The binding of a drug or other ligand to plasma proteins can effect their absorption, metabolism and excretion which can lead to a change in its toxicity and therapeutic action. Fluorescence is a technique that has been used to study such interactions and has the advantages of extreme sensitivity and specificity. Previously fluorescence has been monitored in the UV /vis range of the spectrum. However, a new development is long wavelength fluorescence (600-1000nm), which has the added benefits of a lower background, decreased scattering, decreased photodecomposition and the availability of inexpensive, solid state, optical components. Certain dyes including polymethines, xanthenes and phenoxazines that fluoresce in the long wavelength region (600-1000nm) of the spectrum were investigated for use as fluorescence probes. Nile Red, a strongly hydrophobic phenoxazine dye, was found to have an emission wavelength and intensity which was strongly dependent on the polarity of its environment. Consequently, it was bound to certain proteins including bovine and human serum albumin, aI-acid glycoprotein and B-lactoglobulin and provided both qualitative and quantitative information on the nature and type of binding site on the protein. It was also used in the study of ligand protein binding interactions in which competition for a binding site on the protein occurs between the probe and other ligands such as drugs or fatty acids. The project also involved a preliminary investigation into a novel double probe technique for the study of drug-protein interactions and the development of a flow injection analysis method involving gradient titration of a drug against a probe:protein system.

Drugs exert their (therapeutic) effects via molecular-level interactions with proteins and other biomolecules. Computational prediction of drug-protein interactions plays a significant role in the effort to improve our current and limited knowledge of these interactions. The use of the putative drug-protein interactions could facilitate the discovery of novel applications of drugs, assist in cataloging their targets, and help to explain the details of medicinal efficacy and side-effects of drugs. We investigate current studies related to the computational prediction of drug-protein interactions and categorize them into protein structure-based and similarity-based methods. We evaluate three representative structure-based predictors and develop a Protein-Drug Interaction Database (PDID) that includes the putative drug targets generated by these three methods for the entire structural human proteome. To address the fact that only a limited set of proteins has known structures, we study the similarity-based methods that do not require this information. We review a comprehensive set of 35 high-impact similarity-based predictors and develop a novel, high-quality benchmark database. We group these predictors based on three types of similarities and their combinations that they use. We discuss and compare key architectural aspects of these methods including their source databases, internal databases and predictive models. Using our novel benchmark database, we perform comparative empirical analysis of predictive performance of seven types of representative predictors that utilize each type of similarity individually or in all possible combinations. We assess predictive quality at the database-wide drug-protein interaction level and we are the first to also include evaluation across individual drugs. Our comprehensive analysis shows that predictors that use more similarity types outperform methods that employ fewer similarities, and that the model combining all three types of similarities secures AUC of 0.93. We offer a first-of-its-kind analysis of sensitivity of predictive performance to intrinsic and extrinsic characteristics of the considered predictors. We find that predictive performance is sensitive to low levels of similarities between sequences of the drug targets and several extrinsic properties of the input drug structures, drug profiles and drug targets.

<p>This thesis concerns the development and validation of methods for characterization of multi-component preparative LC systems. Measurements of competitive adsorption isotherms are performed to gain detailed information about the interactions inside the chromatography column. This information increases our understanding of the separation process and makes it possible to perform computer simulations and numerical optimizations to find optimal operating conditions.</p><p>The methods under focus are called “the tracer-pulse method”, “the inverse method”, and “the inverse method on plateaus”. They are extensions of existing methods, with new experimental and numerical procedures to enable rapid and accurate multi-component adsorption isotherm determination. In the validation it was shown that they can produce results agreeing with traditional methods and that the acquired adsorption isotherm parameters can be used in simulations to accurately predict the outcome of preparative LC separations.</p><p>The methods were used to characterize several complex LC systems and two phenomena were discovered and theoretically treated: 1) The presence of invisible deformed peaks in single-component systems. 2) Peak deformations encountered with modern chiral stationary phases, caused by strongly adsorbed eluent additives. The latter type of deformation was highly tuneable and it was possible to adjust the enantiomer peak shapes so that the peaks tailed in opposite directions with the sharp sides in between, yielding baseline resolution at remarkably high sample loads.</p><p>In a final applied study both the LC-based perturbation peak method and a biosensor method based on surface plasmon resonance (SPR) were used for the first time for detailed characterization of chiral drug-protein interactions. The fundamental properties of the two very different methods were compared and it was found that the LC method is more suitable for multi-component analysis and that the SPR method is more suitable for stronger interactions.</p>

This thesis concerns the development and validation of methods for characterization of multi-component preparative LC systems. Measurements of competitive adsorption isotherms are performed to gain detailed information about the interactions inside the chromatography column. This information increases our understanding of the separation process and makes it possible to perform computer simulations and numerical optimizations to find optimal operating conditions. The methods under focus are called “the tracer-pulse method”, “the inverse method”, and “the inverse method on plateaus”. They are extensions of existing methods, with new experimental and numerical procedures to enable rapid and accurate multi-component adsorption isotherm determination. In the validation it was shown that they can produce results agreeing with traditional methods and that the acquired adsorption isotherm parameters can be used in simulations to accurately predict the outcome of preparative LC separations. The methods were used to characterize several complex LC systems and two phenomena were discovered and theoretically treated: 1) The presence of invisible deformed peaks in single-component systems. 2) Peak deformations encountered with modern chiral stationary phases, caused by strongly adsorbed eluent additives. The latter type of deformation was highly tuneable and it was possible to adjust the enantiomer peak shapes so that the peaks tailed in opposite directions with the sharp sides in between, yielding baseline resolution at remarkably high sample loads. In a final applied study both the LC-based perturbation peak method and a biosensor method based on surface plasmon resonance (SPR) were used for the first time for detailed characterization of chiral drug-protein interactions. The fundamental properties of the two very different methods were compared and it was found that the LC method is more suitable for multi-component analysis and that the SPR method is more suitable for stronger interactions.