<p dir="ltr">This thesis focusses on developing novel data driven oral drug formulation analysis methods by employing technologies such as Fourier transform analysis and generative adversarial learning. Data driven measurements have been addressing challenges in advanced manufacturing and analysis for pharmaceutical development for the last two decade. Data science combined with analytical chemistry holds the future to solving key problems in the next wave of industrial research and development. Data acquisition is expensive in the realm of pharmaceutical development, and how to leverage the capability of data science to extract information in data deprived circumstances is a key aspect for improving such data driven measurements. Among multiple measurement techniques, chemical imaging is an informative tool for analyzing oral drug formulations. However, chemical imaging can often fall into data deprived situations, where data could be limited from the time-consuming sample preparation or related chemical synthesis. An integrated imaging approach, which folds data science techniques into chemical measurements, could lead to a future of informative and cost-effective data driven measurements. In this thesis, the development of data driven chemical imaging techniques for the analysis of oral drug formulations via Fourier transformation and generative adversarial learning are elaborated. Chapter 1 begins with a brief introduction of current techniques commonly implemented within the pharmaceutical industry, their limitations, and how the limitations are being addressed. Chapter 2 discusses how Fourier transform fluorescence recovery after photobleaching (FT-FRAP) technique can be used for monitoring the phase separated drug-polymer aggregation. Chapter 3 follows the innovation presented in Chapter 1 and illustrates how analysis can be improved by incorporating diffractive optical elements in the patterned illumination. While previous chapters discuss dynamic analysis aspects of drug product formulation, Chapter 4 elaborates on the innovation in composition analysis of oral drug products via use of novel generative adversarial learning methods for linear analyses.</p>

Solubility prediction usually refers to prediction of the intrinsic aqueous solubility, which is the concentration of an unionised molecule in a saturated aqueous solution at thermodynamic equilibrium at a given temperature. Solubility is determined by structural and energetic components emanating from solid-phase structure and packing interactions, solute–solvent interactions, and structural reorganisation in solution. An overview of the most commonly used methods for solubility prediction is given in Chapter 1. In this thesis, we investigate various approaches to solubility prediction and solvation model development, based on informatics and incorporation of empirical and experimental data. These are of a knowledge-based nature, and specifically incorporate information from the Cambridge Structural Database (CSD). A common problem for solubility prediction is the computational cost associated with accurate models. This issue is usually addressed by use of machine learning and regression models, such as the General Solubility Equation (GSE). These types of models are investigated and discussed in Chapter 3, where we evaluate the reliability of the GSE for a set of structures covering a large area of chemical space. We find that molecular descriptors relating to specific atom or functional group counts in the solute molecule almost always appear in improved regression models. In accordance with the findings of Chapter 3, in Chapter 4 we investigate whether radial distribution functions (RDFs) calculated for atoms (defined according to their immediate chemical environment) with water from organic hydrate crystal structures may give a good indication of interactions applicable to the solution phase, and justify this by comparison of our own RDFs to neutron diffraction data for water and ice. We then apply our RDFs to the theory of the Reference Interaction Site Model (RISM) in Chapter 5, and produce novel models for the calculation of Hydration Free Energies (HFEs).