Sharif Digital Repository

The main aim of this thesis is to develop the technical knowledge of designing and making active ingredients of domestically produced drugs, decreasing the costs, independency in producing these products, and checking outflow of currency. It also aims to reduce the financial challenges of patients who have to pay huge amounts for such drugs. Conducting research in the field of reverse engineering and using its definition, and attempt in made to get knowledge into the stages of product development process, to determine the technical standards associated with each stage, to recognize how knowledge is shared during this process, and to present some methods of establishing the infrastructure... 

Technological capabilities as one of the growth’s aspects in developing countries have gained focus for research in last years. This study attempt to analyze the pattern of technological capabilities in four different sectors including Optic, Aerospace, Pharmaceutical and Biotechnology. In each sectors four Iranian companies as the case studies are selected. After deep interviews with key people in each company, required data is gathered. Data in each company is analyzed. The results of analysis for companies in one sector are gathered for analyzing each sector. In order to evaluate technological capabilities, the time required to improve, lead-lag relationship for gaining higher levels of... 

Lipopolysaccharide (LPS), also known as endotoxin, is a highly toxic component exists in the outer membrane of gram negative bacteria. It releases into the environment during every phase of bacterial growth cycle, so it causes contamination of a wide range of biopharmaceutical products. Even small quantities of endotoxin injected to human body can result in fever, septic shock, and death. Therefore, it is highly important to detect and also quantify endotoxin of biopharmaceutical products in quality control laboratories. The most validated method used for endotoxin detection is Limulus Amebocyte Lysate (LAL). Although this method is sensitive, it has some unavoidable drawbacks such as highly... 

Pharmaceutical active compounds existence in the water would cause serious ecological risks and human health-related adverse effects which turn to environmental concern, therefore several studies have done to remove pharmaceutical active compounds efficiently. Carbamazepine (CBZ), a widely used psychiatric drug, is one of the most frequently detected compounds in the surface water and groundwater which is studies in the current study. Recently, biocatalytic degradation using ligninolytic enzymes such as laccase provides a promising approach for their removal from water and wastewater. In this work, carbamazepine removal by immobilized laccase on modified membrane by multi wall carbon nanotube... 

According to the competitive nature of process industries and the necessity of increasing the efficiency of supply, manufacturing, and distribution operations, the optimization of such activities with an integrated attitude has become a major goal of research in the field of Process Systems Engineering. Lying at the interface of Chemical Engineering and Operations Research, enterprise-wide optimization has become a tool that enables the integrated optimization of different components of an industry to maximize the net profit. Design, Planning, Scheduling, and Control are four major operational items within such an optimization that may be simultaneously considered; among which, Planning and... 

Supply chain management is very important in pharmaceutical industry, due to its impact on people’s health. In this thesis we considered a three-layer multi-product multi-period pharmaceutical supply chain and we proposed a mixed-integer linear programming model to solve location, allocation, inventory and routing problems in this supply chain. This model aims to minimize the total costs in the supply chain. In this problem we also considered the deterioration of medicines. To bring the model closer to real world problems we considered demand as an uncertain parameter and handle the uncertainty using robust programming. We solve the model with large scale examples using genetic and Particle... 

Data mining is the process of extracting information from large data sets using algorithms and methods derived from the field of statistics, machine learning and database management systems. Data mining, popularly known as knowledge discovery in big data, enables companies and organizations to make informed decisions by collecting, aggregating, analyzing and accessing company data. The pharmaceutical industry is one of the most important levels of the drug supply chain, which has a significant impact on the healthcare sector of any society. In this context data mining can be used in various procedure such as discovery of a new medicine, sequential registration of clinical trials, combining... 

The aim of this research is to make a hierarchical zeolite catalyst as a basis for photocatalytic removal reactions of organic pollutants including dyes and drugs. The three main parts of this thesis deal with preparing the photocatalyst, performing the characterization test and investigating the reaction in different operating conditions. To prepare zeolite, first an organic molecule was synthesized to increase the diameter of the holes and it was used along with the rest of the precursors in the hydrothermal method. To increase the efficiency of photocatalytic reactions, the silver element was deposited on the ZnO / zeolite catalyst by photodeposition method and its characteristics were... 

Drug-target binding affinity prediction is one of the most important and vital part of drug discovery. The computational methods to predict binfing affinity is a standing challenge in drug discovery. State-of-the-art models are usually based on supervised machine learning with known label information. It is expensive and time-consuming to collect labeled data. This thesis proposes a semi-supervised model based on convolutional GAN (Generative adversarial networks). The model consists of two Gans and Two CNN blocks for feature extraction and fully connected layers for prediction. Gan can learn protein and drug features from unlabeled data. We evaluate the performance of our method using four... 

Despite significant progress in cancer treatment, drug resistance remains a major challenge. Synergistic drug combinations offer a promising approach to overcome drug resistance and reduce side effects. Still, despite high-throughput testing technologies, existing drug combination databases suffer from biases and a lack of diversity in tested cancer cell lines, which challenges the prediction of drug response on novel cell targets. To address this critical need, we designed a two-level deep learning method that uses large-scale gene expression datasets to estimate the score and synergy of drug compounds on a wide variety of cancer cell lines. Our model includes an auto-encoder that train on... 

Clopidogrel, as an antiplatelet medicine, is currently one of the most widely used medicines to reduce the risk of stroke and prevent the formation of blood clots for heart patients all over the world. This medicine is still produced using the conventional approach of drug production, i.e. batch method. On the other hand, due to the many advantages of flow chemistry, such as increasing the rate of mass and heat transfer, increasing safety, performance, etc., the production of medicines using the flow approach has been highly regarded by large pharmaceutical companies in recent years. In this research, the production of clopidogrel was done using a continuous flow approach and different...