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This dissertation describes formulation of a gastric retention device (GRD) and sustained release (SR) hydrochlorothiazide beads at Oregon State University. Formulation condition and amounts of excipients had significant influence on characteristic of the GRD. The GRD containing SR hydrochlorothiazide beads was employed to assess bioavailability/bioequivalency study in healthy subjects. An original GRD was retained in the stomach with food and completed the drug release. However, this original GRD failed to stay on an empty stomach, leading to lower bioavailability than an immediate release (IR) tablet due to insufficient rigidity. The original device was modified to be more rigid, and this more rigid device successfully stayed on an empty stomach and achieved completion of the drug release at a slow release rate, the bioavailability and the drug effect on diuresis increased compared to the drug in an IR tablet. Less amount of hydrochlorothiazide at a slow release rate achieved equivalent diuresis to higher amount of the drug at a rapid release rate, which indicated slow drug release resulted in higher efficiency of hydrochlorothiazide. In vivo/in vitro correlation of hydrochlorothiazide in a modified GRD and an IR tablet was established to predict in vivo profile with in vitro dissolution profile prior to a clinical study. Pharmacokinetic/pharmacodynamic of nicotine was reviewed in terms of a relationship between plasma nicotine concentrations and pharmacological changes including heart rate and craving. Considering craving and development of tolerance to nicotine effect on cardio-acceleration, a dosing regimen with a combination of rapid input and constant slow input was suggested to improve smoking cessation. A simulation study was carried out to verify the current regulatory policy on assessing bioequivalency of enantiomeric drugs. First-order dissolution and absorption process, and nonlinear stereo-specific pre-systemic and systemic metabolism was taken into account to establish a pharmacokinetic model for the simulation. Four different dissolution profiles, within- and between-subject variability, dose and sample size were considered to simulate 1000 cross-over bioequivalency trials under standard bioequivalency criteria. Probability of false positives was determined to evaluate the current policy. The simulation study validated the importance of individual enantiomer pharmacokinetic for assessing bioequivalency study of the chiral drugs. / Graduation date: 2003

The design and optimization of chemotherapeutic molecules through molecular modeling is a rapidly growing aspect of drug design. The recent increase in computer power and accompanying decrease in the cost of hardware has led to the wide use of computational chemistry in the development of new drugs. In addition, virtual screening of compound libraries also aids in the rapid development of new drugs. In that regard, there are three computational projects in addition to a project involving the synthesis of potential inhibitors that compile the research presented herein. The first project involves molecular mechanics simulations of isoadenosine analogues as potential inhibitors of S-adenosylhomocysteine hydrolase (SAHase). These analogues possess a carbocyclic moiety at the N-3 position instead of the normal N-9. The second project involves molecular mechanics simulations on flexible nucleosides as bioprobes of biologically significant enzymes. These purine analogues have nucleobases that are separated into their imidazole and pyrimidine rings connected by a single carbon-carbon bond.. This feature imparts flexibility to the base. The third project involves molecular dynamics simulations on expanded purine nucleotides in modified DNA. These compounds possess a heteroaromatic spacer ring inserted between the imidazole and pyrimidine portions of adenosine and guanosine purine rings. These analogues were and are incorporated into 10- and 20-mer DNA strands to investigate the effects on DNA. The final project focuses on the synthesis of a series of chlorinated 3-deazaadenosine analogues as potential anticancer agents. These 3-deazaadenine analogues have chlorine systematically placed in the 2-, 6- and 8-positions of adenine.

The traditional approach of Quality by Testing (QbT) limits the assurance of product quality to in-process and post-production testing. To overcome these limitations, a more proactive and systematic means to product development and optimisation is required. Quality by Design (QbD) is an example of such an approach which focuses on understanding the product and its manufacturing process and emphasises that quality should be built into the product and not merely tested. The study aims to optimise ethionamide tablets, an immediate release oral solid dosage form using QbD.

