Development of Methods to Predict and Enhance the Physical Stability of Hot Melt Extruded Solid Dispersions

The application of amorphous solid dispersions is one of the most widely used formulation strategies for the enhancement of in-vitro and in-vivo performance of poorly water-soluble drugs. However, because of their meta-stable nature, the physical stability of amorphous solid dispersions has been considered to be the main obstacle for their formulation development and commercialisation by the pharmaceutical industry. The aim of this project was to understand, predict and enhance the physical stability of amorphous solid dispersions prepared by hot melt extrusion. Four model drugs felodipine, celecoxib, fenofibrate and carbamazepine and two polymeric matrices EUDRAGIT® EPO and Kollidon® VA 64 were formulated by hot melt extrusion and spin coating into solid dispersions. A series of physicochemical characterisation techniques including MTDSC, PXRD, SEM, ATR-FTIR and AFM-LTA were used to evaluate the systems. Physical characterisation of the model drugs and polymers, prediction of drug-polymer miscibility and solubility and real-time physical stability studies under different conditions were carried out. Across the project, several key achievements were obtained. It was revealed that the physical stability of the amorphous drugs alone and the predicted processing-related apparent drugpolymer solubility were the two dominant factors controlling the physical stability of the amorphous systems. A practical method, milling, was developed to provide a more accurate prediction of processing-related apparent drug-polymer solubility. Two methods were developed to enhance the physical stability of amorphous solid dispersions: one based on formulation design, use of immiscible polymer blends and the other based on a particular type of processing, spin coating. The achievements from the project are expected to contribute to the formulation development of amorphous solid dispersions in terms of screening suitable drug candidates, selecting “safe” (physically stable) drug loadings and identification of methodologies to improve the physical stability of formulations

Monitoring and predicting railway subsidence using InSAR and time series prediction techniques

Improvements in railway capabilities have resulted in heavier axle loads and higher speed operations, which increase the dynamic loads on the track. As a result, railway subsidence has become a threat to good railway performance and safe railway operation. The author of this thesis provides an approach for railway performance assessment through the monitoring and prediction of railway subsidence. The InSAR technique, which is able to monitor railway subsidence over a large area and long time period, was selected for railway subsidence monitoring. Future trends of railway subsidence should also be predicted using subsidence prediction models based on the time series deformation records obtained by InSAR. Three time series prediction models, which are the ARMA model, a neural network model and the grey model, are adopted in this thesis. Two case studies which monitor and predict the subsidence of the HS1 route were carried out to assess the performance of HS1. The case studies demonstrate that except for some areas with potential subsidence, no large scale subsidence has occurred on HS1 and the line is still stable after its 10 years' operation. In addition, the neural network model has the best performance in predicting the subsidence of HS1