS and NHANES methodologies, using bisphenol A (BPA) as a case study, to evaluate whether survey differences exist that would hinder our ability to compare chemical concentrations between countries. We explored methods associated with participant selection, urine sampling, and analytical methods. BPA intakes were also estimated to address body weight differences between countries. Differences in survey methods were identified but are unlikely to have substantial impacts on inter-survey comparisons of BPA intakes. BPA intakes for both countries are below health-based guidance values set by the US, Canada and the European Food Safety Authority. We recommend that before comparing biomonitoring data between surveys, a thorough review of methodologic aspects that might impact biomonitoring results be conducted

Exposure to bisphenol A enhanced lung eosinophilia in adult male mice

Background: Bisphenol A (BPA) is useful in many manufacturing processes and is also found in commonly used consumer products. Previous experimental studies have reported that perinatal exposure to BPA promotes the development of allergic lung inflammation in childhood and even into adulthood. In this study, the effects of BPA on allergic lung inflammation in adults were investigated in murine lungs. Methods: CD-1 mice were orally administrated with 1 mg of BPA/mouse four times at one-week intervals with or without ovalbumin (OVA). The pathologic changes in the airways, cytological alterations in bronchoalveolar lavage fluid (BALF), levels of inflammatory cytokines/chemokines in BALF, and OVA-specific IgE and IgG1 antibodies in serum were measured in the treated CD-1 mice. In vitro study using RAW264.7 cells, which are macrophage-like cells derived from BALB/c male mice, was conducted. The gene expression of cytokines and chemokines were measured. Results: BPA enhanced eosinophil recruitment induced by OVA in the alveoli and in the submucosa of the airway, which has a goblet cell proliferation in the bronchial epithelium. BPA increased Th2 cytokines-interleukin-13 (IL-13), eosinophil-relevant cytokines and chemokines, such as IL-5, and CCL2 induced by OVA, in BALF. BPA induced adjuvant effects on OVA-specific IgG1 production. In the in vitro study using RAW264.7 cells, BPA increased the mRNA expression of IL-1β, IL-6, CCL2 and CCL3 compared with the control and OVA groups. Conclusions: These results suggest that (1) the exposure of BPA could synergize with an OVA challenge to aggravate the severity of lung eosinophilia in adult mice, possibly by promoting a Th2-biased immune response and (2) the activation of macrophages and inflammatory cytokines released from these cells by BPA could be participating in this phenomenon

Simulating Microdosimetry in a Virtual Hepatic Lobule

The liver plays a key role in removing harmful chemicals from the body and is therefore often the first tissue to suffer potentially adverse consequences. To protect public health it is necessary to quantitatively estimate the risk of long-term low dose exposure to environmental pollutants. Animal testing is the primary tool for extrapolating human risk but it is fraught with uncertainty, necessitating novel alternative approaches. Our goal is to integrate in vitro liver experiments with agent-based cellular models to simulate a spatially extended hepatic lobule. Here we describe a graphical model of the sinusoidal network that efficiently simulates portal to centrilobular mass transfer in the hepatic lobule. We analyzed the effects of vascular topology and metabolism on the cell-level distribution following oral exposure to chemicals. The spatial distribution of metabolically inactive chemicals was similar across different vascular networks and a baseline well-mixed compartment. When chemicals were rapidly metabolized, concentration heterogeneity of the parent compound increased across the vascular network. As a result, our spatially extended lobule generated greater variability in dose-dependent cellular responses, in this case apoptosis, than were observed in the classical well-mixed liver or in a parallel tubes model. The mass-balanced graphical approach to modeling the hepatic lobule is computationally efficient for simulating long-term exposure, modular for incorporating complex cellular interactions, and flexible for dealing with evolving tissues