Internal Assessments for the publishing house Aufbau in the German Democratic Republic: A Glimpse into Elga Abramowitz’s Vorlass

Elga Abramowitz, editor and translator, worked mainly for the Aufbau publishing house in the German Democratic Republic (GDR) from 1952 to 1990. During her time there, she was responsible for a variety of tasks within the publishing house. Through the preservation of her Vorlass, we have the opportunity to explore her varied work. In particular, this article focuses on the assessments she wrote for Aufbau-Verlag – not the much-discussed assessments for the print permit process, but assessments written to determine the suitability of works for publication in Aufbau-Verlag. These documents were intended for internal use within the publishing house and can offer some insight into assessment and publishing processes in the German Democratic Republic’s publishing houses, particularly the Aufbau-Verlag. One of the most interesting finds is that often, the quality rather than the ideological content of the texts seemed to be a determining factor regarding which books were recommended for publication

The Hellenistic pottery from Sardis: the finds through 1994

https://commons.library.stonybrook.edu/amar/1390/thumbnail.jp

Genetic Variants in <i>CPA6</i> and <i>PRPF31</i> are Associated with Variation in Response to Metformin in Individuals with Type 2 Diabetes

Metformin is the first-line treatment for type 2 diabetes (T2D). Although widely prescribed, the glucose-lowering mechanism for metformin is incompletely understood. Here, we used a genome-wide association approach in a diverse group of individuals with T2D from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial to identify common and rare variants associated with HbA1c response to metformin treatment and followed up these findings in four replication cohorts. Common variants in PRPF31 and CPA6 were associated with worse and better metformin response, respectively (P &lt; 5 × 10-6), and meta-analysis in independent cohorts displayed similar associations with metformin response (P = 1.2 × 10-8 and P = 0.005, respectively). Previous studies have shown that PRPF31(+/-) knockout mice have increased total body fat (P = 1.78 × 10-6) and increased fasted circulating glucose (P = 5.73 × 10-6). Furthermore, rare variants in STAT3 associated with worse metformin response (q &lt;0.1). STAT3 is a ubiquitously expressed pleiotropic transcriptional activator that participates in the regulation of metabolism and feeding behavior. Here, we provide novel evidence for associations of common and rare variants in PRPF31, CPA6, and STAT3 with metformin response that may provide insight into mechanisms important for metformin efficacy in T2D

Endocrine disrupting potential of environmental chemicals characterized by high-throughput screening

Over the past 20 years, an increased focus on detecting environmental chemicals that pose a risk of endocrine disruption and congressional legislation have driven the creation of the U.S. EPA Endocrine Disruptor Screening Program (EDSP). Several thousand chemicals are subject to the EDSP, which will require millions of dollars and decades to process using current test batteries. In order to identify opportunities for increased chemical throughput, we initially investigated how well EPA ToxCast in vitro high-throughput screening (HTS) assays relevant for estrogen, androgen, steroidogenic, and thyroid disrupting mechanisms could identify compounds relative to in vitro and in vivo data collected from studies related to the EDSP Tier 1 screen. An iterative, balanced optimization model was implemented and indicated that ToxCast HTS assays measuring estrogen receptor (ER) and androgen receptor (AR) activation classify compounds with estrogenic and androgenic activity in guideline studies with a high degree of accuracy, respectively. The ER signaling pathway involves a wide array of molecular initiating events and cellular processes. This dissertation examined whether active chemicals in ToxCast ER transactivation assays could indicate chemical-induced upregulation of the ER pathway through ligand binding leading to induced changes in T47D growth kinetics. Considering the complex set of ER in vitro assays in toto increased the overall sensitivity of detection for ER reference chemicals. In addition, this research highlighted important aspects of the biological response such as non-ER specificity in the cell growth assay. These nuances are likely important considerations for the construction of a predictive model. In effort to accurately predict the estrogenic potential of environmental chemicals in a high-throughput format, multiple orthogonal in vitro ER assays were used to develop a predictive model for ~2000 chemicals. Model results indicate a high degree of predictivity for both the uterotrophic in vivo assay and ER reference chemicals. The information provided by the model will aid in understanding how environmental chemicals contribute to endocrine-related human health consequences and predict the estrogenic potential of chemicals through the use of in vitro assays, limiting the need to run more costly and animal-intensive in vivo bioassays.Doctor of Philosoph

Integrated Model of Chemical Perturbations of a Biological Pathway Using 18 In Vitro High Throughput Screening Assays for the Estrogen Receptor

We demonstrate a computational network model that integrates 18 in vitro, high-throughput screening assays measuring estrogen receptor (ER) binding, dimerization, chromatin binding, transcriptional activation and ER-dependent cell proliferation. The network model uses activity patterns across the in vitro assays to predict whether a chemical is an ER agonist or antagonist, or is otherwise influencing the assays through a manner dependent on the physics and chemistry of the technology platform (“assay interference”). The method is applied to a library of 1812 commercial and environmental chemicals, including 45 ER positive and negative reference chemicals. Among the reference chemicals, the network model correctly identified the agonists and antagonists with the exception of very weak compounds whose activity was outside the concentration range tested. The model agonist score also correlated with the expected potency class of the active reference chemicals. Of the 1812 chemicals evaluated, 111 (6.1%) were predicted to be strongly ER active in agonist or antagonist mode. This dataset and model were also used to begin a systematic investigation of assay interference. The most prominent cause of false-positive activity (activity in an assay that is likely not due to interaction of the chemical with ER) is cytotoxicity. The model provides the ability to prioritize a large set of important environmental chemicals with human exposure potential for additional in vivo endocrine testing. Finally, this model is generalizable to any molecular pathway for which there are multiple upstream and downstream assays available

