Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/500210
Full metadata record
DC FieldValueLanguage
dc.coverage.spatial
dc.date.accessioned2023-07-19T11:25:39Z-
dc.date.available2023-07-19T11:25:39Z-
dc.identifier.urihttp://hdl.handle.net/10603/500210-
dc.description.abstractTriple Negative Breast Cancer (TNBC) is a lethal form of breast cancer in where three hormone receptors are negative. The proneness of disease, drug action, and side effects vary from person to person. This may be due to individual variations in the genome. The individual variation demands the design of a population-specific predictive, preventive, participatory and personalised (p4) pharmacogenomics drug molecule. Aim: The present work aims at designing a pharmacogenomic model for Triple Negative Breast Cancer (TNBC) to explain the individual variation in the proneness of the diseases and susceptibility towards drug action. In the present work, variants in kinase domain alone have been considered while making the target model protein molecules. The possibility of designing population-specific (customised) potential ligand molecules inhibiting the TNBC targets among South Asian populations has been excavated. Methods: The work involves the identification of candidate genes associated with TNBC. A patient-specific pharmacogenomic mutation detection system for triple-negative breast cancer (TNBC) using the images of protein expression analysis using a deep learning technique has been developed. The variant annotation has been performed to identify the genetic variants across the 1000 Genomes continental population . Domain identification of targets has been performed. The genetic signatures were incorporated in the wild sequence to generate the mutant protein sequences. The 3D structure of mutant proteins were modelled using homology modelling. The potential control drug molecules were identified by generating a Protein Drug Interaction Network between the approved anti-breast cancer drug molecules and the control targets and mutant target molecules. The network was further evaluated through molecular docking. The most suitable drug combinations were identified. Molecular evolution has been carried out to generate potential derivatives of control drug molecules. A ligand library has been created with..
dc.format.extentxiv, 137
dc.languageEnglish
dc.relation
dc.rightsuniversity
dc.titleThe Design and Development of Precision Drugs for Triple Negative Breast Cancer A Pharmacogenomic Approach
dc.title.alternative
dc.creator.researcherHima Vyshnavi A M
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Interdisciplinary Applications; Kinase domain; Conserved region; Sensitive region; TNBC; SARS-CoV-2; Biomolecular Networking; Pharmacogenomics; Deep learning ; Docking
dc.subject.keywordEngineering and Technology
dc.description.note
dc.contributor.guideKrishnan Namboori P K
dc.publisher.placeCoimbatore
dc.publisher.universityAmrita Vishwa Vidyapeetham University
dc.publisher.institutionCenter for Computational Engineering and Networking (CEN)
dc.date.registered2017
dc.date.completed2023
dc.date.awarded2023
dc.format.dimensions
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Center for Computational Engineering and Networking (CEN)

Files in This Item:
File Description SizeFormat 
01_title.pdfAttached File300.66 kBAdobe PDFView/Open
02_preliminary page.pdf420.35 kBAdobe PDFView/Open
03_content.pdf52.14 kBAdobe PDFView/Open
04_abstract.pdf264.82 kBAdobe PDFView/Open
05_chapter 1.pdf58.04 kBAdobe PDFView/Open
06_chapter 2.pdf218.48 kBAdobe PDFView/Open
07_chapter 3.pdf940.66 kBAdobe PDFView/Open
08_chapter 4.pdf1.85 MBAdobe PDFView/Open
09_chapter 5.pdf5.61 MBAdobe PDFView/Open
10_chapter 6.pdf7.38 MBAdobe PDFView/Open
11_chapter 7.pdf4.95 MBAdobe PDFView/Open
12_chapter 8.pdf4.6 MBAdobe PDFView/Open
13_chapter 9.pdf76 kBAdobe PDFView/Open
14_annexure.pdf5.33 MBAdobe PDFView/Open
80_recommendation.pdf376.22 kBAdobe PDFView/Open


Items in Shodhganga are licensed under Creative Commons Licence Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).

Altmetric Badge: