Effect of oral calcium carbonate on aortic calcification in apolipoprotein E-deficient (apoE−/−) mice with chronic renal failure

Background. In chronic kidney disease (CKD) patients, the intake of calcium-based phosphate binders is associated with a marked progression of coronary artery and aortic calcification, in contrast to patients receiving calcium-free phosphate binders. The aim of this study was to reexamine the role of calcium carbonate in vascular calcification and to analyse its effect on aortic calcification-related gene expression in chronic renal failure (CRF). Methods. Mice deficient in apolipoprotein E underwent either sham operation or subtotal nephrectomy to create CRF. They were then randomly assigned to one of the three following groups: a control non-CRF group and a CRF group fed on standard diet, and a CRF group fed on calcium carbonate enriched diet, for a period of 8 weeks. Aortic atherosclerotic plaque and calcification were evaluated using quantitative morphologic image processing. Aortic gene and protein expression was examined using immunohistochemistry and Q-PCR methods. Results. Calcium carbonate supplementation was effective in decreasing serum phosphorus but was associated with a higher serum calcium concentration. Compared with standard diet, calcium carbonate enriched diet unexpectedly induced a significant decrease of both plaque (p < 0.05) and non-plaque-associated calcification surface (p < 0.05) in CRF mice. It also increased osteopontin (OPN) protein expression in atherosclerotic lesion areas of aortic root. There was also a numerical increase in OPN and osteoprotegerin gene expression in total thoracic aorta but the difference did not reach the level of significance. Finally, calcium carbonate did not change the severity of atherosclerotic lesions. Conclusion. In this experimental model of CRF, calcium carbonate supplementation did not accelerate but instead decreased vascular calcification. If our observation can be extrapolated to humans, it appears to question the contention that calcium carbonate supplementation, at least when given in moderate amounts, necessarily enhances vascular calcification. It is also compatible with the hypothesis of a preponderant role of phosphorus over that of calcium in promoting vascular calcification in CR

Search for the presence of occult hepatitis C in patients with treatment-induced viral clearance using an ultrasensitive assay

Introduction. Occult hepatitis C is defined by the presence of virus in the peripheral blood mononuclear cells (PBMCs) and/or liver cells, in the absence of serum viremia. Objective. To detect the persistence of occult hepatitis C in hemodialysis (HD) patients and patients without renal disease (non-renal) with treatment-induced clearance of hepatitis C virus (HCV) infection, using assays with a very low detection limit of viremia. Methods. A group of 13 HD patients and a group of 43 non-renal patients, with treatment-induced HCV infection clearance were investigated in the study. The HD patients were treated with pegylated interferon alpha (PEG-IFN-α) only, while the non-renal patients were treated with a combination therapy of PEGIFN- α and ribavirin. Detection of a possible persistence of HCV RNA in the PBMCs and plasma samples was assessed by an ultrasensitive reverse transcription polymerase chain reaction (RT-PCR) assay (2 IU/ml). Results. HCV RNA was not detected in the PBMCs and plasma samples of HD patients and of non-renal patients, when assessed by the ultrasensitive RT-PCR assay. Conclusion. When a sensitive RT-PCR assay was applied, to determine if treatment induced clearance of HCV infection had been successful, occult hepatitis C could not be detected by an ultrasensitive assay, neither in HD nor in non-renal patients

Association of the Treatment Induced Clearance of Hepatitis C Virus Infection with the IL28B Gene Polymorphisms

Background: It has been shown that single nucleotide polymorphisms (SNPs) near the interleukin 28B (IL28B) gene were associated with sustained viral response following standard treatment of hepatitis C virus infection.Aim: The aim of the study was to evaluate the association between the SNPs near the IL28B gene and the response to the treatment of chronic hepatitis C.Methods: The genotyping of the three IL28B gene polymorphisms: rs12979860, rs8099917, and&nbsp; rs12980275 was done in 100 Caucasian patients with chronic hepatitis C previously treated with standard antiviral therapy. The study group consisted of 28 hemodialysis patients with end stage renal disease treated with pegylated interferon α and 72 patients without renal disease treated with pegylated interferon α and ribavirin. All patients finished the antiviral treatment at least 6 months before enrollment in the study. Sustained viral response, defined as an absence of detectable HCV RNA in the serum, was tested by an assay with a sensitivity of 20 IU/mL.Results: Sustained viral response was achieved in 56% (56/100) of the treated patients. The three IL28B gene polymorphisms (CC genotype of rs12979860, TT genotype of rs8099917, and AA genotype of rs12980275) were associated with sustained viral response (p=0.006, p=0.002, p=0.007, respectively) in study patients with chronic hepatitis C treated with standard antiviral therapy.Conclusion: The IL28B gene polymorphisms: rs12979860, rs8099917, and rs12980275 were significantly associated with the successful treatment of chronic hepatitis C.</p

Genetic predictors of the response to the treatment of hepatitis C virus infection

The genome-wide association studies have identified a strong association between interleukin 28B (IL28B) gene polymorphisms and the response to treatment in patients with hepatitis C virus (HCV) infection. The aim of the study was to evaluate the association between three most widely studied IL28B gene polymorphisms and the response to antiviral treatment of chronic hepatitis C. We performed the genotyping of the three IL28B gene polymorphisms: rs12979860, rs8099917, and rs12980275 in 72 Caucasian patients with chronic hepatitis C, previously treated with the combination therapy of pegylated interferon alpha (PEGIFN α) and ribavirin (RBV). The patients included in the study had finished the treatment regimen at least 6 months before enrolling in the study. We used the sustained viral response (SVR) for the evaluation of the effectiveness of the antiviral treatment, and it was tested with an assay with a sensitivity of 20 IU/mL. An SVR was achieved in 59.7% (43/72) of the treated patients. The three IL28B gene polymorphisms (CC genotype of rs12979860, TT genotype of rs8099917, and AA genotype of rs12980275) were associated with the SVR (p = 0.029, p = 0.016, and p = 0.028, respectively) in the study patients with chronic hepatitis C treated with the combination therapy of PEGIFN α and RBV. The association of IL28B gene polymorphisms with the treatment response points to the possibility of personalized medicine for the treatment of HCV infection

CERAPP : Collaborative Estrogen Receptor Activity Prediction Project

BACKGROUND: Humans are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Most of these chemicals have never been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for evaluation in costly in vivo tests, for instance, within the U.S. EPA Endocrine Disruptor Screening Program. OBJECTIVES: We describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) and demonstrate the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing. METHODS: CERAPP combined multiple models developed in collaboration with 17 groups in the United States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure-activity relationship models and docking approaches were employed, mostly using a common training set of 1,677 chemical structures provided by the U.S. EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were evaluated on a set of 7,522 chemicals curated from the literature. To overcome the limitations of single models, a consensus was built by weighting models on scores based on their evaluated accuracies. RESULTS: Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. Out of the 32,464 chemicals, the consensus model predicted 4,001 chemicals (12.3%) as high priority actives and 6,742 potential actives (20.8%) to be considered for further testing.CONCLUSION: This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches. This concept will be applied in future projects related to other end points

CoMPARA : Collaborative Modeling Project for Androgen Receptor Activity

BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS: The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast (TM) metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast (TM)/Tox21 HTS in vitro assays. RESULTS: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of similar to 875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment