Metabonomics utilises high resolution analytical platforms to generate spectroscopic profiles that are rich in latent biological information. At present, the two principal analytical platforms used routinely in metabonomic studies are nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). While these analytical methods in their current state of development in the field of metabonomics give broad coverage across multiple chemical classes, none report sufficiently well, if at all, on the absolute configuration of chiral molecules. As a consequence of the stereospecificity present in biological systems, a very large number of biologically active molecules exhibit chirality. Therefore, an important aspect of the metabolome remains largely unexplored by current metabonomic analytical platforms. Current enantioselective methods exist, but these are largely unsuitable for multi-analyte measurements. The work in this thesis addresses the need for appropriate chiral analytical methods for metabolic profiling. The aim was to develop a practical, fit-for-purpose chiral analytical method that will report simultaneously on numerous endogenous metabolites by untargeted or widely targeted analysis, and importantly, could be aligned with existing analytical workflows. Initial evaluation of an NMR spectroscopic approach using chiral solvating agents concluded that additional spectral complexity and interaction-induced internal reference compound chemical shift instability would prohibit efficient chiral profiling in a metabonomics workflow. An existing targeted MS-based assay for enantioselective separation of amino acids was selected for optimisation and further development. Focused analyses were performed to characterise its performance in the differentiation of individual pairs of enantiomers, and subsequently expanded, first to cover the panel of proteinogenic amino acids, and secondly to other detectable metabolites. Each stage of assay optimisation incorporated an evaluation in a representative set of samples and was able to provide highly relevant biological information that would not be accessible using current achiral metabonomic methods. Further work to expand the number of detectable metabolites and quantitative nature of the assay are discussed.