The aim of this work was to assess the suitability of vibrational spectroscopic techniques (Raman, FT-IR and FT-NIR spectroscopy) as a means for the solid-state structural analysis of pharmaceuticals. Budesonide, fluticasone propionate, salbutamol hemisulfate, terbutaline hemisulfate, ipratropium bromide, polymorphic forms of salmeterol xinafoate and two polymorphic forms of sulfathiazole were selected since they are used in the management of certain respiratory disorders and from different chemical and pharmacological entities along with some pharmaceutical excipients. Conventional visual examination is not sufficient to identify and differentiate spectra between different pharmaceuticals. To confirm the assignment of key molecular vibrational band signatures, quantum chemical calculations of the vibrational spectra were employed for better understanding of the first five selected drugs. The nondestructive nature of the vibrational spectroscopic techniques and the success of quantum chemical calculations demonstrated in this work have indeed offered a new dimension for the rapid identification and characterisation of pharmaceuticals and essentially warrant further research. The application of simultaneous in situ Raman spectroscopy and differential scanning calorimetry for the preliminary investigation of the polymorphic transformation of salmeterol xinafoate polymorphs and two polymorphic forms of sulfathiazole has also been explored in this work leading to the development of a new method for the solid-state estimation of the transition temperature of entantiotropically related pharmaceutical polymorphs which represents the first analytical record of the use of this approach for pharmaceutical polymorphs.

Force fields have been an integral part of computational chemistry for decades, providing invaluable insight and facilitating the better understanding of biomolecular system behaviour. Despite the many benefits of a force field, there continue to be deficiencies as a result of the classical architecture they are based upon. Some deficiencies, such as a point charge electrostatic description instead of a multipole moment description, have been addressed over time, permitted by the ever-increasing computational power available. However, whilst incorporating such significant improvements has improved force field accuracy, many still fail to describe several chemical effects including polarisation, non-covalent interactions and secondary/tertiary structural effects. Furthermore, force fields often fail to provide consistency when compared with other force fields. In other words, no force field is reliably performing more accurately than others, when applied to a variety of related problems. The work presented herein develops a next-generation force field entitled FFLUX, which features a novel architecture very different to any other force field. FFLUX is designed to capture the relationship between geometry and energy through a machine learning method known as kriging. Instead of a series of parameterised potentials, FFLUX uses a collection of atomic energy kriging models to make energy predictions. The energies describing atoms within FFLUX are obtained from the Interacting Quantum Atoms (IQA) energy partitioning approach, which in turn derives the energies from the electron density and nuclear charges of topological atoms described by Quantum Chemical Topology (QCT). IQA energies are shown to provide a unique insight into the relationship between geometry and energy, allowing the identification of explicit atoms and energies contributing towards torsional barriers within various systems. The IQA energies can be modelled to within 2.6% accuracy, as shown for a series of small systems including weakly bound complexes. The energies also allow an interpretation of how an atom feels its surrounding environment through intra-atomic, covalent and electrostatic energetic descriptions, which typically are seen to converge within a ~7 - 8 A horizon radius around an atom or small system. These energy convergence results are particularly relevant to tackling the transferability theme within force field development. Where energies are seen to converge, a proximity limit on the geometrical description needed for a transferable energy model is defined. Finally, the FFLUX force field is validated through successfully optimising distorted geometries of a series of small molecules, to near-ab initio accuracy.

Removal of polynuclear aromatics from diesel fuel has become a focus of intense research due to the stringent environmental legislation associated with clean fuels. In this work, selective ring opening of model compound decalin over the set of catalysts comprising of 1) Ir-Pt supported on mesoporous Zr-MCM-41, large and medium pore zeolites like HY and H-Beta and 2) Ni-Mo/carbide on HY, H-Beta, Al-SBA-15, ¥ã- alumina and silica alumina were studied. All the catalysts were extensively characterized by BET surface area measurement, CO-chemisorption, XRD, FTIR, TPR and TPD of ammonia. Ring opening of decalin was studied on these catalysts in a trickle-bed reactor in a temperature range of 200- 400 ¡ÆC, pressure range of 2-7 MPa and LHSV of 1 to 3 h- 1. 31.7 and 65.0 wt.% of RO yield and selectivity were observed on Ir-Pt/HY catalyst at 220 ¡ÆC, whereas 34.0 and 40.0 wt.% of ring opening yield and selectivity were observed on Ni-Mo carbide/HY catalyst at 240 ¡ÆC. From the model compound studies, Ir-Pt/HY, Ni-Mo carbide/HY and Ni-Mo carbide/H-Beta were selected for study of hydrotreated light gas oil in a trickle bed reactor. Ni-Mo carbide/HY performed better over other catalysts and increased the cetane index of hydrotreated light gas oil by 12 units at 325 ¡ÆC. A first order kinetic model was fitted for the hydrotreated light gas oil study. 89, 111 and 42 KJ/gmol of activation energies was observed for dearomatization, aromatization and naphthenes cracking steps, respectively. The thermodynamic equilibrium calculations reveal that the selectivity of ring opening products of decalin can be maximized by favoring the formation of unsaturated compounds at higher operating temperatures. Energetics of dealkylation and ring opening reactions of naphthenes in gas phase and on the surface of Br©ªnsted acid sites were calculated using quantum chemical simulations. In iv gas phase, ratio of Arrhenius activation energies for forward and reverse reactions of RO and dealkylation reactions are 1.92 and 1.82 respectively. Deakylation on different level clusters revealed that surface reaction is the rate controlling.

