Metabolic pathway elucidation towards time- and dose-dependent electrophoretic screening of stable oxidative phenolic compounds.

This study demonstrates an untested link between model phenolic compounds and the formation/electrophoretic separation of stable urinary metabolites. Sterically encumbered carbonyl groups were examined, and mass determination was used to confirm the presence and stability of two oxidative metabolites of pentachlorophenol: tetrachloro-1,2-benzoquinone and tetrachloro-1,4-dihydroquinone. Subsequently, baseline resolved separation of pentachlorophenol and the two oxidative metabolites was demonstrated under the following conditions: 75 mM sodium tetraborate buffer (pH = 8.5) with 5 % methanol and 50 mM SDS, +10.0 kV running voltage, injection time = 5.0 s, effective capillary length = 55 cm, and run temperature = 20 °C. Results not only provide key metabolic inferences for pentachlorophenol, they also exhibit improvements in the ability to separate and detect changes in urinary metabolites in response to phenolic-related exposure.

Biologically driven neural platform invoking parallel electrophoretic separation and urinary metabolite screening.

This work reveals a computational framework for parallel electrophoretic separation of complex biological macromolecules and model urinary metabolites. More specifically, the implementation of a particle swarm optimization (PSO) algorithm on a neural network platform for multiparameter optimization of multiplexed 24-capillary electrophoresis technology with UV detection is highlighted. Two experimental systems were examined: (1) separation of purified rabbit metallothioneins and (2) separation of model toluene urinary metabolites and selected organic acids. Results proved superior to the use of neural networks employing standard back propagation when examining training error, fitting response, and predictive abilities. Simulation runs were obtained as a result of metaheuristic examination of the global search space with experimental responses in good agreement with predicted values. Full separation of selected analytes was realized after employing optimal model conditions. This framework provides guidance for the application of metaheuristic computational tools to aid in future studies involving parallel chemical separation and screening. Adaptable pseudo-code is provided to enable users of varied software packages and modeling framework to implement the PSO algorithm for their desired use.