The structure of pollucite

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Geology and Geophysics, 1967.Bibliography: leaves 52-54.by Richard Myron Beger.M.S

Metabonomics evaluations of age-related changes in the urinary compositions of male Sprague Dawley rats and effects of data normalization methods on statistical and quantitative analysis

<p>Abstract</p> <p>Background</p> <p>Urine from male Sprague-Dawley rats 25, 40, and 80 days old was analyzed by NMR and UPLC/MS. The effects of data normalization procedures on principal component analysis (PCA) and quantitative analysis of NMR-based metabonomics data were investigated. Additionally, the effects of age on the metabolic profiles were examined by both NMR and UPLC/MS analyses.</p> <p>Results</p> <p>The data normalization factor was shown to have a great impact on the statistical and quantitative results indicating the need to carefully consider how to best normalize the data within a particular study and when comparing different studies. PCA applied to the data obtained from both NMR and UPLC/MS platforms reveals similar age-related differences. NMR indicated many metabolites associated with the Krebs cycle decrease while citrate and 2-oxoglutarate, also associated with the Krebs cycle, increase in older rats.</p> <p>Conclusion</p> <p>This study compared four different normalization methods for the NMR-based metabonomics spectra from an age-related study. It was shown that each method of normalization has a great effect on both the statistical and quantitative analyses. Each normalization method resulted in altered relative positions of significant PCA loadings for each sample spectra but it did not alter which chemical shifts had the highest loadings. The greater the normalization factor was related to age, the greater the separation between age groups was observed in subsequent PCA analyses. The normalization factor that showed the least age dependence was total NMR intensity, which was consistent with UPLC/MS data. Normalization by total intensity attempts to make corrections due to dietary and water intake of the individual animal, which is especially useful in metabonomics evaluations of urine. Additionally, metabonomics evaluations of age-related effects showed decreased concentrations of many Krebs cycle intermediates along with increased levels of oxidized antioxidants in urine of older rats, which is consistent with current theories on aging and its association with diminishing mitochondrial function and increasing levels of reactive oxygen species. Analysis of urine by both NMR and UPLC/MS provides a comprehensive and complementary means of examining metabolic events in aging rats.</p

Modeling Chemical Interaction Profiles: II. Molecular Docking, Spectral Data-Activity Relationship, and Structure-Activity Relationship Models for Potent and Weak Inhibitors of Cytochrome P450 CYP3A4 Isozyme

Polypharmacy increasingly has become a topic of public health concern, particularly as the U.S. population ages. Drug labels often contain insufficient information to enable the clinician to safely use multiple drugs. Because many of the drugs are bio-transformed by cytochrome P450 (CYP) enzymes, inhibition of CYP activity has long been associated with potentially adverse health effects. In an attempt to reduce the uncertainty pertaining to CYP-mediated drug-drug/chemical interactions, an interagency collaborative group developed a consensus approach to prioritizing information concerning CYP inhibition. The consensus involved computational molecular docking, spectral data-activity relationship (SDAR), and structure-activity relationship (SAR) models that addressed the clinical potency of CYP inhibition. The models were built upon chemicals that were categorized as either potent or weak inhibitors of the CYP3A4 isozyme. The categorization was carried out using information from clinical trials because currently available in vitro high-throughput screening data were not fully representative of the in vivo potency of inhibition. During categorization it was found that compounds, which break the Lipinski rule of five by molecular weight, were about twice more likely to be inhibitors of CYP3A4 compared to those, which obey the rule. Similarly, among inhibitors that break the rule, potent inhibitors were 2–3 times more frequent. The molecular docking classification relied on logistic regression, by which the docking scores from different docking algorithms, CYP3A4 three-dimensional structures, and binding sites on them were combined in a unified probabilistic model. The SDAR models employed a multiple linear regression approach applied to binned 1D 13C-NMR and 1D 15N-NMR spectral descriptors. Structure-based and physical-chemical descriptors were used as the basis for developing SAR models by the decision forest method. Thirty-three potent inhibitors and 88 weak inhibitors of CYP3A4 were used to train the models. Using these models, a synthetic majority rules consensus classifier was implemented, while the confidence of estimation was assigned following the percent agreement strategy. The classifier was applied to a testing set of 120 inhibitors not included in the development of the models. Five compounds of the test set, including known strong inhibitors dalfopristin and tioconazole, were classified as probable potent inhibitors of CYP3A4. Other known strong inhibitors, such as lopinavir, oltipraz, quercetin, raloxifene, and troglitazone, were among 18 compounds classified as plausible potent inhibitors of CYP3A4. The consensus estimation of inhibition potency is expected to aid in the nomination of pharmaceuticals, dietary supplements, environmental pollutants, and occupational and other chemicals for in-depth evaluation of the CYP3A4 inhibitory activity. It may serve also as an estimate of chemical interactions via CYP3A4 metabolic pharmacokinetic pathways occurring through polypharmacy and nutritional and environmental exposures to chemical mixtures