Removal of polynuclear aromatics from diesel fuel has become a focus of intense research due to the stringent environmental legislation associated with clean fuels. In this work, selective ring opening of model compound decalin over the set of catalysts comprising of 1) Ir-Pt supported on mesoporous Zr-MCM-41, large and medium pore zeolites like HY and H-Beta and 2) Ni-Mo/carbide on HY, H-Beta, Al-SBA-15, ¥ã- alumina and silica alumina were studied. All the catalysts were extensively characterized by BET surface area measurement, CO-chemisorption, XRD, FTIR, TPR and TPD of ammonia. Ring opening of decalin was studied on these catalysts in a trickle-bed reactor in a temperature range of 200- 400 ¡ÆC, pressure range of 2-7 MPa and LHSV of 1 to 3 h- 1. 31.7 and 65.0 wt.% of RO yield and selectivity were observed on Ir-Pt/HY catalyst at 220 ¡ÆC, whereas 34.0 and 40.0 wt.% of ring opening yield and selectivity were observed on Ni-Mo carbide/HY catalyst at 240 ¡ÆC. From the model compound studies, Ir-Pt/HY, Ni-Mo carbide/HY and Ni-Mo carbide/H-Beta were selected for study of hydrotreated light gas oil in a trickle bed reactor. Ni-Mo carbide/HY performed better over other catalysts and increased the cetane index of hydrotreated light gas oil by 12 units at 325 ¡ÆC. A first order kinetic model was fitted for the hydrotreated light gas oil study. 89, 111 and 42 KJ/gmol of activation energies was observed for dearomatization, aromatization and naphthenes cracking steps, respectively. The thermodynamic equilibrium calculations reveal that the selectivity of ring opening products of decalin can be maximized by favoring the formation of unsaturated compounds at higher operating temperatures. Energetics of dealkylation and ring opening reactions of naphthenes in gas phase and on the surface of Br©ªnsted acid sites were calculated using quantum chemical simulations. In iv gas phase, ratio of Arrhenius activation energies for forward and reverse reactions of RO and dealkylation reactions are 1.92 and 1.82 respectively. Deakylation on different level clusters revealed that surface reaction is the rate controlling.

No description available.

Glass materials surround us, impacting our lives on a daily basis, whether geologically deposited by volcanic activity or synthesized in large volume by industry. These amorphous oxide materials are vastly important due to their variety of applications including solid electrolytes, cookware, and storage of high-level nuclear waste. Although they are used for different applications, one common characteristic of these materials is the absence of long-range periodic order. This makes it difficult to use traditional solid-state characterization methods such as x-ray and neutron diffraction to study glass structure. Nuclear magnetic resonance (NMR), is ideally suited to study materials that exhibit short-range non-periodic order as it probes directly at a nucleus of interest and is sensitive to its local structural environment. This ability of solid-state NMR is illustrated by revealing local structural features in various oxide materials presented in this thesis. Within is a compilation of studies looking at basic borates, followed by borovanadates and complex borosilicate glasses. A multinuclear application of using quantum chemical calculations, single and double resonance methods and charge-balance models are discussed to deconvolute the complex structures of these disordered materials. This is followed by a study of a difficult low-gamma nucleus, 73Ge, (once considered “impossible” for solid-state NMR) which is explored for future material studies by looking at 73Ge NMR of crystalline and glassy germanates. 73Ge chemical shifts were related to coordination environments and quadrupolar coupling constants were related to bond length distortions.

