Diss. (sammanfattning) Umeå : Umeå universitet, 2009. / Härtill 5 uppsatser. Även tryckt utgåva.

To be able to make informed descicions regarding the research of new drug molecules (ligands), it is crucial to have access to information regarding the chemical interaction between the drug and its biological target (protein). Computer-based methods have a given role in drug research today and, by using methods such as molecular docking, it is possible to investigate the way in which ligands and proteins interact. Despite the acceleration in computer power experienced in the last decades many problems persist in modelling these complicated interactions. The main objective of this thesis was to investigate and improve molecular modelling methods aimed to estimate protein-ligand binding. In order to do so, we have utilised chemometric tools, e.g. design of experiments (DoE) and principal component analysis (PCA), in the field of molecular modelling. More specifically, molecular docking was investigated as a tool for reproduction of ligand poses in protein 3D structures and for virtual screening. Adjustable parameters in two docking software were varied using DoE and parameter settings were identified which lead to improved results. In an additional study, we explored the nature of ligand-binding cavities in proteins since they are important factors in protein-ligand interactions, especially in the prediction of the function of newly found proteins. We developed a strategy, comprising a new set of descriptors and PCA, to map proteins based on their cavity physicochemical properties. Finally, we applied our developed strategies to design a set of glycopeptides which were used to study autoimmune arthritis. A combination of docking and statistical molecular design, synthesis and biological evaluation led to new binders for two different class II MHC proteins and recognition by a panel of T-cell hybridomas. New and interesting SAR conclusions could be drawn and the results will serve as a basis for selection of peptides to include in in vivo studies.

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Pelchem, a commercial subsidiary of Necsa (South African Nuclear Energy Corporation), produces a range of commercial fluorocarbon products while driving research and development initiatives to support the fluorine product portfolio. One such initiative is to develop improved analytical techniques to analyse product composition during development and to quality assure produce. Generally the C–F type products produced by Necsa are in a solution of anhydrous HF, and cannot be directly analyzed with traditional techniques without derivatisation. A technique such as vibrational spectroscopy, that can analyze these products directly without further preparation, will have a distinct advantage. However, spectra of mixtures of similar compounds are complex and not suitable for traditional quantitative regression analysis. Multivariate data analysis (MVA) can be used in such instances to exploit the complex nature of spectra to extract quantitative information on the composition of mixtures. A selection of fluorocarbon alcohols was made to act as representatives for fluorocarbon compounds. Experimental design theory was used to create a calibration range of mixtures of these compounds. Raman and infrared (NIR and ATR–IR) spectroscopy were used to generate spectral data of the mixtures and this data was analyzed with MVA techniques by the construction of regression and prediction models. Selected samples from the mixture range were chosen to test the predictive ability of the models. Analysis and regression models (PCR, PLS2 and PLS1) gave good model fits (R2 values larger than 0.9). Raman spectroscopy was the most efficient technique and gave a high prediction accuracy (at 10% accepted standard deviation), provided the minimum mass of a component exceeded 16% of the total sample. The infrared techniques also performed well in terms of fit and prediction. The NIR spectra were subjected to signal saturation as a result of using long path length sample cells. This was shown to be the main reason for the loss in efficiency of this technique compared to Raman and ATR–IR spectroscopy. It was shown that multivariate data analysis of spectroscopic data of the selected fluorocarbon compounds could be used to quantitatively analyse mixtures with the possibility of further optimization of the method. The study was a representative study indicating that the combination of MVA and spectroscopy can be used successfully in the quantitative analysis of other fluorocarbon compound mixtures. / Thesis (M.Sc. (Chemistry))--North-West University, Potchefstroom Campus, 2012.