Quality assurance and quality control processes:summary of a metabolomics community questionnaire

Introduction The Metabolomics Society Data Quality Task Group (DQTG) developed a questionnaire regarding quality assurance (QA) and quality control (QC) to provide baseline information about current QA and QC practices applied in the international metabolomics community. Objectives The DQTG has a long-term goal of promoting robust QA and QC in the metabolomics community through increased awareness via communication, outreach and education, and through the promotion of best working practices. An assessment of current QA and QC practices will serve as a foundation for future activities and development of appropriate guidelines. Method QA was defined as the set of procedures that are performed in advance of analysis of samples and that are used to improve data quality. QC was defined as the set of activities that a laboratory does during or immediately after analysis that are applied to demonstrate the quality of project data. A questionnaire was developed that included 70 questions covering demographic information, QA approaches and QC approaches and allowed all respondents to answer a subset or all of the questions. Result The DQTG questionnaire received 97 individual responses from 84 institutions in all fields of metabolomics covering NMR, LC-MS, GC-MS, and other analytical technologies. Conclusion There was a vast range of responses concerning the use of QA and QC approaches that indicated the limited availability of suitable training, lack of Standard Operating Procedures (SOPs) to review and make decisions on quality, and limited use of standard reference materials (SRMs) as QC materials. The DQTG QA/QC questionnaire has for the first time demonstrated that QA and QC usage is not uniform across metabolomics laboratories. Here we present recommendations on how to address the issues concerning QA and QC measurements and reporting in metabolomics

Role of CD44 in clear cell renal cell carcinoma invasiveness after antiangiogenic treatment

Treballs Finals de Grau de Farmàcia, Facultat de Farmàcia, Universitat de Barcelona, 2017. Tutor/a: Joan Carles Rodríguez Rubio.[eng] During last century, big effort to understand the biochemical basis of cancer was carried out. One of the principal branches of these cancer investigations used drugs to prevent the formation of new blood vessels –process called angiogenesis– responsible for the nutrients supply of the tumour. These drugs are generally called antiangiogenics. It was discovered that some types of tumour have or develop resistance to these drugs when treatment was long enough. For that reason, mechanisms of resistance, aggressiveness, invasion and/or metastasis after the treatment are nowadays relevant to study. Recently, a protein that could be involved in the increased invasiveness of tumour cells after the antiangiogenic treatment appeared. This project collects some evidence that indicates that this protein, called CD44, might play a role in the increased invasion after antiangiogenic treatment in mouse models of renal carcinoma.[cat] Durant l’últim segle, s’ha fet un gran esforç per aprofundir en la basant bioquímica de la investigació contra el càncer. Una de les branques principals d’aquesta investigació utilitza fàrmacs que prevenen la formació de nous vasos sanguinis –procés anomenat angiogènesis- encarregats de nodrir el tumor. Aquests fàrmacs es diuen generalment antiangiogènics. S’ha descobert que alguns tipus de tumor tenen o desenvolupen resistència a aquests fàrmacs quan el tractament és prou llarg. Per aquesta raó, actualment s’està investigant profundament quins són els mecanismes pels quals apareix aquesta resistència, així com també perquè els tumors es tornen més agressius, invasius i/o metastàtics després del tractament. Recentment s’ha descobert una proteïna que podria estar involucrada en l’augment de la invasivitat de les cèl·lules tumorals després del tractament antiangiogènic. Aquest treball recull algunes de les evidències que apunten cap al paper de la proteïna CD44 en l’increment de la invasió tumoral post-tractament amb fàrmacs antiangiogènics en models ratolins de càncer renal