From metabonomics to pharmacometabonomics: The role of metabolic profiling in personalized medicine

Variable patient responses to drugs are a key issue for medicine and for drug discovery and development. Personalised medicine, that is the selection of medicines for subgroups of patients so as to maximise drug efficacy and minimise toxicity, is a key goal of 21st century healthcare. Currently, most personalised medicine paradigms rely on clinical judgement based on the patient’s history, and on the analysis of the patients’ genome to predict drug effects i.e. pharmacogenomics. However, variability in patient responses to drugs is dependent upon many environmental factors to which human genomics is essentially blind. A new paradigm for predicting drug responses based on individual pre-dose metabolite profiles has emerged in the past decade: pharmacometabonomics, which is defined as ‘the prediction of the outcome (for example, efficacy or toxicity) of a drug or xenobiotic intervention in an individual based on a mathematical model of pre-intervention metabolite signatures’. The new pharmacometabonomics paradigm is complementary to pharmacogenomics but has the advantage of being sensitive to environmental as well as genomic factors. This review will chart the discovery and development of pharmacometabonomics, and provide examples of its current utility and possible future developments

Incorporating Human Dosimetry and Exposure into the Utilization of ToxCast In Vitro Screening Data for Chemical Prioritization

Humans are frequently exposed to chemicals that have undergone limited safety testing. To reduce the number of untested chemicals and prioritize limited testing resources, multiple governmental programs are using high throughput in vitro toxicity screens for assessing effects across multiple cellular pathways. In this study, metabolic clearance and plasma protein binding were experimentally measured for 39 of the 309 ToxCast Phase I chemicals. The experimental data was modeled using pharmacokinetics for estimating human oral equivalent doses that would produce steady state in vivo concentrations equivalent to ToxCast in vitro AC[50] values. The range of oral equivalent doses for the ToxCast assays was compared with human oral exposure estimates to assess whether in vitro bioactivity could occur at human exposure levels. Two of the 39 chemicals had overlapping oral equivalent doses and human exposures and would not have been identified using the rank potencies of the AC50 values. These results demonstrate the importance of incorporating dosimetry and exposure when using high throughput in vitro data to identify the highest priorities for further testing and risk management.Master of Science in Public Healt

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells

The steroid metabolome in women with premenstrual dysphoric disorder during GnRH agonist-induced ovarian suppression: effects of estradiol and progesterone addback

Clinical evidence suggests that symptoms in premenstrual dysphoric disorder (PMDD) reflect abnormal responsivity to ovarian steroids. This differential steroid sensitivity could be underpinned by abnormal processing of the steroid signal. We used a pharmacometabolomics approach in women with prospectively confirmed PMDD (n=15) and controls without menstrual cycle-related affective symptoms (n=15). All were medication-free with normal menstrual cycle lengths. Notably, women with PMDD were required to show hormone sensitivity in an ovarian suppression protocol. Ovarian suppression was induced for 6 months with gonadotropin-releasing hormone (GnRH)-agonist (Lupron); after 3 months all were randomized to 4 weeks of estradiol (E2) or progesterone (P4). After a 2-week washout, a crossover was performed. Liquid chromatography/tandem mass spectrometry measured 49 steroid metabolites in serum. Values were excluded if >40% were below the limit of detectability (n=21). Analyses were performed with Wilcoxon rank-sum tests using false-discovery rate (q<0.2) for multiple comparisons. PMDD and controls had similar basal levels of metabolites during Lupron and P4-derived neurosteroids during Lupron or E2/P4 conditions. Both groups had significant increases in several steroid metabolites compared with the Lupron alone condition after treatment with E2 (that is, estrone-SO4 (q=0.039 and q=0.002, respectively) and estradiol-3-SO4 (q=0.166 and q=0.001, respectively)) and after treatment with P4 (that is, allopregnanolone (q=0.001 for both PMDD and controls), pregnanediol (q=0.077 and q=0.030, respectively) and cortexone (q=0.118 and q=0.157, respectively). Only sulfated steroid metabolites showed significant diagnosis-related differences. During Lupron plus E2 treatment, women with PMDD had a significantly attenuated increase in E2-3-sulfate (q=0.035) compared with control women, and during Lupron plus P4 treatment a decrease in DHEA-sulfate (q=0.07) compared with an increase in controls. Significant effects of E2 addback compared with Lupron were observed in women with PMDD who had significant decreases in DHEA-sulfate (q=0.065) and pregnenolone sulfate (q=0.076), whereas controls had nonsignificant increases (however, these differences did not meet statistical significance for a between diagnosis effect). Alterations of sulfotransferase activity could contribute to the differential steroid sensitivity in PMDD. Importantly, no differences in the formation of P4-derived neurosteroids were observed in this otherwise highly selected sample of women studied under controlled hormone exposures