Molecular Characterisation of Fluidised Catalytic Cracker Feedstocks using Ruthenium Tetroxide Oxidation: a Study of Model Hydrocarbons

The world's proven reserves of crude oil will be depleted in 42 years at the current rate of consumption. Oil refiners are under considerable economic and environmental pressure to improve the efficiency of refining and the quality and definition of products. Statistical and fundamental models are extensively used to more accurately model the important refinery processes such as Fluidised Catalytic Cracking (FCC). A major problem with the fundamental approach is that FCC feedstocks are by definition heavy petroleum fractions, and as such constitute highly complex mixtures of aromatic and aliphatic hydrocarbons. Gas chromatography (GC) analysis of heavy petroleum fractions reveals a broad 'hump' of unresolved compounds termed an Unresolved Complex Mixture (UCM) of hydrocarbons. Conventional instrumental techniques alone are unable to elucidate the composition of UCMs, they are simply too complex. Oxidative degradation of UCMs has already been used with some success to selectively oxidise aliphatic and aromatic UCMs to reveal some of the structures incorporated in UCMs from various natural and anthropogenic sources. Ruthenium tetroxide (RUO4) attacks aromatic rings at the ipso-carbon of aromatic moieties. Unsubstituted aromatic carbon is oxidised to CO2 whereas substituents are preserved as carboxylic acids. "Retro-structural analysis" involves reconstruction of the products of oxidation to reveal the original molecule or 'average' molecule. However, previous studies have highlighted problems with the recovery of products from die oxidation of hydroaromatic compounds. Hydroaromatic compounds contain an alicyclic ring attached to an aromatic ring e.g. tetralin. This study presents evidence that (theoretically) data from RUO4 oxidation FCC feedstocks can make a significant improvement to the accuracy of FCC modelling at BP Amoco. RUO4 oxidation and work-up procedures were developed further in an attempt to overcome problems with 'losses' of oxidation products from hydroaromatic compounds, including an improved carbon dioxide trap. Several novel hydroaromatic compounds and a diaromatic compound proposed in a previous study as being 'average' UCM components were synthesised and fully characterised by GC, GCMS, FTIR and NMR spectroscopy. The compounds synthesised were 6-cyclohexyltetralin, l-(3'- methylbutyl)7-cyclohexyltetralin, 1-n-nonyl-7-cyclohexyltetralin and 1-/n-nonyl-7- cyclohexylnaphthalene. RUO4 oxidation of the synthetic compounds and commercial tetralin revealed that while losses of between 70 and 50% of the expected water soluble dicarboxylic acids are observed, these losses can be at least partially accounted for by the 'over oxidation' of carboxylic acids to produce smaller carboxylic acids. For example, the RUO4 oxidation of tetralin produces 1,6-hexanedioic acid as a major product but significant amounts of 1,5-pentanedioic acid is observed along with trace amounts of 1,4- butanedioic acid. Smaller acids are likely to be undetected or lost as butyl esters during the work-up. Where 2-n-nonyl-1,6-hexanedioic acid was produced, decanoic and nonanoic acid as well as 1,5- pentanedioic acid and 1,4-butanedioic acid were observed corresponding to oxidation of the 2- position on the dicarboxylic acid. The three major products from RUO4 oxidation of ln-nonyl-7-cyclohexylnaphthalene were partially oxidised compounds including 2-(l-oxo-n-decane)-4-cyclohexylbenzoic acid, showing that the oxidation of diaromatic compounds in UCMs gives more complex oxidation products. This is consistent with previous studies where diaromatic UCMs were oxidised to give a more complex 'oxidised UCM' rather than simple carboxylic acids. The observation of monocarboxylic acids in oxidation products from the alicyclic portion of a hydroaromatic compound has not previously been reported. This represents a new source of monocarboxylic acids in the RUO4 oxidation products of UCMs and should be taken into account when oxidising UCMs likely to contain a significant proportion of hydroaromatic structures, such as hydrotreated FCC feedstocks. The synthesis and oxidation of di-substituted teiralins has increased the understanding of RuG4 oxidation products from UCMs and consequently furthered the use of RUO4 as a potentially useftjl tool in the elucidation of FCC feedstock compositions and other aromatic UCMs