Metabolomics enables precision medicine: “A White Paper, Community Perspective”

Introduction: Background to metabolomics: Metabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or “-omics” level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person’s metabolic state provides a close representation of that individual’s overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry analytes to assess health and disease states. Examples include measuring glucose to monitor diabetes, measuring cholesterol and high density lipoprotein/low density lipoprotein ratio to assess cardiovascular health, BUN and creatinine for renal disorders, and measuring a panel of metabolites to diagnose potential inborn errors of metabolism in neonates. Objectives of White Paper—expected treatment outcomes and metabolomics enabling tool for precision medicine: We anticipate that the narrow range of chemical analyses in current use by the medical community today will be replaced in the future by analyses that reveal a far more comprehensive metabolic signature. This signature is expected to describe global biochemical aberrations that reflect patterns of variance in states of wellness, more accurately describe specific diseases and their progression, and greatly aid in differential diagnosis. Such future metabolic signatures will: (1) provide predictive, prognostic, diagnostic, and surrogate markers of diverse disease states; (2) inform on underlying molecular mechanisms of diseases; (3) allow for sub-classification of diseases, and stratification of patients based on metabolic pathways impacted; (4) reveal biomarkers for drug response phenotypes, providing an effective means to predict variation in a subject’s response to treatment (pharmacometabolomics); (5) define a metabotype for each specific genotype, offering a functional read-out for genetic variants: (6) provide a means to monitor response and recurrence of diseases, such as cancers: (7) describe the molecular landscape in human performance applications and extreme environments. Importantly, sophisticated metabolomic analytical platforms and informatics tools have recently been developed that make it possible to measure thousands of metabolites in blood, other body fluids, and tissues. Such tools also enable more robust analysis of response to treatment. New insights have been gained about mechanisms of diseases, including neuropsychiatric disorders, cardiovascular disease, cancers, diabetes and a range of pathologies. A series of ground breaking studies supported by National Institute of Health (NIH) through the Pharmacometabolomics Research Network and its partnership with the Pharmacogenomics Research Network illustrate how a patient’s metabotype at baseline, prior to treatment, during treatment, and post-treatment, can inform about treatment outcomes and variations in responsiveness to drugs (e.g., statins, antidepressants, antihypertensives and antiplatelet therapies). These studies along with several others also exemplify how metabolomics data can complement and inform genetic data in defining ethnic, sex, and gender basis for variation in responses to treatment, which illustrates how pharmacometabolomics and pharmacogenomics are complementary and powerful tools for precision medicine. Conclusions: Key scientific concepts and recommendations for precision medicine: Our metabolomics community believes that inclusion of metabolomics data in precision medicine initiatives is timely and will provide an extremely valuable layer of data that compliments and informs other data obtained by these important initiatives. Our Metabolomics Society, through its “Precision Medicine and Pharmacometabolomics Task Group”, with input from our metabolomics community at large, has developed this White Paper where we discuss the value and approaches for including metabolomics data in large precision medicine initiatives. This White Paper offers recommendations for the selection of state of-the-art metabolomics platforms and approaches that offer the widest biochemical coverage, considers critical sample collection and preservation, as well as standardization of measurements, among other important topics. We anticipate that our metabolomics community will have representation in large precision medicine initiatives to provide input with regard to sample acquisition/preservation, selection of optimal omics technologies, and key issues regarding data collection, interpretation, and dissemination. We strongly recommend the collection and biobanking of samples for precision medicine initiatives that will take into consideration needs for large-scale metabolic phenotyping studie

Quality assurance and quality control reporting in untargeted metabolic phenotyping: mQACC recommendations for analytical quality management

