Open Access Open Access  Restricted Access Subscription or Fee Access

In Silico Drug Designing of Breast Cancer in Homo sapiens

Srishti Singh

Abstract


Breast cancer is a severe health concern in India, with the highest mortality rate among women. It is brought on by uncontrolled cell division and has the potential to spread to other body regions. As a transcription factor, interactions with estrogen receptor (ER) alpha are primarily responsible for malignant tumors through regulating the transcription of numerous genes. Because most of the medications now used to treat breast cancer have significant side effects, we concentrated on natural chemicals that have no detrimental impact on normal human cells. The specific type of breast cancer can affect either one breast or both breasts. In the body, cancer develops when cells multiply uncontrollably and infiltrate surrounding tissues. Breast cancer can strike either a man or a woman. Most breast lumps are benign, meaning they do not spread or result in cancer. The risk of developing cancerous cells may occasionally increase as a result of these benign breast lumps. This project has been done to find the most suitable drug for breast cancer. Here, we have used in silico technique to find the most suitable drug. First of all, we have selected the target protein responsible for breast cancer with the help of the KEGG pathway database and then we found its isoforms using PDB (protein data bank). Secondly, we have selected three market drugs responsible for curing breast cancer and then we have docking of the market drugs and ligands with the protein isoforms by using Molegro Virtual Docker. After docking, we can evaluate the energy and find the best drug from our results. The ligand with the highest minimum energy will be considered the best one to use in the future

Keywords


Breast cancer, databases, drug discovery, KEGG pathway, molecular docking

Full Text:

PDF

References


Wadood A, Ahmed N, Shah L, Ahmad A, Hassan H, Shams S. In-silico drug design: An approach which revolutionarised the drug discovery process. OA Drug Design & Delivery. 2013; 1(1): 1–3.

Fosgerau K, Hoffman T. Peptide therapeutics: Current status and future directions. Drug Discovery Today. 2015; 20(1): 122–128.

Ciemny M, Kurcinski M, et al. Protein-peptide docking: Opportunities and challenges. Drug Discovery Today. 2018; 23(8): 1530–1537.

National Cancer Institute. Breast Cancer Treatment [Online] Available from https://www.cancer.

gov/types/breast/patient/breast-treatment-pdq#section/all.

Saunders C, Jassal S. Breast Cancer. Oxford: Oxford University Press; 2009.

American Cancer Society. What is breast cancer? [Online] Available from https://www.cancer.

org/cancer/breast-cancer/about/what-is-breast-cancer.html.

Anzhelika Mitsuk. (2016). Breast cancer information for young women. [Online] Available from https://www.theseus.fi/bitstream/handle/10024/123683/thesis%2020.12%20new.pdf?sequence=1.

Mayo Clinic. Breast Cancer. [Online] Available from https://www.mayoclinic.org/diseases-conditions/breast-cancer/symptoms-causes/syc-20352470.

American Cancer Society. What causes breast cancer? [Online] Available from https://www.cancer.org/cancer/breast-cancer/about/how-does-breast-cancer-form.html.

Kamińska M, Ciszewski T, Łopacka-Szatan K, Miotła P, Starosławska E. Breast cancer risk factors. Przeglad Menopauzalny Menopause Review. 2015; 14(3): 196–202.

Ban KA, Godellas CV. Epidemiology of breast cancer. Surg Oncol Clin N Am. 2014; 23:

–422.

Francken AB, Schouten PC, Bleiker E, et al. Breast cancer in women at high risk: The role of rapid genetic testing for BRCA1 and -2 mutations and the consequences for treatment strategies. Breast. 2013; 22: 561–568.

Antoniou A, Pharoah PD, Narod S, et al. Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case series unselected for family history: A combined analysis of 22 studies. Am J Hum Genet. 2003; 72: 1117–1130.

Thiébaut AC, Kipnis V, Chang SC, et al. Dietary fat and postmenopausal invasive breast cancer in the National Institutes of Health-AARP diet and health study cohort. J Natl Cancer Inst. 2007; 99(6): 451–462.

Saxe GA, Rock CL, Wicha MS, et al. Diet and risk for breast cancer recurrence and survival. Breast Cancer Res Treat. 1999; 53: 241–253.

Inic Z, Zegarac M, Inic M, Markovic I, Kozomara Z, Djurisic I, Inic I, Pupic G, Jancic S. Difference between luminal A and luminal B subtypes according to Ki-67, tumor size, and progesterone receptor negativity providing prognostic information. Clinical Medicine Insights: Oncology. 2014; 8: 107–111.

KEGG Pathway Database. [Online] Available from https://www.genome.jp/kegg/pathway.html.

wwPDB Consortium. Protein Data Bank: the single global archive for 3D macromolecular structure data. Nucleic Acids Research. 2019; 47 (D1): 520–528.

Molexus. Molegro Virtual Docker. Available at http://molexus.io/molegro-virtual-docker/#:~:text=Molegro%20Virtual%20Docker%20is%20an,binding%20modes%20of%20the%20ligands.

In Silico Drug Designing. (2019). Lipinski rule of five [Online] Available from sciencedirect.

com/topics/pharmacology-toxicology-and-pharmaceutical-science/lipinskis-rule-of-five#:~:text=

Traditionally%2C%20therapeutics%20have%20been%20small,P%20not%20greater%20than%

.


Refbacks

  • There are currently no refbacks.