Data exploration and knowledge extraction: their application to the study of endocrine disrupting chemicals

Interest in computer-aided methods for investigating the biological field has increased significantly. One method is Quantitative Structure-Activity Relationships (QSAR), a valuable technique for predicting the effects of a substance from its chemical structure. A challenging application of QSAR is in characterizing the (bio)activity profiles of chemicals. Endocrine disrupters (EDs) are exogenous substances interfering with the function of the endocrine system and represent an interesting field of application for in silico methods. EDs targets include nuclear receptors, particularly effects mediated by the oestrogen receptor (ER). They are also mentioned as substances requiring a more detailed control and specific authorisation within REACH, the new European legislation on chemicals. QSAR represents a challenging method to approach data gap about EDs since REACH substantially boosted interest on computational chemistry to replace experimental testing. This work: aimed to explore the status, availability and reliability of non-testing methods applied to endocrine disruption via oestrogen receptors and eventually to propose new models easily exploitable in regulatory contexts. The work evaluated existing QSAR models present in literature to assess their validity on the basis of the OECD principles for QSAR validation. Different kinds of models have been analysed and they were externally validated with new data found in the literature. Furthermore, new QSAR binary classifiers have been developed using different data mining techniques (e.g.: classification trees, fuzzy logic, neural networks) based on a very large and heterogeneous dataset of chemical compounds. The focus was given to both binding (RBA) and transcriptional activity (RA) better to characterize the effects of EDs. A possible combination of the models was also explored. A very good accuracy was achieved for both RA and RBA (>85%). These models can be a valuable complement to in vivo and in vitro studies in the toxicological characterisation of chemical compounds

Systems and chemical biology approaches to study cell function and response to toxins

Toxicity is one of the main causes of failure during drug discovery, and of withdrawal once drugs reached the market. Prediction of potential toxicities in the early stage of drug development has thus become of great interest to reduce such costly failures. Since toxicity results from chemical perturbation of biological systems, we combined biological and chemical strategies to help understand and ultimately predict drug toxicities. First, we proposed a systematic strategy to predict and understand the mechanistic interpretation of drug toxicities based on chemical fragments. Fragments frequently found in chemicals with certain toxicities were defined as structural alerts for use in prediction. Some of the predictions were supported with mechanistic interpretation by integrating fragmentchemical, chemical-protein, protein-protein interactions and gene expression data. Next, we systematically deciphered the mechanisms of drug actions and toxicities by analyzing the associations of drugs’ chemical features, biological features and their gene expression profiles from the TG-GATEs database. We found that in vivo (rat liver) and in vitro (rat hepatocyte) gene expression patterns were poorly overlapped and gene expression responses in different species (rat and human) and different tissues (liver and kidney) varied widely. Eventually, for further understanding of individual differences in drug responses, we reviewed how genetic polymorphisms influence the individual's susceptibility to drug toxicity by deriving chemical-protein interactions and SNP variations from Mechismo database. Such a study is also essential for personalized medicine. Overall, this study showed that, integrating chemical and biological in addition to genetic data can help assess and predict drug toxicity at system and population levels

Bioinformatics Analysis and Modelling of Therapeutically Relevant Molecules

Ph.DDOCTOR OF PHILOSOPH

Protein function and inhibitor prediction by statistical learning approach

Ph.DDOCTOR OF PHILOSOPH

The prediction of mutagenicity and pKa for pharmaceutically relevant compounds using 'quantum chemical topology' descriptors