Pelchem, a commercial subsidiary of Necsa (South African Nuclear Energy Corporation), produces a range of commercial fluorocarbon products while driving research and development initiatives to support the fluorine product portfolio. One such initiative is to develop improved analytical techniques to analyse product composition during development and to quality assure produce. Generally the C–F type products produced by Necsa are in a solution of anhydrous HF, and cannot be directly analyzed with traditional techniques without derivatisation. A technique such as vibrational spectroscopy, that can analyze these products directly without further preparation, will have a distinct advantage. However, spectra of mixtures of similar compounds are complex and not suitable for traditional quantitative regression analysis. Multivariate data analysis (MVA) can be used in such instances to exploit the complex nature of spectra to extract quantitative information on the composition of mixtures. A selection of fluorocarbon alcohols was made to act as representatives for fluorocarbon compounds. Experimental design theory was used to create a calibration range of mixtures of these compounds. Raman and infrared (NIR and ATR–IR) spectroscopy were used to generate spectral data of the mixtures and this data was analyzed with MVA techniques by the construction of regression and prediction models. Selected samples from the mixture range were chosen to test the predictive ability of the models. Analysis and regression models (PCR, PLS2 and PLS1) gave good model fits (R2 values larger than 0.9). Raman spectroscopy was the most efficient technique and gave a high prediction accuracy (at 10% accepted standard deviation), provided the minimum mass of a component exceeded 16% of the total sample. The infrared techniques also performed well in terms of fit and prediction. The NIR spectra were subjected to signal saturation as a result of using long path length sample cells. This was shown to be the main reason for the loss in efficiency of this technique compared to Raman and ATR–IR spectroscopy. It was shown that multivariate data analysis of spectroscopic data of the selected fluorocarbon compounds could be used to quantitatively analyse mixtures with the possibility of further optimization of the method. The study was a representative study indicating that the combination of MVA and spectroscopy can be used successfully in the quantitative analysis of other fluorocarbon compound mixtures. / Thesis (M.Sc. (Chemistry))--North-West University, Potchefstroom Campus, 2012.

A trend in natural science and especially in analytical chemistry is the increasing need for analysis of a large number of complex samples with low analyte concentrations. Biological samples (urine, blood, plasma, cerebral spinal fluid, tissue etc.) are often suitable for analysis with liquid separation mass spectrometry (LS-MS), resulting in two-way data tables (time vs. m/z). Such biological 'fingerprints' taken for all samples in a study correspond to a large amount of data. Detailed characterization requires a high sampling rate in combination with high mass resolution and wide mass range, which presents a challenge in data handling and exploration. This thesis describes methods for managing and exploring large data sets made up of such detailed 'fingerprints' (represented as data matrices). The methods were implemented as scripts and functions in Matlab, a wide-spread environment for matrix manipulations. A single-file structure to hold the imported data facilitated both easy access and fast manipulation. Routines for baseline removal and noise reduction were intended to reduce the amount of data without loosing relevant information. A tool for visualizing and exploring single runs was also included. When comparing two or more 'fingerprints' they usually have to be aligned due to unintended shifts in analyte positions in time and m/z. A PCA-like multivariate method proved to be less sensitive to such shifts, and an ANOVA implementation made it easier to find systematic differences within the data sets. The above strategies and methods were applied to complex samples such as plasma, protein digests, and urine. The field of application included urine profiling (paracetamole intake; beverage effects), peptide mapping (different digestion protocols) and search for potential biomarkers (appendicitis diagnosis) . The influence of the experimental factors was visualized by PCA score plots as well as clustering diagrams (dendrograms).

