<p>  </p> <p>As the pharmaceutical industry moves towards molecular obesity with the use of high throughput screening for identification of promising candidates, the low aqueous solubilities of new chemical entities pose significant challenges to achieving adequate oral absorption and bioavailability. Enabling formulations are often needed to address this issue. Amorphous solid dispersion (ASD), where an amorphous drug and a polymer are molecularly mixed, has gained popularity as a dissolution/solubility enhancing strategy over the years. Upon ASD dissolution, the release rate of drug is much higher than that of the neat amorphous form of the drug. More importantly, the apparent concentration of drug in the solution can exceed its amorphous solubility through the formation of a drug-rich colloidal phase in the solution, also called nanodroplets. The presence of nanodroplets has been shown to be beneficial for oral absorption and bioavailability and their formation during release is therefore desirable. However, such release profiles are only achieved at relatively low drug loadings (DLs) and release tends to drop with increasing DL. For ASDs based on polyvinylpyrrolidone/vinyl acetate (PVPVA), drug release drops drastically once the DL exceeds a certain value, called limit of congruency (LoC). The low DL at which the ASD demonstrates good release also presents additional challenges since it can create a pill burden for patients due to the large amount of polymer needed in the formulation. Therefore, to achieve optimal drug product performance, it is crucial to understand the mechanisms of drug release. Therefore, this thesis focuses on understanding the factors affecting, and the mechanisms of ASD drug release, as well as enhancing drug release through addition of surfactants. </p> <p>The glass transition temperature of a drug and its interaction with the polymer were identified as important factors affecting the drug release and LoC. Another phase transition occurring during ASD hydration/dissolution, amorphous-amorphous phase separation (AAPS), was shown to affect drug release from ASD significantly. During dissolution, water-induced AAPS occurs, and the initially miscible ASD separates into two phases, an insoluble drug-rich phase and a soluble water/polymer-rich phase. The formation of a continuous drug-rich phase at the ASD-solution interface was shown to be detrimental to drug release as it could act as barrier that blocked any further drug release. When the drug-rich phase formed adopted a discrete morphology or when phase separation occurred in the solution outside of the dissolving ASD matrix, good release could be achieved. Surfactants could interrupt the formation of the continuous drug-rich both kinetically and thermodynamically, improving drug release as a result. Other mechanisms of release enhancement by surfactants included increased polymer release rate, increased water ingress and plasticization. The findings in this thesis will provide insight into ASD release mechanisms, and facilitate rational excipient selection when designing ASD formulations.  </p>

The applications of dense gas technology (DGT) in modern particle engineering have shown promising results in producing submicron particles with uniform particle morphology. In this study, two configurations of dense gas antisolvent processes were employed for the micronization, encapsulation and co-precipitation of pharmaceutical compounds. The encapsulation of superparamagnetic iron oxide nanoparticles (SPIONs) by a pH-responsive polymer (Eudragit?? S100) was successfully performed using the supercritical antisolvent (SAS) process. Nanocomposites of less than 200nm in diameter with encapsulated SPIONs content as high as 16 wt% were achieved. Magnetic characterization of the product was also performed and the data were fitted by the Langevin equation. The superparamagnetic properties of the composites were preserved and the effective magnetic size was about 10 nm. The magnetically and pH-responsive nanocomposites can be potentially utilized as magnetic resonance imaging contrast agents and drug carriers. Screening experiments of 8 active pharmaceutical ingredients and 5 pharmaceutical excipients were performed using the recently patented atomized rapid injection solvent extraction (ARISE) process. Candidates with promising product morphology and recovery were selected for co-precipitation studies. The co-precipitation of the anti-cancer drug 5-fluorouracil (5FU) and poly l-lactic acid (PLLA) was conducted to develop a controlled release system. Experiments were designed based on a two-level, three-factor factorial design, in order to investigate the effects of processing parameters on product characteristics. Submicron PLLA-5FU composites (diameter<0.8 ??m) with a drug loading of 7.4 wt% were produced.

