School of Chemistry - Theses
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ItemStable isotope labelling and computational data mining approaches in drug metabolism studies( 2017)Many small organic molecules will be chemically modified in some way after entering the body through metabolism. Thus, metabolism plays a significant role in determining the biological properties of a compound including half-life and toxicity profile. Identifying the metabolites of a compound is an important part of drug discovery projects. This thesis describes the development and application of methodologies to detect such metabolites. A guiding principle of this work is the detection of metabolites from a complex sample without prior knowledge of their structure or formation pathways. This ‘non-targeted’ analysis approach allows unknown or unexpected metabolites to be detected, providing a complete picture of the metabolic fate of a xenobiotic. The basis of the non-targeted approach is described in Chapter 2. Here, paracetamol (APAP) and an equal quantity of 13C6-APAP are simultaneously administered to rats. Analysis of blood plasma extracts by liquid chromatography mass spectrometry (LC-MS) resulted in mass spectra that contained pairs of ions that eluted simultaneously with equal intensity and are unique to metabolites. To automate data analysis, software called HiTIME was written enabling the non-targeted but selective detection of metabolites that appear as twin-ions from highly complex samples. In some cases, xenobiotics form electrophilic metabolites that can covalently react with cellular proteins. This is thought to trigger allergic and toxic side-effects. The specific nature of the protein adduct may be a determinant of the biological response. Chapter 3 describes a reactivity survey of the electrophilic APAP metabolite, N-acetyl-p-benzoquinoneimine (NAPQI), towards a panel of amino acids and peptides. In addition to the well-known reactivity toward cysteine, previously undocumented covalent adducts between chemically synthesized NAPQI and tyrosine, tryptophan and methionine were also observed, isolated and characterised. Chapter 4 introduces a non-targeted method to identify the protein targets of reactive metabolites which uses twin-ion and HiTIME analysis to detect tryptic peptides that have been covalently modified by drug metabolites. Software called Xenophile was developed that can identify the site of modification, the mass and the chemical formula of a reactive metabolite directly from shotgun LC-MS data. In Chapter 5, Xenophile was applied to identify the protein targets of APAP and 13C6-APAP metabolites following incubation with liver tissue extracts and global trypsin digest. The Xenophile software correctly identified the reactive metabolite as C8H7NO2 (i.e. NAPQI) and the adduction site as Cysteine residues. Further investigation identified 7 unique proteins that were modified by APAP including those that have been previously identified as adduction targets of NAPQI and other xenobiotic reactive metabolites. HiTIME and Xenophile are then used to assess the small molecule metabolism and protein adduction profile of the environmental contaminants benzene, bromobenzene and toluene in liver extracts. Numerous twin-ions were detected that correspond to glutathione adducts of epoxide and quinone metabolites. No modified proteins were detected following analysis of global protein digests for any sample. To rule out false negatives, targeted approaches were taken to identify protein adducts. As these did not result in the recovery of any missed peptides, we conclude that protein adducts were not formed in these experiments. This finding is rationalized based on the extent of formation of small molecule GSH adducts.