<p>Leonotis leonorus and Mentha longifolia are two herbs commonly used in South Africa, mostly in oral liquid dosage forms. Several disadvantages are associated with these traditional dosage forms which can perhaps be remedied by using an appropriate oral solid dosage form, provided the actual plant material in the latter still resemble, as closely as possible, the traditionally used material and provide products of suitable pharmaceutical quality. The objectives of this study were to prepare and evaluate the pharmaceutical suitability of the freeze-dried aqueous extracts of Leonotis Leonorus and Mentha Longifolia as plant raw material for the capsule dosage of these two therapies and to formulate and manufacture capsules of Leonotis Leonorus and Mentha Longifolia aqueous extract that would contain amounts of the plant materials equivalent to that found in their traditional liquid dosage forms, and have immediate release characteristics and suitability stability.</p>

University of Macau / Faculty of Science and Technology. / Department of Computer and Information Science

Developing a new drug costs up to US$2.6B and 13.5 years. To save money and time, we have developed a toolset for computer-aided drug discovery, and utilized our toolset to discover drugs for the treatment of cancers and influenza. / We first implemented a fast protein-ligand docking tool called idock, and obtained a substantial speedup over a popular counterpart. To facilitate the large-scale use of idock, we designed a heterogeneous web platform called istar, and collected a huge database of more than 23 million small molecules. To elucidate molecular interactions in web, we developed an interactive visualizer called iview. To synthesize novel compounds, we developed a fragment-based drug design tool called iSyn. To improve the predictive accuracy of binding affinity, we exploited the machine learning technique random forest to re-score both crystal and docked poses. To identify structurally similar compounds, we ported the ultrafast shape recognition algorithms to istar. All these tools are free and open source. / We applied our novel toolset to real world drug discovery. We repurposed anti-acne drug adapalene for the treatment of human colon cancer, and identified potential inhibitors of influenza viral proteins. Such new findings could hopefully save human lives. / 開發一種新藥需要多至26億美元和13年半的時間。為節省金錢和時間，我們開發了一套計算機輔助藥物研發工具集，並運用該工具集尋找藥物治療癌症和流感。 / 我們首先實現了一個快速的蛋白與配體對接工具idock，相比一個同類流行軟件獲得了顯著的速度提升。為輔助idock 的大規模使用，我們設計了一個異構網站平台istar，收集了多達兩千三百萬個小分子的大型數據庫。為在網頁展示分子間相互作用，我們開發了一個交互式可視化軟件iview。為生成全新的化合物，我們開發了一個基於分子片段的藥物設計工具iSyn。為改進結合強度預測的精度，我們利用了機器學習技術隨機森林去重新打分晶體及預測構象。為尋找結構相似的化合物，我們移植了超快形狀識別算法至istar。所有這些工俱全是免費和開源。 / 我們應用了此創新工具集至現實世界藥物尋找中。我們發現抗痤瘡藥阿達帕林可用於治療人類結腸癌，亦篩選出流感病毒蛋白的潛在抑制物。這些新發現可望拯救人類生命。 / Li, Hongjian. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2015. / Includes bibliographical references (leaves 340-394). / Abstracts also in Chinese. / Title from PDF title page (viewed on 15, September, 2016). / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only.