This multidisciplinary thesis is concerned with the prediction of drug formulations for the reduction of drug toxicity. Both scientific and computational approaches are utilised to make original contributions to the field of predictive toxicology. The first part of this thesis provides a detailed scientific discussion on all aspects of drug formulation and toxicity. Discussions are focused around the principal mechanisms of drug toxicity and how drug toxicity is studied and reported in the literature. Furthermore, a review of the current technologies available for formulating drugs for toxicity reduction is provided. Examples of studies reported in the literature that have used these technologies to reduce drug toxicity are also reported. The thesis also provides an overview of the computational approaches currently employed in the field of in silico predictive toxicology. This overview focuses on the machine learning approaches used to build predictive QSAR classification models, with examples discovered from the literature provided. Two methodologies have been developed as part of the main work of this thesis. The first is focused on use of directed bipartite graphs and Venn diagrams for the visualisation and extraction of drug-vehicle relationships from large un-curated datasets which show changes in the patterns of toxicity. These relationships can be rapidly extracted and visualised using the methodology proposed in chapter 4. The second methodology proposed, involves mining large datasets for the extraction of drug-vehicle toxicity data. The methodology uses an area-under-the-curve principle to make pairwise comparisons of vehicles which are classified according to the toxicity protection they offer, from which predictive classification models based on random forests and decisions trees are built. The results of this methodology are reported in chapter 6.

This multidisciplinary thesis is concerned with the prediction of drug formulations for the reduction of drug toxicity. Both scientific and computational approaches are utilised to make original contributions to the field of predictive toxicology. The first part of this thesis provides a detailed scientific discussion on all aspects of drug formulation and toxicity. Discussions are focused around the principal mechanisms of drug toxicity and how drug toxicity is studied and reported in the literature. Furthermore, a review of the current technologies available for formulating drugs for toxicity reduction is provided. Examples of studies reported in the literature that have used these technologies to reduce drug toxicity are also reported. The thesis also provides an overview of the computational approaches currently employed in the field of in silico predictive toxicology. This overview focuses on the machine learning approaches used to build predictive QSAR classification models, with examples discovered from the literature provided. Two methodologies have been developed as part of the main work of this thesis. The first is focused on use of directed bipartite graphs and Venn diagrams for the visualisation and extraction of drug-vehicle relationships from large un-curated datasets which show changes in the patterns of toxicity. These relationships can be rapidly extracted and visualised using the methodology proposed in chapter 4. The second methodology proposed, involves mining large datasets for the extraction of drug-vehicle toxicity data. The methodology uses an area-under-the-curve principle to make pairwise comparisons of vehicles which are classified according to the toxicity protection they offer, from which predictive classification models based on random forests and decisions trees are built. The results of this methodology are reported in chapter 6.

Antibiotics are biologically active and are globally used in humans and animal medicine for treatment and in sub-therapeutic amounts as growth promoters in animal husbandry, aquaculture and agriculture. After excretion, inappropriate disposal and discharge from drug production facilities they enter into water bodies either as intact drugs, metabolites or transformed products. In water environments they promote development of antibiotic resistance genes (ARGs) which could serve as a reservoir and be horizontally transferred to human-associated bacteria and thus contribute to AR proliferation. Measurement of antibiotics has been revolutionized with the usage of solid phase extraction (SPE) for enrichment followed by Liquid chromatography mass spectrometry (LC-MS). On-line SPE coupled to LC-MS/MS has the advantages of high sample throughput, low sample preparation time and minimal solvent utilization.  Constructed wetlands (CWs) are potential alternatives to conventional treatment plants to remove organic pollutants. A study at Plönninge, Halmstad was performed to assess the impact of bacterial community pattern and development of resistance in spiked (n=4) and control (n=4). CWs were spiked with antibiotics at environmentally relevant concentrations continuously for 25 days. Shannon Index (H’) were used to determine the bacterial diversity and real-time PCR detected and quantified antibiotic resistance genes (ARGs) sulI, tetA, tetB, erm, dfrA1, qnrS and vanB and class 1 integrons intI1. No significant differences in bacterial compositions or in ARGs or integron concentrations could be discerned between exposed and control wetlands. A study conducted in Northern Pakistan showed that the antibiotic levels in most studied rivers were comparable to surface water measurements in unpolluted sites in Europe and the US. However, high levels of antibiotics were detected in the river in close vicinity of the 10 million city Lahore, e.g. 4600 ng L−1 sulfamethoxazole. Highest detected levels were at one of the drug formulation facilities, with measured levels up to 49000 ng L−1 of sulfamethoxazole for example. The highest levels of ARGs detected, sul1 and dfrA1, were directly associated with the antibiotics detected at the highest concentrations, sulfamethoxazole and trimethoprim. In the study in UK, sewage epidemiology surveillance is used to measure the oseltamivir carboxylate (OC), metabolite of oseltamivir (parent drug) in twenty four time proportional hourly influent samples from two WWTPs and then back-calculations were made to assess the compliance of drug.  Predicted users of oseltamivir, based on measured OC in waste water, ranged from 3-4 and 120-154 people for the two WWTP catchments, respectively, which are consistent with the projected use from national antiviral allocation statistics, 3-8 and 108-270, respectively. Scenario analysis suggests compliance was likely between 45-60% in the study regions.