Properties of small molecular drug loading and diffusion in a fluorinated PEG hydrogel studied by ^1H molecular diffusion NMR and ^(19)F spin diffusion NMR

R_f-PEG (fluoroalkyl double-ended poly(ethylene glycol)) hydrogel is potentially useful as a drug delivery depot due to its advanced properties of sol–gel two-phase coexistence and low surface erosion. In this study, ^1H molecular diffusion nuclear magnetic resonance (NMR) and ^(19)F spin diffusion NMR were used to probe the drug loading and diffusion properties of the R_f-PEG hydrogel for small anticancer drugs, 5-fluorouracil (FU) and its hydrophobic analog, 1,3-dimethyl-5-fluorouracil (DMFU). It was found that FU has a larger apparent diffusion coefficient than that of DMFU, and the diffusion of the latter was more hindered. The result of ^(19)F spin diffusion NMR for the corresponding freeze-dried samples indicates that a larger portion of DMFU resided in the R_f core/IPDU intermediate-layer region (where IPDU refers to isophorone diurethane, as a linker to interconnect the R_f group and the PEG chain) than that of FU while the opposite is true in the PEG–water phase. To understand the experimental data, a diffusion model was proposed to include: (1) hindered diffusion of the drug molecules in the R_f core/IPDU-intermediate-layer region; (2) relatively free diffusion of the drug molecules in the PEG-water phase (or region); and (3) diffusive exchange of the probe molecules between the above two regions. This study also shows that molecular diffusion NMR combined with spin diffusion NMR is useful in studying the drug loading and diffusion properties in hydrogels for the purpose of drug delivery applications

MetaFIND: A feature analysis tool for metabolomics data

<p>Abstract</p> <p>Background</p> <p>Metabolomics, or metabonomics, refers to the quantitative analysis of all metabolites present within a biological sample and is generally carried out using NMR spectroscopy or Mass Spectrometry. Such analysis produces a set of peaks, or <it>features</it>, indicative of the metabolic composition of the sample and may be used as a basis for sample classification. Feature selection may be employed to improve classification accuracy or aid model explanation by establishing a subset of class discriminating features. Factors such as experimental noise, choice of technique and threshold selection may adversely affect the set of selected features retrieved. Furthermore, the high dimensionality and multi-collinearity inherent within metabolomics data may exacerbate discrepancies between the set of features retrieved and those required to provide a complete explanation of metabolite signatures. Given these issues, the latter in particular, we present the MetaFIND application for 'post-feature selection' correlation analysis of metabolomics data.</p> <p>Results</p> <p>In our evaluation we show how MetaFIND may be used to elucidate metabolite signatures from the set of features selected by diverse techniques over two metabolomics datasets. Importantly, we also show how MetaFIND may augment standard feature selection and aid the discovery of additional significant features, including those which represent novel class discriminating metabolites. MetaFIND also supports the discovery of higher level metabolite correlations.</p> <p>Conclusion</p> <p>Standard feature selection techniques may fail to capture the full set of relevant features in the case of high dimensional, multi-collinear metabolomics data. We show that the MetaFIND 'post-feature selection' analysis tool may aid metabolite signature elucidation, feature discovery and inference of metabolic correlations.</p

From differentiating metabolites to biomarkers

The current developments in metabolomics and metabolic profiling technologies have led to the discovery of several new metabolic biomarkers. Finding metabolites present in significantly different levels between sample sets, however, does not necessarily make these metabolites useful biomarkers. The route to valid and applicable biomarkers (biomarker qualification) is long and demands a significant amount of work. In this overview, we critically discuss the current state-of-the-art of metabolic biomarker discovery, with highlights and shortcomings, and suggest a pathway to clinical usefulness

Interpretation and Visualization of Non-Linear Data Fusion in Kernel Space: Study on Metabolomic Characterization of Progression of Multiple Sclerosis

