PHARMACODYNAMIC STUDY OF ORAL MIRTOSELECT IN PATIENTS WITH SUSPECTED COLORECTAL DISEASE
Ontology highlight
ABSTRACT:
DISEASE(S): Colorectal Cancer,Patients With Operable Colorectal Cancer
PROVIDER: 2520418 | ecrin-mdr-crc |
REPOSITORIES: ECRIN MDR
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2019-12-31 | GSE118922 | GEO
Project description:In this study, small RNAs were isolated from individual donations of eight forensically relevant biological fluids (blood, semen, vaginal fluid, menstrual blood, saliva, urine, feces, and perspiration) and subjected to next generation sequencing using the Illumina® Hi-Seq platform. Sequencing reads were aligned and annotated against miRbase release 21, resulting in a list of miRNAs and their relative expression levels for each sample analyzed. Body fluids with high bacterial loads (vaginal fluid, saliva, and feces) yielded relatively low annotated miRNA counts, likely due to oversaturation of small RNAs from the endogenous bacteria. Both body-fluid specific and potential normalization miRNAs were identified for further analysis as potential body fluid identification tools for each body fluid. 32 samples - 3-5 replicates of each human biological fluid: venous blood, urine, semen (normal and vasectomized), vaginal secretions, menstrual secretions, perspiration, feces, saliva
2016-08-20 | E-GEOD-85830 | biostudies-arrayexpress
Project description:Horse urine is easily collected and contains molecules readily measurable using mass spectrometry that can be used as biomarkers representative of health, disease or drug tampering. This study aimed at analyzing microliter levels of horse urine to purify, identify and quantify proteins, polar metabolites and non-polar lipids. Urine from a healthy 12 year old quarter horse mare on a diet of grass hay and vitamin/mineral supplements with limited pasture access was collected for serial-omics characterization. The urine was treated with methyl tert-butyl ether (MTBE) and methanol to partition into three distinct layers for protein, non-polar lipid and polar metabolite content from a single liquid-liquid extraction and was repeated two times. Each layer was analyzed by high performance liquid chromatography – high resolution tandem mass spectrometry (LC-MS/MS) to obtain protein sequence and relative protein levels as well as identify and quantify small polar metabolites and lipids. The results show 46 urine proteins, many related to normal kidney function, structural and circulatory proteins as well as 474 small polar metabolites but only 10 lipid molecules. Metabolites were mostly related to urea cycle and ammonia recycling as well as amino acid related pathways, plant diet specific molecules, etc. The few lipids represented triglycerides and phospholipids. These data show a complete mass spectrometry based –omics characterization of equine urine from a single 333 uL mid-stream urine aliquot. The data can be used as a baseline for healthy urine composition and analyses can be used to monitor disease progression, health status, monitor drug use, etc.
2018-07-23 | PXD007646 | Pride
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Project description:This model is supplementary material of publication "Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues"
by Darta Maija Zake, Linda Zaharenko, Janis Kurlovics, Vitalijs Komasilovs, Janis Klovins and Egils Stalidzans.
The model is pre-set for simulation of a single peroral dose.
This is a whole-body model representing the pharmacokinetics of metformin in the mouse body. The model is in the form of ordinary differential equations and describes metformin concentration in 20 compartments.
The model consists of 20 compartments (“Compartments” in COPASI model) describing various tissues or tissue sub-compartments and body fluids of metformin action (venous and arterial plasma, intestine, kidney, heart, fat, muscle, brain, lungs, stomach, liver, portal vein, remainder urine and feces). Body weight and the weight of all compartments is expressed as a volume in mL and for the calculations it is assumed that 1mL = 1g. The volumes of most compartments are calculated as a fraction of the body weight/volume, and the fractions are determined from literature data, the volumes of the stomach lumen and intestine lumen are fixed and do not change depending on the body weight. Similarly, the volume of external urine and feces is set to 1mL, but those are “volumeless” compartments as they are only necessary for the calculation of metformin amount, not concentration.
The model consists of 20 species (“Species” in COPASI model) that correspond to the metformin concentrations in the 20 compartments. The initial concentrations for all the species are 0 nmol/mL as metformin is not produced in the body and can only be detected after dose administration.
The model consists of 33 reactions – they describe the transport processes of metformin in the body. The reactions include local parameters that are involved only in that particular reaction and global parameters – parameters that are used in multiple reactions or are calculated depending on another parameter e.g. scale-up coefficients.
The model consists of 52 global quantities – parameters involved in multiple reactions or necessary for another parameter calculation:
1.Parameters describing metformin dose – either in peroral (Metformin Dose in Lumen in mg) or intravenous (Metformin Dose in Plasma in mg).