Industrial chemicals in European Union produced or imported in volumes above 1 tonne annually, necessitate a registration within REACH. A common problem, concerning these chemicals, is deficient information and lack of data for assessing the hazards posed to human health and the environment. Animal studies for the type of toxicological information needed are both expensive and time consuming, and to that an ethical aspect is added. Alternative methods to animal testing are thereby requested. REACH have called for an increased use of in silico tools for non-testing data as structure-activity relationships (SARs), quantitative structure-activity relationships (QSARs), and read-across. The main objective of the studies underlying this thesis is related to explore and refine the use of in silico tools in a risk assessment context of industrial chemicals. In particular, try to relate properties of the molecular structure to the toxic effect of the chemical substance, by using principles and methods of computational chemistry. The initial study was a survey of all industrial chemicals; the Industrial chemical map was created. A part of this map was identified including chemicals of potential concern. Secondly, the environmental pollutants, polychlorinated biphenyls (PCBs) were examined and in particular the non-dioxin-like PCBs (NDL-PCBs). A set of 20 NDL-PCBs was selected to represent the 178 PCB congeners with three to seven chlorine substituents. The selection procedure was a combined process including statistical molecular design for a representative selection and expert judgements to be able to include congeners of specific interest. The 20 selected congeners were tested in vitro in as much as 17 different assays. The data from the screening process was turned into interpretable toxicity profiles with multivariate methods, used for investigation of potential classes of NDL-PCBs. It was shown that NDL-PCBs cannot be treated as one group of substances with similar mechanisms of action. Two groups of congeners were identified. A group including in general lower chlorinated congeners with a higher degree of ortho substitution showed a higher potency in more assays (including all neurotoxic assays). A second group included abundant congeners with a similar toxic profile that might contribute to a common toxic burden. To investigate the structure-activity pattern of PCBs effect on DAT in rat striatal synaptosomes, ten additional congeners were selected and tested in vitro. NDL-PCBs were shown to be potent inhibitors of DAT binding. The congeners with highest DAT inhibiting potency were tetra- and penta-chlorinated with 2-3 chlorine atoms in ortho-position. The model was not able to distinguish the congeners with activities in the lower μM range, which could be explained by a relatively unspecific response for the lower ortho chlorinated PCBs. / Den europeiska kemikalielagstiftningen REACH har fastställt att kemikalier som produceras eller importeras i en mängd över 1 ton per år, måste registreras och riskbedömmas. En uppskattad siffra är att detta gäller för 30 000 kemikalier. Problemet är dock att data och information ofta är otillräcklig för en riskbedömning. Till stor del har djurförsök använts för effektdata, men djurförsök är både kostsamt och tidskrävande, dessutom kommer den etiska aspekten in. REACH har därför efterfrågat en undersökning av möjligheten att använda in silico verktyg för att bidra med efterfrågad data och information. In silico har en ungefärlig betydelse av i datorn, och innebär beräkningsmodeller och metoder som används för att få information om kemikaliers egenskaper och toxicitet. Avhandlingens syfte är att utforska möjligheten och förfina användningen av in silico verktyg för att skapa information för riskbedömning av industrikemikalier. Avhandlingen beskriver kvantitativa modeller framtagna med kemometriska metoder för att prediktera, dvs förutsäga specifika kemikaliers toxiska effekt. I den första studien (I) undersöktes 56 072 organiska industrikemikalier. Med multivariata metoder skapades en karta över industrikemikalierna som beskrev dess kemiska och fysikaliska egenskaper. Kartan användes för jämförelser med kända och potentiella miljöfarliga kemikalier. De mest kända miljöföroreningarna visade sig ha liknande principal egenskaper och grupperade i kartan. Genom att specialstudera den delen av kartan skulle man kunna identifiera fler potentiellt farliga kemiska substanser. I studie två till fyra (II-IV) specialstuderades miljögiftet PCB. Tjugo PCBs valdes ut så att de strukturellt och fysiokemiskt representerade de 178 PCB kongenerna med tre till sju klorsubstituenter. Den toxikologiska effekten hos dessa 20 PCBs undersöktes i 17 olika in vitro assays. De toxikologiska profilerna för de 20 testade kongenerna fastställdes, dvs vilka som har liknande skadliga effekter och vilka som skiljer sig åt. De toxicologiska profilerna användes för klassificering av PCBs. Kvantitativa modeller utvecklades för prediktioner, dvs att förutbestämma effekter hos ännu icke testade PCBs, och för att få ytterligare kunskap om strukturella egenskaper som ger icke önskvärda effekter i människa och natur. Information som kan användas vid en framtida riskbedömning av icke-dioxinlika PCBs. Den sista studien (IV) är en struktur-aktivitets studie som undersöker de icke-dioxinlika PCBernas hämmande effekt av signalsubstansen dopamin i hjärnan.

