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http://hdl.handle.net/10603/500210
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DC Field | Value | Language |
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dc.coverage.spatial | ||
dc.date.accessioned | 2023-07-19T11:25:39Z | - |
dc.date.available | 2023-07-19T11:25:39Z | - |
dc.identifier.uri | http://hdl.handle.net/10603/500210 | - |
dc.description.abstract | Triple 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.extent | xiv, 137 | |
dc.language | English | |
dc.relation | ||
dc.rights | university | |
dc.title | The Design and Development of Precision Drugs for Triple Negative Breast Cancer A Pharmacogenomic Approach | |
dc.title.alternative | ||
dc.creator.researcher | Hima Vyshnavi A M | |
dc.subject.keyword | Computer Science | |
dc.subject.keyword | Computer Science Interdisciplinary Applications; Kinase domain; Conserved region; Sensitive region; TNBC; SARS-CoV-2; Biomolecular Networking; Pharmacogenomics; Deep learning ; Docking | |
dc.subject.keyword | Engineering and Technology | |
dc.description.note | ||
dc.contributor.guide | Krishnan Namboori P K | |
dc.publisher.place | Coimbatore | |
dc.publisher.university | Amrita Vishwa Vidyapeetham University | |
dc.publisher.institution | Center for Computational Engineering and Networking (CEN) | |
dc.date.registered | 2017 | |
dc.date.completed | 2023 | |
dc.date.awarded | 2023 | |
dc.format.dimensions | ||
dc.format.accompanyingmaterial | None | |
dc.source.university | University | |
dc.type.degree | Ph.D. | |
Appears in Departments: | Center for Computational Engineering and Networking (CEN) |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 300.66 kB | Adobe PDF | View/Open |
02_preliminary page.pdf | 420.35 kB | Adobe PDF | View/Open | |
03_content.pdf | 52.14 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 264.82 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 58.04 kB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 218.48 kB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 940.66 kB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 1.85 MB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 5.61 MB | Adobe PDF | View/Open | |
10_chapter 6.pdf | 7.38 MB | Adobe PDF | View/Open | |
11_chapter 7.pdf | 4.95 MB | Adobe PDF | View/Open | |
12_chapter 8.pdf | 4.6 MB | Adobe PDF | View/Open | |
13_chapter 9.pdf | 76 kB | Adobe PDF | View/Open | |
14_annexure.pdf | 5.33 MB | Adobe PDF | View/Open | |
80_recommendation.pdf | 376.22 kB | Adobe PDF | View/Open |
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