Background Demonstrating that the data produced in metabolic phenotyping investigations (metabolomics/metabonomics) is of good quality is increasingly seen as a key factor in gaining acceptance for the results of such studies. The use of established quality control (QC) protocols, including appropriate QC samples, is an important and evolving aspect of this process. However, inadequate or incorrect reporting of the QA/QC procedures followed in the study may lead to misinterpretation or overemphasis of the findings and prevent future metanalysis of the body of work. Objective The aim of this guidance is to provide researchers with a framework that encourages them to describe quality assessment and quality control procedures and outcomes in mass spectrometry and nuclear magnetic resonance spectroscopy-based methods in untargeted metabolomics, with a focus on reporting on QC samples in sufficient detail for them to be understood, trusted and replicated. There is no intent to be proscriptive with regard to analytical best practices; rather, guidance for reporting QA/QC procedures is suggested. A template that can be completed as studies progress to ensure that relevant data is collected, and further documents, are provided as on-line resources. Key reporting practices Multiple topics should be considered when reporting QA/QC protocols and outcomes for metabolic phenotyping data. Coverage should include the role(s), sources, types, preparation and uses of the QC materials and samples generally employed in the generation of metabolomic data. Details such as sample matrices and sample preparation, the use of test mixtures and system suitability tests, blanks and technique-specific factors are considered and methods for reporting are discussed, including the importance of reporting the acceptance criteria for the QCs. To this end, the reporting of the QC samples and results are considered at two levels of detail: “minimal” and “best reporting practice” levels

The time is now: Achieving FH paediatric screening across Europe – The Prague Declaration

ReviewFamilial Hypercholesterolaemia (FH) is severely under-recognized, under-diagnosed and under-treated in Europe, leading to a significantly higher risk of premature cardiovascular diseases in those affected. FH stands for inherited, very high cholesterol and affects 1:300 individuals regardless of their age, race, sex, and lifestyle, making it the most common inherited metabolic disorder and a non-modifiable cardiovascular disease risk factor in the world..info:eu-repo/semantics/publishedVersio

Single valproic acid treatment inhibits glycogen and RNA ribose turnover while disrupting glucose-derived cholesterol synthesis in liver as revealed by the [U-13C6]-d-glucose tracer in mice

Previous genetic and proteomic studies identified altered activity of various enzymes such as those of fatty acid metabolism and glycogen synthesis after a single toxic dose of valproic acid (VPA) in rats. In this study, we demonstrate the effect of VPA on metabolite synthesis flux rates and the possible use of abnormal 13C labeled glucose-derived metabolites in plasma or urine as early markers of toxicity. Female CD-1 mice were injected subcutaneously with saline or 600 mg/kg) VPA. Twelve hours later, the mice were injected with an intraperitoneal load of 1 g/kg [U-13C]-d-glucose. 13C isotopomers of glycogen glucose and RNA ribose in liver, kidney and brain tissue, as well as glucose disposal via cholesterol and glucose in the plasma and urine were determined. The levels of all of the positional 13C isotopomers of glucose were similar in plasma, suggesting that a single VPA dose does not disturb glucose absorption, uptake or hepatic glucose metabolism. Three-hour urine samples showed an increase in the injected tracer indicating a decreased glucose re-absorption via kidney tubules. 13C labeled glucose deposited as liver glycogen or as ribose of RNA were decreased by VPA treatment; incorporation of 13C via acetyl-CoA into plasma cholesterol was significantly lower at 60 min. The severe decreases in glucose-derived carbon flux into plasma and kidney-bound cholesterol, liver glycogen and RNA ribose synthesis, as well as decreased glucose re-absorption and an increased disposal via urine all serve as early flux markers of VPA-induced adverse metabolic effects in the host

The effect of black cohosh extract and risedronate coadministration on bone health in an ovariectomized rat model

Preparations of black cohosh extract are sold as dietary supplements marketed to relieve the vasomotor symptoms of menopause, and some studies suggest it may protect against postmenopausal bone loss. Postmenopausal women are also frequently prescribed bisphosphonates, such as risedronate, to prevent osteoporotic bone loss. However, the pharmacodynamic interactions between these compounds when taken together is not known. To investigate possible interactions, 6-month-old, female Sprague-Dawley rats underwent bilateral ovariectomy or sham surgery and were treated for 24 weeks with either vehicle, ethinyl estradiol, risedronate, black cohosh extract or coadministration of risedronate and black cohosh extract, at low or high doses. Bone mineral density (BMD) of the femur, tibia, and lumbar vertebrae was then measured by dual-energy X-ray absorptiometry (DEXA) at weeks 0, 8, 16, and 24. A high dose of risedronate significantly increased BMD of the femur and vertebrae, while black cohosh extract had no significant effect on BMD individually and minimal effects upon coadministration with risedronate. Under these experimental conditions, black cohosh extract alone had no effect on BMD, nor did it negatively impact the BMD-enhancing properties of risedronate