Using HPLC retention parameters to estimate fish bioconcentration factors of organic compounds

Reversed-phase high performance liquid chromatography (RP-HPLC) was employed to develop predictive models for fish bioconcentration factors (BCF) of organic compounds. Estimation of BCF from RP-HPLC retention parameters on octadecyl-bonded silica gel (ODS), cyanopropyl-bonded silica gel (CN), and phenyl-bonded silica gel (Ph) columns were investigated. The results show that, for a set of compounds belonging to different chemical classes, the CN stationary phase is the best one among the three columns and better than n-octanol/water model for BCF estimation. A multi-column RP-HPLC model, using the retention parameters on the CN and Ph columns as the variables of multiple linear regression equations, was further evaluated to estimate BCF of organic compounds belonging to different chemical classes, and the results show that the multi-column RP-HPLC model is better than that of any single RP-HPLC column for BCF estimation.

Retention modeling and simultaneous optimization of pH value and gradient steepness in RP-HPLC using feed-forward neural networks

A novel approach is proposed for the simultaneous optimization of mobile phase pH and gradient steepness in RP-HPLC using artificial neural networks. By presetting the initial and final concentration of the organic solvent, a limited number of experiments with different gradient time and pH value of mobile phase are arranged in the two-dimensional space of mobile phase parameters. The retention behavior of each solute is modeled using an individual artificial neural network. An "early stopping" strategy is adopted to ensure the predicting capability of neural networks. The trained neural networks can be used to predict the retention time of solutes under arbitrary mobile phase conditions in the optimization region. Finally, the optimal separation conditions can be found according to a global resolution function. The effectiveness of this method is validated by optimization of separation conditions for amino acids derivatised by a new fluorescent reagent.

Retention modeling and optimization of pH value and solvent composition in HPLC using back-propagation neural networks and uniform design

A novel method for the optimization of pH value and composition of mobile phase in HPLC using artificial neural networks and uniform design is proposed. As the first step. seven initial experiments were arranged and run according to uniform design. Then the retention behavior of the solutes is modeled using back-propagation neural networks. A trial method is used to ensure the predicting capability of neural networks. Finally, the optimal separation conditions can be found according to a global resolution function. The effectiveness of this method is validated by optimization of separation conditions for both basic and acidic samples.

Determination of peptides and amino acids from wool and beer with sensitive fluorescent reagent 2-(9-carbazole)-ethyl chloroformate by reverse phase high-performance liquid chromotography and liquid chromotography mass spectrometry

A new method for the sensitive determination of amino acids and peptides using the tagging reagent 2-(9-carbazole)-ethyl chloroformate (CEOC) with fluorescence (FL) detection has been developed. Identification of derivatives was carried out by liquid chromotography mass spectrometry. The chromophore in the 2-(9-fluorenyl)-ethyl chloroformate (FMOC) reagent was replaced by carbazole, which resulted in a sensitive fluorescence lerivatizing agent CEOC. CEOC can easily and quickly label peptides and amino acids. Derivatives are stable enough to be efficiently analyzed by high-performance liquid chromatography. Studies on derivatization demonstrate excellent derivative yields over the pH range 8.8-10.0. Maximal yields close to 100% are observed with three- to fourfold molar reagent excess. Derivatives exhibit strong fluorescence and allow direct injection of the reaction mixture with no significant disturbance from the major fluorescent reagent degradation by-products, such as 2(9-carbazole)-ethanol and bis-(2-(9-carbazole)-ethyl) carbonate. In addition, the detection responses for CEOC derivatives are compared to those obtained with FMOC. The ratios AC(CEOC)/AC(FMOC) = 1.00-1.82 for fluorescence (FL) response and AC'(CEOC)/AC'(FMOC) = 1.00-1.21 for ultraviolet (UV) response are observed (here, AC and AC' are, respectively, FL and UV F response). Separation of the derivatized peptides and amino acids has been optimized on a Hypersil BDS C18 column. Excellent linear responses are observed. This method was used successfully to analyze protein hydrolysates from wool and from direct-derivatized beer. (C) 2003 Elsevier Science (USA). All rights reserved.

Study of normal and modified nucleosides in serum by RP-HPLC

Modified nucleosides derived predominantly from transfer ribonucleic acid (tRNA) have been studied as possible tumor markers. In this paper a reversed-phase high-performance liquid chromatographic (RP-HPLC) method has been applied to study 15 normal and modified nucleosides in serum. The nucleoside levels in normal human serum were established, and the concentrations of 15 nucleosides in serum from 19 cancer patients were determined, it was found that the concentrations of modified nucleosides were significantly higher in patient sera. Based on 15 nucleoside concentrations in serum, factor analysis could classify correctly 90% of cancer patients from the normal humans Further work showed that urine was slightly better than serum when determining nucleosides as biological marker candidates. More work is ongoing to determine the clinical usefulness of modified nucleosides as tumor markers.

Excretion pattern investigation of urinary normal and modified nucleosides of breast cancer patients by RP-HPLC and factor analysis method

Modified nucleosides, formed post-transcriptionally in RNA by a number of modification enzymes, are excreted in abnormal levels in the urine of patients with malignant tumors. To test their usefulness as tumor markers, and to compare them with the conventional tumor markers, a reversed-phase high-performance liquid chromatographic (RP-HPLC) method and a factor analysis method have been used to study the excretion pattern of nucleosides of breast cancer patients. A clear cut differentiation of the breast cancer group and the healthy individuals in two clusters without overlapping was obtained. Copyright (C) 2000 John Wiley & Sons, Ltd.