Preparation and evaluation of azithromycin binary solid dispersions using various polyethylene glycols for the improvement of the drug solubility and dissolution rate

A azitromicina é um fármaco insolúvel em água, com biodisponibilidade muito baixa. A fim de aumentar a taxa de solubilidade e dissolução e, consequentemente, aumentar a biodisponibilidade de fármacos fracamente solúveis (tais como azitromicina), várias técnicas podem ser aplicadas. Uma dessas técnicas é a "dispersão sólida", frequentemente usada para melhorar a taxa de dissolução de compostos fracamente solúveis em água. Devido à baixa taxa de solubilidade e de dissolução, este fármaco não tem biodisponibilidade adequada. Portanto, o principal objetivo desta pesquisa foi o de aumentar a taxa de solubilidade e dissolução da azitromicina, preparando a sua dispersão sólida, utilizando diferentes glicóis de polietileno (PEG). As dispersões sólidas e as misturas físicas de azitromicina foram preparadas utilizando PEG 4000, 6000, 8000, 12000 e 20000, em várias proporções, com base no método de evaporação do solvente. O perfil de liberação do fármaco foi estudado e verificou-se que tanto a taxa de dissolução da mistura física quanto as dispersões sólidas foram maiores do que as do fármaco sozinho. Espectros de IR não revelaram incompatibilidade química entre o fármaco e o polímero. Interações fármaco-polímero também foram investigadas usando calorimetria diferencial de varredura (DSC), Difração de Raios X (PXRD) e Microscopia Eletrônica de Varredura(SEM). Em conclusão, a taxa de dissolução e a solubilidade da azitromicina melhoraram, de forma significativa, utilizando suportes hidrofílicos, especialmente PEG 6000.Azithromycin is a water-insoluble drug, with a very low bioavailability. In order to increase the solubility and dissolution rate, and consequently increase the bioavailability of poorly-soluble drugs (such as azithromycin), various techniques can be applied. One of such techniques is "solid dispersion". This technique is frequently used to improve the dissolution rate of poorly water-soluble compounds. Owing to its low solubility and dissolution rate, azithromycin does not have a suitable bioavailability. Therefore, the main purpose of this investigation was to increase the solubility and dissolution rate of azithromycin by preparing its solid dispersion, using different Polyethylene glycols (PEG). Preparations of solid dispersions and physical mixtures of azithromycin were made using PEG 4000, 6000, 8000, 12000 and 20000 in various ratios, based on the solvent evaporation method. From the studied drug release profile, it was discovered that the dissolution rate of the physical mixture, as the well as the solid dispersions, were higher than those of the drug alone. There was no chemical incompatibility between the drug and polymer from the observed Infrared (IR) spectra. Drug-polymer interactions were also investigated using Differential Scanning Calorimetry (DSC), Powder X-Ray Diffraction (PXRD) and Scanning Election Microscopy (SEM). In conclusion, the dissolution rate and solubility of azithromycin were found to improve significantly, using hydrophilic carriers, especially PEG 6000

Bayesian parameter identification in plasticity

To evaluate the cyclic behaviour under different loading conditions using the kinematic and isotropic hardening theory of steel a Chaboche visco-plastic material model is employed. The parameters of a constitutive model are usually identified by minimization of the distance between model response and experimental data. However, measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article the Choboche model is used and a stochastic simulation technique is applied to generate artificial data which exhibit the same stochastic behaviour as experimental data. Then the model parameters are identified by applying a variaty of Bayes’s theorem. Identified parameters are compared with the true parameters in the simulation and the efficiency of the identification method is discussed

End-To-End Alzheimer's Disease Diagnosis and Biomarker Identification

As shown in computer vision, the power of deep learning lies in automatically learning relevant and powerful features for any perdition task, which is made possible through end-to-end architectures. However, deep learning approaches applied for classifying medical images do not adhere to this architecture as they rely on several pre- and post-processing steps. This shortcoming can be explained by the relatively small number of available labeled subjects, the high dimensionality of neuroimaging data, and difficulties in interpreting the results of deep learning methods. In this paper, we propose a simple 3D Convolutional Neural Networks and exploit its model parameters to tailor the end-to-end architecture for the diagnosis of Alzheimer's disease (AD). Our model can diagnose AD with an accuracy of 94.1\% on the popular ADNI dataset using only MRI data, which outperforms the previous state-of-the-art. Based on the learned model, we identify the disease biomarkers, the results of which were in accordance with the literature. We further transfer the learned model to diagnose mild cognitive impairment (MCI), the prodromal stage of AD, which yield better results compared to other methods