Contains fulltext : 93904.pdf (publisher's version ) (Open Access

Sustained proliferation in cancer: mechanisms and novel therapeutic targets

Proliferation is an important part of cancer development and progression. This is manifest by altered expression and/or activity of cell cycle related proteins. Constitutive activation of many signal transduction pathways also stimulates cell growth. Early steps in tumor development are associated with a fibrogenic response and the development of a hypoxic environment which favors the survival and proliferation of cancer stem cells. Part of the survival strategy of cancer stem cells may manifested by alterations in cell metabolism. Once tumors appear, growth and metastasis may be supported by overproduction of appropriate hormones (in hormonally dependent cancers), by promoting angiogenesis, by undergoing epithelial to mesenchymal transition, by triggering autophagy, and by taking cues from surrounding stromal cells. A number of natural compounds (e.g., curcumin, resveratrol, indole-3-carbinol, brassinin, sulforaphane, epigallocatechin-3-gallate, genistein, ellagitannins, lycopene and quercetin) have been found to inhibit one or more pathways that contribute to proliferation (e.g., hypoxia inducible factor 1, nuclear factor kappa B, phosphoinositide 3 kinase/Akt, insulin-like growth factor receptor 1, Wnt, cell cycle associated proteins, as well as androgen and estrogen receptor signaling). These data, in combination with bioinformatics analyses, will be very important for identifying signaling pathways and molecular targets that may provide early diagnostic markers and/or critical targets for the development of new drugs or drug combinations that block tumor formation and progression

Pollutant-Induced Modulation in Conformation and β-Lactamase Activity of Human Serum Albumin

Structural changes in human serum albumin (HSA) induced by the pollutants 1-naphthol, 2-naphthol and 8-quinolinol were analyzed by circular dichroism, fluorescence spectroscopy and dynamic light scattering. The alteration in protein conformational stability was determined by helical content induction (from 55 to 75%) upon protein-pollutant interactions. Domain plasticity is responsible for the temperature-mediated unfolding of HSA. These findings were compared to HSA-hydrolase activity. We found that though HSA is a monomeric protein, it shows heterotropic allostericity for β-lactamase activity in the presence of pollutants, which act as K- and V-type non-essential activators. Pollutants cause conformational changes and catalytic modifications of the protein (increase in β-lactamase activity from 100 to 200%). HSA-pollutant interactions mediate other protein-ligand interactions, such as HSA-nitrocefin. Therefore, this protein can exist in different conformations with different catalytic properties depending on activator binding. This is the first report to demonstrate the catalytic allostericity of HSA through a mechanistic approach. We also show a correlation with non-microbial drug resistance as HSA is capable of self-hydrolysis of β-lactam drugs, which is further potentiated by pollutants due to conformational changes in HSA

An Effective Assessment of Simvastatin-Induced Toxicity with NMR-Based Metabonomics Approach

BACKGROUND: Simvastatin, which is used to control elevated cholesterol levels, is one of the most widely prescribed drugs. However, a daily excessive dose can induce drug-toxicity, especially in muscle and liver. Current markers for toxicity reflect mostly the late stages of tissue damage; thus, more efficient methods of toxicity evaluation are desired. METHODOLOGY/PRINCIPAL FINDINGS: As a new way to evaluate toxicity, we performed NMR-based metabonomics analysis of urine samples. Compared to conventional markers, such as AST, ALT, and CK, the urine metabolic profile provided clearer distinction between the pre- and post-treatment groups treated with toxic levels of simvastatin. Through multivariate statistical analysis, we identified marker metabolites associated with the toxicity. Importantly, we observed that the treatment group could be further categorized into two subgroups based on the NMR profiles: weak toxicity (WT) and high toxicity (HT). The distinction between these two groups was confirmed by the enzyme values and histopathological exams. Time-dependent studies showed that the toxicity at 10 days could be reliably predicted from the metabolic profiles at 6 days. CONCLUSIONS/SIGNIFICANCE: This metabonomics approach may provide a non-invasive and effective way to evaluate the simvastatin-induced toxicity in a manner that can complement current measures. The approach is expected to find broader application in other drug-induced toxicity assessments