Insilico Molecular Docking and Pharmacokinetic Studies of Selected Phytochemicals With Estrogen and Progesterone Receptors as Anticancer Agent for Breast Cancer

Yıl 2018, Cilt: 5 Sayı: 3, 1337 - 1350, 01.09.2018

Sagir Ismail , Adamu Uzairu Balarabe Sagagi Muhd Sabiu Suleiman

https://doi.org/10.18596/jotcsa.449778

Cited By: 15

Öz

Molecular docking and pharmacokinetic
studies were carried out on 20 selected phytochemicals with estrogen and
progesterone receptors and it was found that all the phytochemicals have strong
binding energy and high number of interactions, Gabridin has the highest
binding energy of -10.3kcal/mol and 12 numbers of various interactions when
docked with estrogen receptor, while Quercetin has the highest binding energy
of -9.6kcal/mol and about 14 numbers of various interactions when docked with
progesterone receptor. Pharmacokinetic study carried out showed that all the
leading compound (Gabridin and Quaercetin) are in agreement with lipinski′s
rule of 5 as they does not violate any of the rule,  this shows that they will be readily
bioavailable. considering the high binding affinity of these compounds and
pharmacokinetic parameters, most of the phytochemicals used in this study can
be used in designing a highly pontent anti breast cancer drug.   

Anahtar Kelimeler

Phytochemicals, Estrogen, Progesterone, Gabridin, anticancer agent

Kaynakça

• 1. Ogundele AV, Otun KO, Ajiboye A, Olanipekun BE, Ibrahim RB. Anti-Diabetic Efficacy and Phytochemical Screening of Methanolic Leaf Extract of Pawpaw (Carica papaya) Grown in North Central Nigeria. Journal of the Turkish Chemical Society, Section A: Chemistry. 2017;4(1):99–114.
• 2. Arendt EK, Zannini E. Cereal grains for the food and beverage industries. Elsevier; 2013.
• 3. Pratheeshkumar P, Son Y-O, Korangath P, Manu KA, Siveen KS. Phytochemicals in cancer prevention and therapy. BioMed research international. 2015;2015.
• 4. Webb D. Phytochemicals’ Role in Good Health [Internet]. Today’s Dietitian. 2013 [cited 2018 Dec 4]. Available from: https://www.todaysdietitian.com/newarchives/090313p70.shtml
• 5. Arthur DE. Toxicity modelling of some active compounds against k562 cancer cell line using genetic algorithm-multiple linear regressions. Journal of the Turkish Chemical Society, Section A: Chemistry. 2017;4(1):355–374.
• 6. Nordqvist C. Breast cancer: Symptoms, causes, and treatment [Internet]. What you need to know about breast cancer. [cited 2018 Dec 4]. Available from: https://www.medicalnewstoday.com/articles/37136.php
• 7. Anonymous. Breast Cancer Treatment (PDB (R)). National Cancer Institute; 2014.
• 8. Anonymous. Breast Disorders, Breast cancer. Merck; 2014.
• 9. Ferdous S, Mirza MU, Saeed U. Docking studies reveal phytochemicals as the long searched anticancer drugs for breast cancer. International Journal of Computer Applications. 2013;67(25):1–5.
• 10. Toepak E, Tambunan U. In silico design of fragment-based drug targeting host processing α-glucosidase i for dengue fever. In: IOP Conference Series: Materials Science and Engineering. IOP Publishing; 2017. p. 012017.
• 11. Lumachi F, Santeufemia DA, Basso SM. Current medical treatment of estrogen receptor-positive breast cancer. World journal of biological chemistry. 2015;6(3):231.
• 12. Giulianelli S, Molinolo A, Lanari C. Targeting progesterone receptors in breast cancer. In: Vitamins & Hormones. Elsevier; 2013. p. 161–184. 13. Davis AM, Riley RJ. Predictive ADMET studies, the challenges and the opportunities. Current opinion in chemical biology. 2004;8(4):378–386.
• 14. Tang Y, Zhu W, Chen K, Jiang H. New technologies in computer-aided drug design: toward target identification and new chemical entity discovery. Drug discovery today: technologies. 2006;3(3):307–313.
• 15. Kadam R, Roy N. Recent trends in drug-likeness prediction: A comprehensive review of In silico methods. Indian Journal of Pharmaceutical Sciences. 2007;69(5):609.
• 16. Geldenhuys WJ, Gaasch KE, Watson M, Allen DD, Van der Schyf CJ. Optimizing the use of open-source software applications in drug discovery. Drug Discovery Today. 2006;11(3–4):127–132.
• 17. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced drug delivery reviews. 1997;23(1–3):3–25.
• 18. Abad-Zapatero C. Analysis of the Content of SAR Databases. In: Ligand Efficiency Indices for Drug Discovery [Internet]. Elsevier; 2013 [cited 2018 Dec 4]. p. 67–79. Available from: https://linkinghub.elsevier.com/retrieve/pii/B9780124046351000050
• 19. Lipinski CA. Lead-and drug-like compounds: the rule-of-five revolution. Drug Discovery Today: Technologies. 2004;1(4):337–341.