Metal-hydrocarbon complexes were prepared in a laser-vaporization molecular beam source and studied by single-photon zero electron kinetic energy (ZEKE) and mass-analyzed threshold ionization (MATI) spectroscopy. The ionization energies and vibrational frequencies of the metal complexes were measured from the ZEKE and MATI spectra. Metal-ligand bonding and low-lying electronic states of the neutral and ionized complexes were analyzed by combining the spectroscopic measurements with quantum chemical calculations and spectral simulations. In this dissertation, the metal complexes of mesitylene, aniline, cyclooctatetraene, benzene, ethene, and propene were studied. For each complex, different effects from metal coordination have been identified. Although metal-bis(mesitylene) sandwich complexes may adopt eclipsed and staggered conformations, the group VI metal-bis(mesitylene) complexes are determined to be in the eclipsed form. In this form, rotational conformers with the methyl group dihedral angles of 0 and 60° are identified for the Cr complex, whereas the 0° rotamer is observed for the Mo and W species. The unsuccessful observation of the 60° rotamer for the Mo and W complexes is the result of its complete conversion to the 0° rotamer in both He and He/Ar carriers. For group III metal aniline complexes, the ZEKE spectrum of each complex exhibits a strong origin band, a short M+-aniline stretching progression, and several low-frequency ligand based vibrational modes. The intensities of most of the transitions can be explained by the Franck-Condon (FC) principle within the harmonic approximation. However, the intensity of the low frequency out-of-plane ring deformation mode is greatly overestimated by the FC calculations and may be caused by the anharmonic nature of the mode. Although aniline offers two possible binding modes for the metal atoms, a п binding mode is identified with the metal atom over the phenyl ring. For Ce, Pr, and Nd(cyclooctatetraene) complexes multiple band systems are observed. This is assigned to the ionization of several low-lying electronic states of the neutral complex. This observation is different from the Gd(cyclooctatetraene) complex, for which a single band system is observed. The presence of the multiple low-energy electronic states is caused by the splitting of the partially filled lanthanide 4f orbitals in the ligand field. The ZEKE spectrum of the Gd(benzene) complex exhibits a strong origin band, whereas the spectrum of Lu(benzene) displays a weak one. The benzene ring is planar in the Gd complex, but bent in the Lu complex. Dehydrogenation and C-C coupling products are observed in the reaction of La atom and ethene/propene. For the La and ethene reaction, La(C2H2) and La(C4H6) complexes are identified. With propene, C-H bond activation leads to the formation of the La(C3H4) and H-La(C3H5) complexes, whereas the C-C coupling yields the La(trimethylenemethane) complex. In addition, the La(CHCCH3) and La(CHCHCH2) isomers of La(C3H4) are observed, which are produced by the 1,2- and 1,3-hydrogen elimination of propene.

Glass materials surround us, impacting our lives on a daily basis, whether geologically deposited by volcanic activity or synthesized in large volume by industry. These amorphous oxide materials are vastly important due to their variety of applications including solid electrolytes, cookware, and storage of high-level nuclear waste. Although they are used for different applications, one common characteristic of these materials is the absence of long-range periodic order. This makes it difficult to use traditional solid-state characterization methods such as x-ray and neutron diffraction to study glass structure. Nuclear magnetic resonance (NMR), is ideally suited to study materials that exhibit short-range non-periodic order as it probes directly at a nucleus of interest and is sensitive to its local structural environment. This ability of solid-state NMR is illustrated by revealing local structural features in various oxide materials presented in this thesis. Within is a compilation of studies looking at basic borates, followed by borovanadates and complex borosilicate glasses. A multinuclear application of using quantum chemical calculations, single and double resonance methods and charge-balance models are discussed to deconvolute the complex structures of these disordered materials. This is followed by a study of a difficult low-gamma nucleus, 73Ge, (once considered “impossible” for solid-state NMR) which is explored for future material studies by looking at 73Ge NMR of crystalline and glassy germanates. 73Ge chemical shifts were related to coordination environments and quadrupolar coupling constants were related to bond length distortions.

Computational methods have been extensively developed at various levels of approximation in recent years to model biomolecular interactions and for rational drug design. This research work aims to explore the feasibility of using quantum mechanical (QM) methods within the two broad categories of in silico ligand-based and structure-based drug design. First, density functional theory at the M06L level of theory was employed to examine structure-activity relationships of boron-based heterocyclic compounds, anti-inflammatory inhibitors targetting the interleukin-1β (IL-1β) cytokine. Our findings from computed energies and shapes of the molecular orbitals provide understanding of electronic effects associated with the inhibitory activity. We also found that the boron atom, specifically its electrostatic polarity, appears to be essential for the anti-IL-1β activity as evidenced by the biological assay of non-boron analogues selected from the ligand-based virtual screening results. Secondly, we aimed to dock ligands at the active sites of zinc-containing metalloproteins with reasonable computational cost and with accuracy. Therefore, an in-house docking scheme based on a Monte Carlo sampling algorithm using the semiempirical PM6/AMBER force field scoring function was compiled for the first time within the Gaussian 09 program. It was applied to four test cases, docking to cytidine deaminase and human carbonic anhydrase II proteins. The docking results show the method’s promise in resolving false-positive docking poses and improving the predicted binding modes over a conventional docking scheme. Finally, semiempirical QM methods which include dispersion and hydrogen-bond corrections were assessed for modelling conformations of β-cyclodextrin (βCD) and their adsorption on graphene. The closed in vacuo βCD cccw conformer was found to be in the lowest energy, in good agreement with previous ab initio QM studies. DFTB3, PM6-DH2 and PM7 methods were applied to model the intermolecular interactions of large βCD/graphene complexes, over a thousand atoms in size. We found that the binding preference of βCD on graphene is in a closed conformation via its C2C3 rim, agreeing with reported experimental and computational findings.

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.