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Current Protein & Peptide Science

Editor-in-Chief

ISSN (Print): 1389-2037
ISSN (Online): 1875-5550

Review Article

Natural Products as Anticancerous Therapeutic Molecules with Special Reference to Enzymatic Targets Topoisomerase, COX, LOX and Aromatase

Author(s): Swati Singh, Manika Awasthi, Veda P. Pandey and Upendra N. Dwivedi*

Volume 19, Issue 3, 2018

Page: [238 - 274] Pages: 37

DOI: 10.2174/1389203718666170106102223

Price: $65

Abstract

Cancer, characterized by uncontrolled growth and proliferation of cells, is affecting millions of people every year and estimated as the second leading cause of death. Its successful treatment yet remains a challenge due to the lack of selectivity, toxicity and the development of multidrug resistant cells to the currently available drugs. Plant derived natural products hold great promise for discovery and development of new pharmaceuticals against cancer as evident by the fact that out of 121 drugs prescribed for cancer treatment till date, 90 are derived from plant sources. Furthermore, the plant derived therapeutic molecules are also considered as safer substitutes to those of synthetic ones. In this review, the therapeutic potentials of plant derived natural products belonging to secondary metabolites, namely alkaloids, flavonoids and terpenoids as anticancer molecules, involving various strategies of treatment, have been discussed with special reference to topoisomerases (Topo), cyclooxygenases (COX), lipoxygenase (LOX) and aromatase as enzymatic targets for various types of cancers. Furthermore, in view of the recent advances made in the field of computer aided drug design, the present review also discusses the use of computational approaches such as ADMET, molecular docking, molecular dynamics simulation and QSAR to assess and predict the safety, efficacy, potency and identification of such potent anticancerous therapeutic molecules.

Keywords: Cancer, natural products, topoisomerases, cyclooxygenases, lipoxygenase, aromatase, molecular docking and dynamics simulation, QSAR.

Graphical Abstract


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