Human hair profiling is an integral part of a forensic investigation but it is one of the most technically difficult subjects in forensic science. This thesis describes the research and development of a novel approach for the rapid identification of unknown human and other related keratin fibres found at a crime scene. The work presented here is developed systematically and considers sample collection, sample preparation, analysis and interpretation of spectral data for the profiling of hair fibres encountered in criminal cases. Spectral comparison of fibres was facilitated with the use of chemometrics methods such as PCA, SIMCA and Fuzzy Clustering, and the less common approach of multi-criteria decision making methodology (MCDM). The aim of the thesis was to investigate the potential of some vibrational spectroscopy techniques for matching and discrimination of single keratin hair fibres in the context of forensic evidence. The first objective (chapter 3) of the thesis was to evaluate the use of Raman and FT-IR micro-spectroscopy techniques for the forensic sampling of hair fibres and to propose the preferred technique for future forensic hair comparisons. The selection of the preferred technique was based on criteria such as spectral quality, ease of use, rapid analysis and universal application to different hair samples. FT-IR micro-spectroscopy was found to be the most appropriate technique for hair analysis because it enabled the rapid collection of spectra from a wide variety of hair fibres. Raman micro-spectroscopy, on the other hand, was hindered with fluorescence problems and did not allow the collection of spectra from pigmented fibres. This objective has therefore shown that FT-IR micro-spectroscopy is the preferable spectroscopic technique for forensic analysis of hair fibres, whilst Raman spectroscopy is the least preferred. The second objective (chapter 3) was to investigate, through a series of experiments, the effect of chemical treatment on the micro-environment of human hair fibres. The effect of bleaching agents on the hair fibres was studied with some detail at different treatment times and the results indicate a significant change in the chemical environment of the secondary structure of the hair fibre along with changes in the C-C backbone structure. One of the most important outcomes of this research was the behaviour of the fÑ-helix during chemical treatment. The hydrogen bonding in the fÑ-helix provides for the stable structure of the fibre and therefore any disruption to the fÑ-helix will inevitably damage the molecular structure of the fibre. The results highlighted the behaviour of the fÑ-helix, which undergoes a significant decrease in content during oxidation, and is partly converted to a random-coil structure, whilst the fÒ-sheet component of the secondary structure remains unaffected. The reported investigations show that the combination of FT-IR and Raman micro-spectroscopy can provide an insight and understanding into the complex chemical properties and reactions within a treated hair fibre. Importantly, this work demonstrates that with the aid of chemometrics, it is possible to investigate simultaneously FT-IR and Raman micro-spectroscopic information from oxidised hair fibres collected from one subject and treated at different times. The discrimination and matching of hair fibres on the basis of treatment has potential forensic applications. The third objective (chapter 4) attempted to expand the forensic application of FT-IR micro-spectroscopy to other keratin fibres. Animal fibres are commonly encountered in crime scenes and it thus becomes important to establish the origin of those fibres. The aim of this work was to establish the forensic applications of FT-IR micro-spectroscopy to animal fibres and to investigate any fundamental molecular differences between these fibres. The results established a discrimination between fibres consisting predominantly of fÑ-helix and those containing mainly a fÒ-sheet structure. More importantly, it was demonstrated through curve-fitting and chemometrics, that each keratin fibre contains a characteristic secondary structure arrangement. The work presented here is the first detailed FT-IR micro-spectroscopic study, utilising chemometrics as well as MCDM methods, for a wide range of keratin fibres, which are commonly, found as forensic evidence. Furthermore, it was demonstrated with the aid of the rank ordering MCDM methods PROMETHEE and GAIA, that it is possible to rank and discriminate keratin fibres according to their molecular characteristics