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Ali, Junaid
- An In-SILICO Modeling for Molecular Drug Designing that Patient Derived Anti-Cancerous Cells
Authors
1 Centre of Agricultural Biochemistry & Biotechnology, The University of Agriculture Peshawar, PK
2 Department of Plant Breeding and Genetics, The University of Agriculture Peshawar, PK
3 Department of Entomology-The University of Agriculture Peshawar, PK
4 Department of Plant Pathology, The University of Agriculture Peshawar, PK
Source
Biometrics and Bioinformatics, Vol 11, No 4 (2019), Pagination: 60-64Abstract
Glioblastoma Multiform (GBM) is an aggressive disease associated with poor survival. It is essential to account for the complexity of GBM biology to improve diagnostic as well as therapeutic strategies. This complexity is best represented by the increasing amounts of profiling (“OMICS”) data available due to advances in biotechnology. Brain metastases remain a daunting adversary that negatively impact patient survival. Metastatic brain tumors affect up to 45% of all cancer patients with systemic cancer and account for ~20% of all cancer-related deaths. The brain micro-environment modulates metastatic tumor growth; however, defining the precise genetic events that promote metastasis in the brain niche represents an important, unresolved problem. Understanding these events will reveal disease-based targets and offer effective strategies to treat brain metastases. Effective therapeutic strategies based upon the biology of brain metastases represent an urgent, unmet need with immediate potential for clinical impact. In-SILICO docking method have been used to solve Glioblastoma (GBM) problem. Mostly CDK’s (Cyclin-Dependent Kinase) protein causes the severe Brain Tumor in human being. In this article strong inhibitor are used to block the action of CDK’s. This protein is encoded by the human genome to cause the initial tumor cells in brain & in the later this cause the death. But now it is possible to treat easily by using the effective inhibitor against brain tumor. This methodology is used to check out the protein-ligand binding affinity that shows the results against brain tumor and their ability to control the disease effectively. Among the drug-mutation associations reported in the Garnett study, this In-SILICO model accurately predicted ~85% of the associations. While testing the model in a prospective manner using simulations of patient-derived GBM cell lines. This study will also help to find out causes and the treatments on clinical bases.