Bestimmung des viskoplastischen Schadensmodells durch Bayes-Methoden

The state of materials and accordingly the properties of structures are changing over the period of use, which may influence the reliability and quality of the structure during its life-time. Therefore identification of the model parameters of the system is a topic which has attracted attention in the content of structural health monitoring. The parameters of a constitutive model are usually identified by minimization of the difference between model response and experimental data. However, the measurement errors and differences in the specimens lead to deviations in the determined parameters. In this thesis, the focus is on the identification of material parameters of a viscoplastic damaging material using a stochastic simulation technique to generate artificial data which exhibit the same stochastic behavior as experimental data. It is proposed to use Bayesian inverse methods for parameter identification. To do so, two steps are considered, solving the forward and the inverse problem. Therefore, first the propagation of the a priori parametric uncertainty through the model including hardening behavior and damage describing the behavior of a steel structure is studied. A non-intrusive stochastic finite element method based on polynomial chaos is applied. From the forward model, material parameters can be identified using measurement data such as displacement via Bayesian approaches. In this thesis, two methods are applied. The first one is a Transitional Markov chain Monte Carlo method that generates the samples of the posterior probability distribution functions. The second one is a linear approximation of the conditional expectation, the so-called Gauss-Markov-Kalman filter, which is a modification of the Kalman filter, by using the polynomial chaos expansion as the spectral approximation. The applicability of these methods on the desired model is evaluated and the results of both these methods are studied. Further, the efficiency of these identification methods is discussed. Moreover, the evaluated efficient approach is applied to a well-known CT-Test to identify its model parameters by using the data from a pure surface measurement of strain. As the damage parameters can also be determined by considering a minor damage, i.e. not a collapsing damage, the selected Bayesian approach can be proposed for the purpose of structure health monitoring for mechanical material models considering real tests.Der Zustand von Materialien und dementsprechend die Eigenschaften von Konstruktionen ändern sich über die Nutzungsdauer, was die Zuverlässigkeit und Qualität der Konstruktion während ihrer Lebensdauer beeinflussen kann. Daher ist die Identifizierung der Modellparameter des Systems ein Thema, das inhaltlich beim strukturellen Zustandsmonitoring Interesse gefunden hat. Die Parameter eines konstitutiven Modells werden normalerweise durch Minimierung der Differenz zwischen der Modellantwort und den experimentellen Daten identifiziert. Die Messfehler und Unterschiede in den Proben führen jedoch zu Abweichungen in den ermittelten Parametern. In dieser Arbeit liegt der Fokus auf der Identifizierung von Materialparametern eines viskoplastischen Materials mit Möglichkeit der Schädigung unter Verwendung einer stochastischen Simulationstechnik, um künstliche Daten zu erzeugen, die dasselbe stochastische Verhalten wie experimentelle Daten zeigen. Es wird vorgeschlagen, Bayes'sche inverse Methoden zur Parameteridentifikation zu verwenden. Um dies zu tun, werden zwei Schritte betrachtet: das vorwärts- und das inverse Problem. Daher wird zunächst die Ausbreitung der a priori parametrischen Unsicherheit durch das Modell untersucht, einschließlich des Verfertigungsverhaltens und der das Verhalten einer Stahlstruktur beschreibenden Schädigung. Es wird eine nicht-intrusive stochastische Finite-Elemente-Methode angewendet, die auf polynomialem Chaos basiert. Aus dem Vorwärtsmodell können Materialparameter anhand von Messdaten wie Verschiebungen über Bayes'sche Ansätze identifiziert werden. In dieser Arbeit werden zwei Methoden angewendet. Die erste ist eine Transitional-Markov-Chain-Monte-Carlo-Methode, die die Stichproben der a posteriori Wahrscheinlichkeitsverteilungsfunktionen generiert. Die zweite ist eine lineare Approximation an die bedingte Erwartung, das sogenannte Gauss-Markov-Kalman-Filter, das eine Modifikation des Kalman-Filters ist, indem die Polynom-Chaos-Expansion als spektrale Näherung verwendet wird. Die Anwendbarkeit dieser Methoden auf das gewünschte Modell wird bewertet und die Ergebnisse dieser beiden Methoden werden untersucht. Ferner wird die Effizienz dieser Identifikationsmethoden diskutiert. Darüber hinaus wird der effizientbewertete Ansatz auf einen bekannten CT-Test angewendet, um seine Modellparameter anhand der Daten einer reinen Oberflächenmessung der Dehnung zu ermitteln. Da die Schadensparameter auch bestimmt werden können, indem ein geringer Schaden betrachtet wird, d. h. kein schwerer schaden, kann der ausgewählte Bayes'sche Ansatz zum Zweck der Zustandsüberwachung für mechanische Materialmodelle unter Berücksichtigung realer Tests vorgeschlagen werden