Ciprofibrate quantification in human plasma by high-performance liquid chromatography coupled with electrospray tandem mass spectrometry for pharmacokinetic studies

A rapid, sensitive and specific method for quantifying ciprofibrate in human plasma using bezafibrate as the internal standard (IS) is described. The sample was acidified prior extraction with formic acid (88%). The analyte and the IS were extracted from plasma by liquid-liquid extraction using an organic solvent (diethyl ether/dichloromethane 70/30 (v/v)). The extracts were analyzed by high performance liquid chromatography coupled with electrospray tandem mass spectrometry (HPLC-MS/MS). Chromatography was performed using Genesis C18 4 mu m analytical column (4.6 x 150 mm i.d.) and a mobile phase consisting of acetonitrile/water (70/30, v/v) and 1 mM acetic acid. The method had a chromatographic run time of 3.4 min and a linear calibration curve over the range 0.1-60 mu g/mL (r > 0.99). The limit of quantification was 0.1 mu g/mL. The intra- and interday accuracy and precision values of the assay were less than 13.5%. The stability tests indicated no significant degradation. The recovery of ciprofibrate was 81.2%, 73.3% and 76.2% for the 0.3, 5.0 and 48.0 ng/mL standard concentrations, respectively. For ciprofibrate, the optimized parameters of the declustering potential, collision energy and collision exit potential were -51 V, -16 eV and -5 V, respectively. The method was also validated without the use of the internal standard. This HPLC-MS/MS procedure was used to assess the bioequivalence of two ciprofibrate 100 mg tablet formulations in healthy volunteers of both sexes. The following pharmacokinetic parameters were obtained from the ciprofibrate plasma concentration vs. time curves: AUC(last), AUC(0-168 h), C(max) and T(max). The geometric mean with corresponding 90% confidence interval (CI) for test/reference percent ratios were 93.80% (90% CI = 88.16-99.79%) for C(max), 98.31% (90% CI = 94.91-101.83%) for AUC(last) and 97.67% (90% CI = 94.45-101.01%) for AUC(0-168 h). Since the 90% Cl for AUC(last), AUC(0-168 h) and C(max) ratios were within the 80-125% interval proposed by the US FDA, it was concluded that ciprofibrate (Lipless (R) 100 mg tablet) formulation manufactured by Biolab Sanus Farmaceutica Ltda. is bioequivalent to the Oroxadin (R) (100 mg tablet) formulation for both the rate and the extent of absorption. (C) 2011 Published by Elsevier B.V.

Evaluation of monolithic columns for determination of formaldehyde and acetaldehyde in sugar cane spirits by High-Performance Liquid Chromatography

High-Performance Liquid Chromatography (HPLC) conditions are described for separation of 2,4-dinitrophenylhydrazone (2,4-DNPH) derivatives of carbonyl compounds in a 10 cm long C-18 reversed phase monolithic column. Using a linear gradient from 40 to 77% acetonitrile (acetonitrile-water system), the separation was achieved in about 10 min-a time significantly shorter than that obtained with a packed particles column. The method was applied for determination of formaldehyde and acetaldehyde in Brazilian sugar cane spirits. The linear dynamic range was between 30 and 600 mu g L-1, and the detection limits were 8 and 4 mu g L-1 for formaldehyde and acetaldehyde, respectively.

A multi-GPU algorithm for large-scale neuronal networks

Large-scale simulations of parts of the brain using detailed neuronal models to improve our understanding of brain functions are becoming a reality with the usage of supercomputers and large clusters. However, the high acquisition and maintenance cost of these computers, including the physical space, air conditioning, and electrical power, limits the number of simulations of this kind that scientists can perform. Modern commodity graphical cards, based on the CUDA platform, contain graphical processing units (GPUs) composed of hundreds of processors that can simultaneously execute thousands of threads and thus constitute a low-cost solution for many high-performance computing applications. In this work, we present a CUDA algorithm that enables the execution, on multiple GPUs, of simulations of large-scale networks composed of biologically realistic Hodgkin-Huxley neurons. The algorithm represents each neuron as a CUDA thread, which solves the set of coupled differential equations that model each neuron. Communication among neurons located in different GPUs is coordinated by the CPU. We obtained speedups of 40 for the simulation of 200k neurons that received random external input and speedups of 9 for a network with 200k neurons and 20M neuronal connections, in a single computer with two graphic boards with two GPUs each, when compared with a modern quad-core CPU. Copyright (C) 2010 John Wiley & Sons, Ltd.