Journal of Bioanalysis & Biomedicine

A simple and rapid method has been developed and validated for determination of ticlopidine in human plasma using protein precipitation and liquid chromatography coupled with tandem mass spectrometry (LC/MS/MS). Ticlopidine was extracted from 20-μL aliquots of human plasma by one-step protein precipitation with 980 μL of acetonitrile containing 10 ng/mL clopidogrel as an internal standard (IS). Chromatographic separation was performed on a reverse-phase Gemini C 18 column (50 mm x 2.0 mm, 5 μm) with an isocratic mobile phase (acetonitrile: 1 mM ammonium acetate in water = 75:25, v/v). Ticlopidine and IS were detected and quantified by tandem mass spectrometry with positive electrospray ionization using multiple reaction monitoring of the transition m/z 264.04 to m/z 154.20 for ticlopidine and m/z 322.40 to m/z 212.20 for IS. This method was linear over the concentrations ranging from 2 to 2000 ng/mL. The accuracy in the inter-batch assay was 92.4-95.6% and the precision was within 6.4% coefficient of variation. The validated method was successfully applied to the human pharmacokinetic study of ticlopidine.

A rapid and sensitive liquid chromatography tandem mass spectrometry (LC–MS/MS) method has been developed for quantitative determination of Tamsulosin (TMS). The analyte was extracted from human plasma by Liquid-Liquid Extraction (LLE) using Methyl Tertiary Butyl Ether (MTBE) and Dichloromethane (DCM). Tolteridone Tartrate (TLT) was used as the internal standard. A Phenomenex RP-18 (50 x 4.6 mm i.d., 5μ) column provided chromatographic separation of the analyte using a mobile phase containing Acetonitrile: 0.01M ammonium formate (pH9.0) (90:10) at a flow rate of 0.8 ml/min with an elution time as low as 3.0 min which was followed by detection with mass spectrometry. The Multiple Reaction Monitoring (MRM) pair (m/z) 409.3/271.4 for TMS and 326.4/147.1 for TLT. Simple isocratic chromatographic conditions and mass spectrometric detection of the method enables the detection of TMS at less than nanogram levels. The proposed method was found to be linear from 0.20-100.08 ng/ ml. The precision and accuracy values are within 10%. The overall recovery of TMS was 90.92 %.

The chemical characterization of Agaricus sylvaticus (A. sylvaticus) cultivated in Brazil is necessary to determine nutritional and pharmacological substances in order to guarantee its safe use as food or herbal medicine. The objective of this study was to determine the chemical composition and assess the antioxidant potential of A. sylvaticus fungi grown in Brazil. Through this study it was able to observe the rich chemical composition of A. sylvaticus, highlighting the variety and amount of minerals as well as the high protein content of this fungus. It was also observed the great antioxidant potential of the aqueous, alcoholic and ethereal A. sylvaticus mushroom extracts, emphasizing the alcoholic extract, which testifies the extraordinary benefits of this fungus in diet, since antioxidants prevent premature aging and various types of cancer as well. The composition of A. sylvaticus mushroom displayed differences when compared to the chemical composition of the same fungus in other studies and with other Agaricales fungi.

There are many ways to normalize biofluid metabolomics data to account for changes in dilution, all of which have been thoroughly examined in model systems. Here, urine metabolomics data was examined under relevant physiological conditions obtained from a porcine model of hemorrhagic shock and resuscitation. This includes highly variable intravascular fluid volume and urine output coupled with large perturbations in the abundance of endogenous metabolites. Seven different normalization techniques and raw data were evaluated to determine an appropriate normalization technique in this setting, including spectral post-processing methods and physiological measures of concentration. Relationships between normalization constants for each urine sample were examined, as well as relationships between urinary and serum creatinine concentrations. Principal components analysis was used to examine clustering of metabolomics data. The set of normalization constants associated with each sample were reflective of urine concentration, with a trend toward concentration decreases during late resuscitation timepoints. Urinary creatinine normalized to urine output was most reflective of serum creatinine levels. Principal components analysis showed that urine samples clustered according to experimental timepoint for all normalization methods examined. Little separation was seen in raw data. Urine output-normalized data stands out from the six other normalization methods studied because it is reflective of renal clearance and should be used when comparing urine and serum metabolomics data.