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Identification of Therapeutic Targets and Screening of Natural Compounds For Treating Chronic Obstructive Pulmonary Disease (COPD)

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dc.contributor.author Khalid, Rimsha
dc.date.accessioned 2022-07-04T09:48:33Z
dc.date.available 2022-07-04T09:48:33Z
dc.date.issued 2020-04-23
dc.identifier.other RCMS003334
dc.identifier.uri http://10.250.8.41:8080/xmlui/handle/123456789/29788
dc.description.abstract Chronic Obstructive Pulmonary disease is indeed the third largest leading cause of mor- tality, and it’s becoming a serious worldwide health issue. According to research studies, 3.1 million people have died every year from COPD, whereas 65.3 million suffer from it. COPD is characterized by progression of the airflow limit, which is inefficiently reversible and damages the peripheral airways. COPD is frequently linked with comorbidities, re- quiring more medical attention. Aside from multiple exposures, other genetic defects associated with the disease’s occurrence have yet to be uncovered, and therefore, there is no known cure. In the case of COPD, when pulmonary cells break down inside the lungs, there is still to be discovered a way to stop this ongoing deterioration and im- prove the damage. In the research study, with the help of NGS analysis 10 differentially expressed genes that were common in the datasets have been identified. Out of which 3 genes CAP1, PPABPC4, and SLC2A1 had linked with the progression of COPD. ABL in ROBO-SLIT signalling, Defective SLC2A1 causes GLUT1 deficiency syndrome 1 (GLUT1DS1), Post-transcriptional silencing and insulin resistance are some of the common pathways in which all these genes are involved. These 3 genes were further docked with the natural compounds. The 3 genes showed the highest affinity with Aca- cia Arabica which has lots of medicinal properties for treating respiratory diseases. The PLIP analysis further confirmed the interactions and highest binding affinity of SLC2A1 with Acacia Arabica. Further analysis carried on the compound proved that it can be used as a potent drug against COPD.In addition, a machine learning pipeline was built to forecast all probable interactions. en_US
dc.description.sponsorship Dr. Rehan Zafar Paracha en_US
dc.language.iso en_US en_US
dc.publisher SINES NUST en_US
dc.subject Identification of Therapeutic Targets en_US
dc.title Identification of Therapeutic Targets and Screening of Natural Compounds For Treating Chronic Obstructive Pulmonary Disease (COPD) en_US
dc.type Thesis en_US


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