2.Parameter describing mice physiology – body weight (in mL), cardiac output, blood flow to different compartments described as Q”compartment_name” (for example Qliver describes blood flow to the liver compartment). Qgfr refers to the glomerular filtration rate.
3.Tissue:plasma partition coefficients (Ktp) that are necessary for the scale-up to humans.
4.Parameters involved in the calculation of metformin amount in mg, these parameters are named mg”Compartment_name” (for example mgLiver describes the metformin amount in mg in the liver tissues).
The time points of dose release are defined as “events” in COPASI and can be changed as necessary.
Time course simulations can be accessed through the section “Time Course” in this section the time duration and intervals can be changed. When time-course simulations are run three plots are created – Metformin amount in the 20 compartments, metformin concentrations in the compartments and reaction fluxes of all the reactions (see “Output Specifications” -> “Plots” to activate or deactivate plots).
2024-09-02 | BIOMD0000001027 | BioModels
Project description:This model is supplementary material of publication "Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues"
by Darta Maija Zake, Linda Zaharenko, JanisKurlovics, Vitalijs Komasilovs, Egils Stalidzans and Janis Klovins.
This is a whole-body model representing the pharmacokinetics of metformin in the mouse body. The model is in the form of ordinary differential equations and describes metformin concentration in 20 compartments.
The model consists of 20 compartments (“Compartments” in COPASI model) describing various tissues or tissue sub-compartments and body fluids of metformin action (venous and arterial plasma, intestine, kidney, heart, fat, muscle, brain, lungs, stomach, liver, portal vein, remainder urine and feces). Body weight and the weight of all compartments is expressed as a volume in mL and for the calculations it is assumed that 1mL = 1g. The volumes of most compartments are calculated as a fraction of the body weight/volume, and the fractions are determined from literature data, the volumes of the stomach lumen and intestine lumen are fixed and do not change depending on the body weight. Similarly, the volume of external urine and feces is set to 1mL, but those are “volumeless” compartments as they are only necessary for the calculation of metformin amount, not concentration.
The model consists of 20 species (“Species” in COPASI model) that correspond to the metformin concentrations in the 20 compartments. The initial concentrations for all the species are 0 nmol/mL as metformin is not produced in the body and can only be detected after dose administration.
The model consists of 33 reactions – they describe the transport processes of metformin in the body. The reactions include local parameters that are involved only in that particular reaction and global parameters – parameters that are used in multiple reactions or are calculated depending on another parameter e.g. scale-up coefficients.
The model consists of 52 global quantities – parameters involved in multiple reactions or necessary for another parameter calculation:
1.Parameters describing metformin dose – either in peroral (Metformin Dose in Lumen in mg) or intravenous (Metformin Dose in Plasma in mg).
2.Parameter describing mice physiology – body weight (in mL), cardiac output, blood flow to different compartments described as Q”compartment_name” (for example Qliver describes blood flow to the liver compartment). Qgfr refers to the glomerular filtration rate.
3.Tissue:plasma partition coefficients (Ktp) that are necessary for the scale-up to humans.
4.Parameters involved in the calculation of metformin amount in mg, these parameters are named mg”Compartment_name” (for example mgLiver describes the metformin amount in mg in the liver tissues).
The time points of dose release are defined as “events” in COPASI and can be changed as necessary.
Time course simulations can be accessed through the section “Time Course” in this section the time duration and intervals can be changed. When time-course simulations are run three plots are created – Metformin amount in the 20 compartments, metformin concentrations in the compartments and reaction fluxes of all the reactions (see “Output Specifications” -> “Plots” to activate or deactivate plots). Also plotting the species result after 0.5 hours will reproduce the literature results.
2024-09-02 | BIOMD0000001039 | BioModels
Project description:BACKGROUND: As evidenced by epidemiological and etiological studies, the development of varicose veins is driven by risk-factors which support the development of venous hypertension and thus chronically augment circumferential stress of the venous wall (e.g. dysfunctional venous valves, pregnancy or obesity). We have previously verified the relevance of this biomechanical stimulus for the activation of venous endothelial as well as smooth muscle cells and the subsequent detrimental structural remodeling of the vein wall in experimental mouse models. METHODS: Here, transcriptome analyses revealed an increase in the expression of cyclooxygenase 2 (COX-2) in human venous endothelial cells upon exposure to biomechanical stress. Subsequently, we investigated the impact of diclofenac – a cyclooxygenase inhibitor – on responses of isolated mouse veins to augmented wall stress in vitro and on varicose-like venous remodeling in vivo. RESULTS: Diclofenac treatment decreased COX-2 protein abundance in mouse veins but had no significant impact on the expression of corresponding transcripts. Short-term exposure to elevated pressure levels stimulated the activity of matrix-metalloproteinase-2 (MMP-2) and mitogen activated protein kinases ERK1/2. Diclofenac decreased the level of activated MMP-2 and ERK1/2 in pressure-exposed mouse veins. Varikose-like remodeling of veins in the mouse auricle was significantly inhibited by transdermal application of diclofenac-containing phospholipid-micelles. This effect was associated with decreased COX-2 and MMP-2 abundance as well as cell proliferation. CONCLUSION: The cyclooxygenase inhibitor diclofenac interferes with short term activation of MAP-kinases and matrix-metalloproteinases in cells of the wall stress-exposed venous wall while attenuating venous remodeling in vivo. Thus, nonsteroidal anti-inflammatory drugs may be suitable to interfere with processes promoting the progression of varicose vein development and biomechanical activation of venous cells.