obtained from direct measurements together with information sourced from the literature. The final objective (chapter 5) of the thesis was to propose an alternative method for the discrimination and matching of single scalp human hair fibres through the use of FT-IR micro-spectroscopy and chemometrics. The work successfully demonstrated, through a number of case scenarios, the application of the technique for the identification of variables such as gender and race for an unknown single hair fibre. In addition, it was also illustrated that known hair fibres (from the suspect or victim) can be readily matched to the unknown hair fibres found at the crime scene. This is the first time that a substantial, systematic FT-IR study of forensic hair identification has been presented. The research has shown that it is possible to model and correlate individual¡¦s characteristics with hair properties at molecular level with the use of chemometrics methods. A number of different, important forensic variables of immediate use to police in a crime scene investigation such as gender, race, treatment, black and white hair fibres were investigated. Blind samples were successfully applied both to validate available experimental data and extend the current database of experimental determinations. Protocols were posed for the application of this methodology in the future. The proposed FT-IR methodology presented in this thesis has provided an alternative approach to the characterisation of single scalp human hair fibres. The technique enables the rapid collection of spectra, followed by the objective analytical capabilities of chemometrics to successfully discriminate animal fibres, human hair fibres from different sources, treated from untreated hair fibres, as well as black and white hair fibres, on the basis of their molecular structure. The results can be readily produced and explained in the courts of law. Although the proposed relatively fast FT-IR technique is not aimed at displacing the two slower existing methods of hair analysis, namely comparative optical microscopy and DNA analysis, it has given a new dimension to the characterisation of hair fibres at a molecular level, providing a powerful tool for forensic investigations.

Les maladies neurodégénératives représentent un défi sociétal majeur. Trouver des outils pour mieux comprendre et diagnostiquer ces maladies est donc nécessaire. La spectroscopie infrarouge (IR) et Raman semblent être de bons candidats puisqu’elles peuvent caractériser l’état physiopathologique d’un échantillon. Le but de cette thèse a été d’appliquer ces méthodes à l’étude de peptides modèles, de tissus cérébraux et de liquides céphalorachidiens (LCR). Dans le cadre de l’étude des tissus cérébraux, la spectroscopie IR et Raman ont été couplées à la microscopie afin de combiner des informations spectrales et spatiales. Cela a permis de mieux comprendre la formation et le rôle des plaques amyloïdes dans la maladie d’Alzheimer (MA). Egalement, cela a permis de montrer l’intérêt d’utiliser ces méthodes dans des études futures pour suivre l’effet de différents traitements contre la sclérose en plaques. Concernant l’étude des LCR, la spectroscopie IR en mode ATR et la spectroscopie Raman exaltée de surface ont été utilisées afin de mettre en évidence des marqueurs spectroscopiques de la MA et de la maladie à corps de Lewy qui pourraient permettre un diagnostic plus précoce de ces maladies et un diagnostic différentiel entre ces deux. / Neurodegenerative diseases represent a major societal challenge. So, it is necessary to develop new tools for a better understanding and diagnosing of these diseases. Infrared (IR) and Raman spectroscopies seem to be good candidates since they can characterize the physiopathological conditions of a biological sample. The purpose of this thesis was to apply these methods to the study of model peptides, brain tissues and cerebrospinal fluids (CSF). As a part of brain tissue analysis, IR and Raman spectroscopy were coupled to microscopy in order to combine spectral and spatial information. This methodology improved our understanding of the formation and the role of amyloid plaques in Alzheimer’s disease (AD). Moreover, it allowed to demonstrate the potential of these approaches in future studies on the effect of various treatments against multiple sclerosis. Concerning the study of CSF, IR-ATR and surface enhanced Raman spectroscopy were applied to identify spectroscopic markers of AD and Lewy body disease that could enable early diagnosis of these diseases and discrimination between them.