<p>CYP2C9 is one of our major drug metabolising enzymes and belongs to the cytochrome P450 (CYP) super family. The aim of this thesis was to gain an understanding of the quantitative structure–activity relationships (QSAR) of CYP2C9 substrates and inhibitors. This information will be useful in predicting drug metabolism and the potential for drug–drug interactions. To achieve this, a well characterised data set of structurally diverse, competitive CYP2C9 inhibitors was identified in our laboratory. Several computational methodologies, many based on GRID molecular interaction fields, were applied or developed in order to handle issues such as compound alignment and bioactive conformer selection. First, a traditional 3D QSAR was carried out in GOLPE, generating a predictive model. In this model the selection of a bioactive conformer and alignment was based on docking in a homology model of CYP2C9. Secondly, we introduced the concept of alignment independent descriptors from ALMOND. These descriptors were used to generate quantitatively and qualitatively predictive models. We subsequently derived conformation independent descriptors from molecular interaction fields calculated in FlexGRID. This enabled the derivation of 3D QSAR models without taking into account the selection of an alignment or a bioactive conformer. A subsequent programming effort enabled the conversion of this model back to 3D aligned pharmacophores. Similar alignment independent descriptors were also used in the development of the software MetaSite® that predicts the site of metabolism for CYP2C9 ligands. Finally, as crystal information on this isoform emerged, the performance of molecular dynamics simulations and homology models and the flexibility of the protein were evaluated using statistical analyses.</p><p>These modelling efforts have resulted in detailed knowledge of the structural characteristics in ligand interactions with the cytochrome P450 2C9 isoform.</p>

CYP2C9 is one of our major drug metabolising enzymes and belongs to the cytochrome P450 (CYP) super family. The aim of this thesis was to gain an understanding of the quantitative structure–activity relationships (QSAR) of CYP2C9 substrates and inhibitors. This information will be useful in predicting drug metabolism and the potential for drug–drug interactions. To achieve this, a well characterised data set of structurally diverse, competitive CYP2C9 inhibitors was identified in our laboratory. Several computational methodologies, many based on GRID molecular interaction fields, were applied or developed in order to handle issues such as compound alignment and bioactive conformer selection. First, a traditional 3D QSAR was carried out in GOLPE, generating a predictive model. In this model the selection of a bioactive conformer and alignment was based on docking in a homology model of CYP2C9. Secondly, we introduced the concept of alignment independent descriptors from ALMOND. These descriptors were used to generate quantitatively and qualitatively predictive models. We subsequently derived conformation independent descriptors from molecular interaction fields calculated in FlexGRID. This enabled the derivation of 3D QSAR models without taking into account the selection of an alignment or a bioactive conformer. A subsequent programming effort enabled the conversion of this model back to 3D aligned pharmacophores. Similar alignment independent descriptors were also used in the development of the software MetaSite® that predicts the site of metabolism for CYP2C9 ligands. Finally, as crystal information on this isoform emerged, the performance of molecular dynamics simulations and homology models and the flexibility of the protein were evaluated using statistical analyses. These modelling efforts have resulted in detailed knowledge of the structural characteristics in ligand interactions with the cytochrome P450 2C9 isoform.

Philosophiae Doctor - PhD / Given the significant disease burden caused by human coronaviruses, the discovery of an effective antiviral strategy is paramount, however there is still no effective therapy to combat infection. This thesis details the in silica exploration of ligand libraries to identify candidate lead compounds that, based on multiple criteria, have a high probability of inhibiting the 3 chymotrypsin-like protease (3CUro) of human coronaviruses. Atomistic models of the 3CUro were obtained from the Protein Data Bank or theoretical models were successfully generated by homology modelling. These structures served the basis of both structure- and ligand-based drug design studies. Consensus molecular docking and pharmacophore modelling protocols were adapted to explore the ZINC Drugs-Now dataset in a high throughput virtual screening strategy to identify ligands which computationally bound to the active site of the 3CUro . Molecular dynamics was further utilized to confirm the binding mode and interactions observed in the static structure- and ligand-based techniques were correct via analysis of various parameters in a IOns simulation. Molecular docking and pharmacophore models identified a total of 19 ligands which displayed the potential to computationally bind to all 3CUro included in the study. Strategies employed to identify these lead compounds also indicated that a known inhibitor of the SARS-Co V 3CUro also has potential as a broad spectrum lead compound. Further analysis by molecular dynamic simulations largely confirmed the binding mode and ligand orientations identified by the former techniques. The comprehensive approach used in this study improves the probability of identifying experimental actives and represents a cost effective pipeline for the often expensive and time consuming process of lead discovery. These identified lead compounds represent an ideal starting point for assays to confirm in vitro activity, where experimentally confirmed actives will be proceeded to subsequent studies on lead optimization.