Li, Hongjian. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 132-156). / Abstracts in English and Chinese. / Abstract --- p.i / Abstract in Chinese --- p.iii / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.2 / Chapter 1.2 --- Objective --- p.2 / Chapter 1.3 --- Method --- p.3 / Chapter 1.4 --- Outline --- p.4 / Chapter 2 --- Background --- p.7 / Chapter 2.1 --- Overview of the Pharmaceutical Industry --- p.7 / Chapter 2.2 --- The Process of Modern Drug Discovery --- p.10 / Chapter 2.2.1 --- Development of an Innovative Idea --- p.10 / Chapter 2.2.2 --- Establishment of a Project Team --- p.11 / Chapter 2.2.3 --- Target Identification --- p.11 / Chapter 2.2.4 --- Hit Identification --- p.12 / Chapter 2.2.5 --- Lead Identification --- p.13 / Chapter 2.2.6 --- Lead Optimization --- p.14 / Chapter 2.2.7 --- Clinical Trials --- p.14 / Chapter 2.3 --- Drug Discovery via Computational Means --- p.15 / Chapter 2.3.1 --- Structure-Based Virtual Screening --- p.16 / Chapter 2.3.2 --- Computational Synthesis of Potent Ligands --- p.20 / Chapter 2.3.3 --- General-Purpose Computing on GPU --- p.23 / Chapter 3 --- Approximate Matching of DNA Patterns --- p.26 / Chapter 3.1 --- Problem Definition --- p.27 / Chapter 3.2 --- Motivation --- p.28 / Chapter 3.3 --- Background --- p.30 / Chapter 3.4 --- Method --- p.32 / Chapter 3.4.1 --- Binary Representation --- p.32 / Chapter 3.4.2 --- Agrep Algorithm --- p.32 / Chapter 3.4.3 --- CUDA Implementation --- p.34 / Chapter 3.5 --- Experiments and Results --- p.39 / Chapter 3.6 --- Discussion --- p.44 / Chapter 3.7 --- Availability --- p.45 / Chapter 3.8 --- Conclusion --- p.47 / Chapter 4 --- Structure-Based Virtual Screening --- p.50 / Chapter 4.1 --- Problem Definition --- p.51 / Chapter 4.2 --- Motivation --- p.52 / Chapter 4.3 --- Medicinal Background --- p.52 / Chapter 4.4 --- Computational Background --- p.59 / Chapter 4.4.1 --- Scoring Function --- p.59 / Chapter 4.4.2 --- Optimization Algorithm --- p.65 / Chapter 4.5 --- Method --- p.68 / Chapter 4.5.1 --- Scoring Function --- p.69 / Chapter 4.5.2 --- Inactive Torsions --- p.72 / Chapter 4.5.3 --- Optimization Algorithm --- p.73 / Chapter 4.5.4 --- C++ Implementation Tricks --- p.74 / Chapter 4.6 --- Data --- p.75 / Chapter 4.6.1 --- Proteins --- p.75 / Chapter 4.6.2 --- Ligands --- p.76 / Chapter 4.7 --- Experiments and Results --- p.77 / Chapter 4.7.1 --- Program Validation --- p.77 / Chapter 4.7.2 --- Virtual Screening --- p.81 / Chapter 4.8 --- Discussion --- p.89 / Chapter 4.9 --- Availability --- p.90 / Chapter 4.10 --- Conclusion --- p.91 / Chapter 5 --- Computational Synthesis of Ligands --- p.92 / Chapter 5.1 --- Problem Definition --- p.93 / Chapter 5.2 --- Motivation --- p.93 / Chapter 5.3 --- Background --- p.94 / Chapter 5.4 --- Method --- p.97 / Chapter 5.4.1 --- Selection --- p.99 / Chapter 5.4.2 --- Mutation --- p.102 / Chapter 5.4.3 --- Crossover --- p.102 / Chapter 5.4.4 --- Split --- p.103 / Chapter 5.4.5 --- Merging --- p.104 / Chapter 5.4.6 --- Drug Likeness Testing --- p.104 / Chapter 5.5 --- Data --- p.105 / Chapter 5.5.1 --- Proteins --- p.105 / Chapter 5.5.2 --- Initial Ligands --- p.107 / Chapter 5.5.3 --- Fragments --- p.107 / Chapter 5.6 --- Experiments and Results --- p.109 / Chapter 5.6.1 --- Binding Conformation --- p.112 / Chapter 5.6.2 --- Free Energy and Molecule Weight --- p.115 / Chapter 5.6.3 --- Execution Time --- p.116 / Chapter 5.6.4 --- Support for Phosphorus --- p.116 / Chapter 5.7 --- Discussion --- p.120 / Chapter 5.8 --- Availability --- p.123 / Chapter 5.9 --- Conclusion --- p.123 / Chapter 5.10 --- Personal Contribution --- p.124 / Chapter 6 --- Conclusion --- p.125 / Chapter 6.1 --- Conclusion --- p.125 / Chapter 6.2 --- Future Work --- p.128 / Chapter A --- Publications --- p.130 / Chapter A.1 --- Conference Papers --- p.130 / Chapter A.2 --- Journal Papers --- p.131 / Bibliography --- p.132