Investigating interaction networks for drug-induced glucose-6-phosphate dehydrogenase deficiency-related hemolysis

碩士 === 高雄醫學大學 === 藥學系碩士在職專班 === 104 === Glucose 6-phosphate dehydrogenase deficiency, an X-linked disorder, is the most common enzymatic disorder of red blood cells in humans. It can cause hemolysis after exposure to drugs with a high redox potential, fava beans, selected infections, or metabolic ab...

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Bibliographic Details
Main Authors: Wan-Lin Hsu, 徐宛琳
Other Authors: Chun-Wei Tung
Format: Others
Language:zh-TW
Published: 2016
Online Access:http://ndltd.ncl.edu.tw/handle/38082545986976855470
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Summary:碩士 === 高雄醫學大學 === 藥學系碩士在職專班 === 104 === Glucose 6-phosphate dehydrogenase deficiency, an X-linked disorder, is the most common enzymatic disorder of red blood cells in humans. It can cause hemolysis after exposure to drugs with a high redox potential, fava beans, selected infections, or metabolic abnormalities. Several drugs have been found to be high risk of hemolytic anemia for G6PD deficiency patients. However, there is no standard method to evaluate drugs for their risks of hemolytic anemia. We propose to utilize ChemDIS system to study the interaction network of drugs and proteins and identify important biomarkers capable of predict corresponding risks. A total of 42 high-risk drugs, 36 low-risk drugs and 53 safe drugs were collected. Several proteins have been identified as important biomarkers such as G6PD, KMO, MHD2 and NF-kB1. The interaction networks of high-risk, low-risk and safe drugs were analyzed to find out the important biomarkers. This study identified G6PD, KMO, MHD2 and NF-kB1 as key biomarkers. Beclin1 and Diablo associated with apoptosis are also useful biomarkers that could be important factors for hemolysis of high-risk drugs. For the risk prediction of drugs using the identified biomarkers, this study found that the precision for high-risk drugs is 56%, the precision for risk drugs is over 80%. When Beclin1 and Diablo are combined for prediction, sensitivity can be improved to higher than 40%. The prediction methods could be used to screen drug hemolytic risk.