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Estimating Demand Function for Antihypertensives in Iran
, M.Sc. Thesis Sharif University of Technology ; Fatemi Ardestati, Farshad (Supervisor)
Abstract
The essential need for allocation of appropriate resources to the wellbeing and medication plans in a society can be understood from the fundamental impact of health on the performance of the labor force of the society, which can lead to significant increase in the economic growth rate. The high costs associated with research and development make pharmaceutical industry owners to be very vigilant about their investment on new drugs and expected sales. The purpose of this study is to evaluate current Iranian market for a part of Antihypertensive drugs and to estimate the demand for this group of drugs. The method used to obtain the estimation involved multi-stage budgeting, which benefits...
Investigating the Effect of Geometric Shape and Properties of Protein Corona on Drug Release Using Finite Element Method
, M.Sc. Thesis Sharif University of Technology ; Naghdabadi, Reza (Supervisor)
Abstract
In novel drug delivery systems, once nanocarriers confront the biological milieu, their surface is rapidly covered with a layer of biomolecules (i.e., “protein corona”) which play an important role in their drug release rate. Various experimental studies have been done to elucidate the effect of nanoparticles properties on the drug release rate in different biological applications. The physical and geometrical properties of protein corona totally influence on the release profile. In this study, we proposed a suitable finite element model which contains the nanoparticles and the protein layer with their properties in the biological milieu. To this end, diffusion parameters including diffusion...
Drug-Target Binding Affinity Prediction Based on Machine Learning Algorithms and Features Extraction using Neural Networks
, M.Sc. Thesis Sharif University of Technology ; Koohi, Somayyeh (Supervisor)
Abstract
One of the fundamental steps in drug discovery and development is predicting the binding affinity of the drug to target proteins. Due to the high costs, slow pace, and lack of structural data in experimental approaches, several machine learning and deep learning-based methods have been proposed to date. Machine learning-based methods, which have shown high prediction capabilities, require precise engineering and selection of a wide range of physicochemical, biological, and structural information about proteins and drugs. On the other hand, deep neural network-based methods require the design of numerous complex networks for automatic feature extraction. Therefore, a method that can utilize...