Quantum Chemical Topology (QCT) descriptors, calculated from ab initio wave functions, have been utilised to model pKa and mutagenicity for data sets of pharmaceutically relevant compounds. The pKa of a compound is a pivotal property in both life science and chemistry since the propensity of a compound to donate or accept a proton is fundamental to understanding chemical and biological processes. The prediction of mutagenicity, specifically as determined by the Ames test, is important to aid medicinal chemists select compounds avoiding this potential pitfall in drug design. Carbocyclic and heterocyclic aromatic amines were chosen because this compounds class is synthetically very useful but also prone to positive outcomes in the battery of genotoxicity assays.The importance of pKa and genotoxic characteristics cannot be overestimated in drug design, where the multivariate optimisations of properties that influence the Absorption-Distribution-Metabolism-Excretion-Toxicity (ADMET) profiles now features very early on in the drug discovery process.Models were constructed using carboxylic acids in conjunction with the Quantum Topological Molecular Similarity (QTMS) method. The models produced Root Mean Square Error of Prediction (RMSEP) values of less than 0.5 pKa units and compared favourably to other pKa prediction methods. The ortho-substituted benzoic acids had the largest RMSEP which was significantly improved by splitting the compounds into high-correlation subsets. For these subsets, single-term equations containing one ab initio bond length were able to accurately predict pKa. The pKa prediction equations were extended to phenols and anilines.Quantitative Structure Activity Relationship (QSAR) models of acceptable quality were built based on literature data to predict the mutagenic potency (LogMP) of carbo- and heterocyclic aromatic amines using QTMS. However, these models failed to predict Ames test values for compounds screened at GSK. Contradictory internal and external data for several compounds motivated us to determine the fidelity of the Ames test for this compound class. The systematic investigation involved recrystallisation to purify compounds, analytical methods to measure the purity and finally comparative Ames testing. Unexpectedly, the Ames test results were very reproducible when 14 representative repurified molecules were tested as the freebase and the hydrochloride salt in two different solvents (water and DMSO). This work formed the basis for the analysis of Ames data at GSK and a systematic Ames testing programme for aromatic amines. So far, an unprecedentedly large list of 400 compounds has been made available to guide medicinal chemists. We constructed a model for the subset of 100 meta-/para-substituted anilines that could predict 70% of the Ames classifications. The experimental values of several of the model outliers appeared questionable after closer inspection and three of these have been retested so far. The retests lead to the reclassification of two of them and thereby to improved model accuracy of 78%. This demonstrates the power of the iterative process of model building, critical analysis of experimental data, retesting outliers and rebuilding the model.EThOS - Electronic Theses Online ServiceEPSRCGlaxoSmithKlineGBUnited Kingdo

Study of the aryl hydrocarbon receptor mediated effects through in silico modeling and in vitro bioassays