2021-01-12 | GSE121557 | GEO
Project description:1H NMR metabolomics was performed on urine samples weekly and on plasma and liver samples collected at week 4 from these animals, to identify metabolites that respond to protein undernutrition. To further investigate metabolites which were fluctuated in response to protein undernutrition in terms of metabolic enzymes, hepatic mRNA microarray and quantitative PCR analyses were also performed at week 4.
2020-01-25 | GSE144207 | GEO
Project description:To assess underlying metabolic processes and regulatory mechanisms during cold exposure of strawberry, integrative “omic” approaches were applied to F. ananassa ‘Korona’. Analysis of leaf tissue was emphasized because of its immediate and early responses in the cold acclimation processes. Levels of metabolites, proteins, and transcripts in tissues from plants grown at 20°C were compared to those following 1 to 10 days of cold exposure. When leaves and roots were subjected to GC/TOF-MS-based metabolite profiling, about 160 compounds comprising mostly structurally annotated primary metabolites and secondary metabolites, were found. Overall, ‘Korona’ showed a modest increase of protective metabolites such as amino acids (aspartic acid, leucine, isoleucine, and valine), pentoses, phosphorylated and non-phosphorylated hexoses, and distinct compounds of the raffinose pathway (galactinol and raffinose). Distinctive responses were observed in roots and leaves. By 2DE proteomics a total of 845 spots were observed in leaves; 4.6% changed significantly in response to cold. Twenty-one proteins were identified, many of which were associated with general metabolism or photosynthesis. Transcript levels in leaves were determined by microarray, where dozens of cold associated transcripts were quantitatively characterized, and levels of several potential key contributors (e.g., the dehydrin COR47 and GADb) to cold tolerance were confirmed by qRT-PCR. Cold responses are placed within the existing knowledge base of low temperature stress change in plants, allowing an evaluation of the uniqueness or generality of Fragaria responses in photosynthetic tissues. Overall, the cold response characteristics of ‘Korona’ are consistent with a moderately cold tolerant plant.
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Project description:The effect of oral microbiota on the intestinal microbiota has garnered growing attention as a mechanism linking periodontal diseases to systemic diseases. However, the salivary microbiota is diverse and comprises numerous bacteria with a largely similar composition in healthy individuals and periodontitis patients. Thus, the systemic effects of small differences in the oral microbiota are unclear. In this study, we explored how health-associated and periodontitis-associated salivary microbiota differently colonized the intestine and their subsequent systemic effects by analyzing the hepatic gene expression and serum metabolomic profiles. The salivary microbiota was collected from a healthy individual and a periodontitis patient and gavaged into C57BL/6NJcl[GF] mice. Samples were collected five weeks after administration. Gut microbial communities were analyzed by 16S ribosomal RNA gene sequencing. Hepatic gene expression profiles were analyzed using a DNA microarray and quantitative polymerase chain reaction. Serum metabolites were analyzed by capillary electrophoresis time-of-flight mass spectrometry. The gut microbial composition at the genus level was significantly different between periodontitis-associated microbiota-administered (PAO) and health-associated oral microbiota-administered (HAO) mice. The hepatic gene expression profile demonstrated a distinct pattern between the two groups, with higher expression of Neat1, Mt1, Mt2, and Spindlin1, which are involved in lipid and glucose metabolism. Disease-associated metabolites such as 2-hydroxyisobutyric acid and hydroxybenzoic acid were elevated in PAO mice. These metabolites were significantly correlated with Bifidobacterium, Atomobium, Campylobacter, and Haemophilus, which are characteristic taxa in PAO mice. Conversely, health-associated oral microbiota were associated with higher levels of beneficial serum metabolites in HAO mice. The multi-omics approach used in this study revealed that periodontitis-associated oral microbiota is associated with the induction of disease phenotype when they colonized the gut of germ-free mice.
2022-08-17 | GSE198570 | GEO