The aryl hydrocarbon receptor (AhR) is a cytoplasmatic sensor of diverse endogenous and exogenous substances. In a toxicological context, the former known as “dioxin receptor” has been investigated as a xenobiotic chemoreceptor and due to its roles in mediating carcinogenesis, endocrine disruption, among other immunological, hepatic, cardiovascular, and dermal toxicity mechanisms. The deep physiological implications of AhR in cellular proliferation, adhesion, division, differentiation, as well as in the reproductive, immunological and cardiovascular homeostasis have opened a new field of research in order to harness AhR’s pharmacological potential. Hence, AhR has become a therapeutic target of inflammatory, infectious, malignant, and immunological conditions. Toxicological and pharmacological fields could benefit from discovering novel AhR modulators to elucidate further on the chemical-biological implications of this crucial transcription factor. In this Thesis, the following objective was proposed in order to contribute to such understanding. General Objective: Evaluate diverse chemical compounds as modulators of AhR by means of in silico and in vitro methods. The general objective was concretized in specific aims distributed in the five Chapters of this Thesis as follow: Chapter 1. Review the literature on AhR mediated effects and the existing theoretical and experimental methods employed to study the structural and functional aspects of the receptor. Chapter 2. Develop and experimentally validate QSAR models to predict the AhR agonist activity of chemical compounds. Chapter 3. Analyze the dual effects of a set of benzothiazoles as AhR modulators and antimicrobial agents. Chapter 4. Evaluate a novel set of triarylmethane compounds as AhR ligands. Chapter 5. Study the AhR antagonism discovered in potentially harmful substances using computational methods.The aryl hydrocarbon receptor (AhR) is a cytoplasmatic sensor of diverse endogenous and exogenous substances. In a toxicological context, the former known as “dioxin receptor” has been investigated as a xenobiotic chemoreceptor and due to its roles in mediating carcinogenesis, endocrine disruption, among other immunological, hepatic, cardiovascular, and dermal toxicity mechanisms. The deep physiological implications of AhR in cellular proliferation, adhesion, division, differentiation, as well as in the reproductive, immunological and cardiovascular homeostasis have opened a new field of research in order to harness AhR’s pharmacological potential. Hence, AhR has become a therapeutic target of inflammatory, infectious, malignant, and immunological conditions. Toxicological and pharmacological fields could benefit from discovering novel AhR modulators to elucidate further on the chemical-biological implications of this crucial transcription factor. In this Thesis, the following objective was proposed in order to contribute to such understanding. General Objective: Evaluate diverse chemical compounds as modulators of AhR by means of in silico and in vitro methods. The general objective was concretized in specific aims distributed in the five Chapters of this Thesis as follow: Chapter 1. Review the literature on AhR mediated effects and the existing theoretical and experimental methods employed to study the structural and functional aspects of the receptor. Chapter 2. Develop and experimentally validate QSAR models to predict the AhR agonist activity of chemical compounds. Chapter 3. Analyze the dual effects of a set of benzothiazoles as AhR modulators and antimicrobial agents. Chapter 4. Evaluate a novel set of triarylmethane compounds as AhR ligands. Chapter 5. Study the AhR antagonism discovered in potentially harmful substances using computational methods

Development of nanomaterial based sensors for the detection of explosives

Detection of low levels of illicit materials, such as explosives, is a key challenge for security and environmental monitoring. Recent advances in highly sensitive molecular-recognition techniques utilising nanomaterials may pro- vide a wealth of useful tools for this purpose. In this thesis two classes of nanomaterials are applied to explosives sensing. The first is a range of novel gold nanoparticles, produced via the facile reduction of chloroauric acid with mono- and di-ketones. The mechanism of this reaction and the resultant particles are characterised with spectroscopy and tunnelling electron microscopy. Several different sizes of gold colloid were created, but most interesting was the creation of gold nanostars, which have potential as a substrate for surface-enhanced Raman spectroscopy. The second nanomaterial-based sensor is a quantum dot array featuring supramolecular receptors for small-molecule explosive detection. By combining array elements into a single, multichannel platform; faster results can be obtained from smaller amounts of sample. The ability of quantum dots to act as luminescent probes in a multichannel array, due to their sharp, variable emissions from a single excitation wavelength, was exploited to detect five explosives - 2,4-dinitrotoluene (DNT), 2,4,6-trinitrotoluene (TNT), tetryl (2,4,6-trinitrophenylmethylnitramine), cyclotrimethylenetrinitramine (RDX) and pentaerythritol tetranitrate (PETN). To create the array, each different colour quantum dot was functionalised with a different cavitand, aromatic or nucleophilic-heteroatom based receptor via a facile photoligation process. These receptors undergo supramolecular interactions with the explosives, inducing variable fluorescence quenching of the quantum dots. Pattern analysis of the fluorescence quenching data allowed for explosive detection and identification with limits-of-detection of < 1 part-per-million. Finally, the development of the quantum dot based sensors from solution phase to solid phase is examined, with the aim of creating point-of- test devices for use in the field. A key outcome was the development of supramolecular organogel/nanoparticle hybrid “smart” materials for sensing applications