Browsing by Author "Bvunzawabaya, Jonathan Tatenda"
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- ItemOpen AccessStructure-based in silico identification of novel anticancer natural compounds for enhanced breast cancer chemotherapy(Kamuzu University of Health Sciences, 2022-01-01) Bvunzawabaya, Jonathan TatendaBreast cancer remains a serious public health concern all over the world with heavier burdens on developing countries. In Sub-Saharan Africa, the mortality rate for breast cancer is currently on the rise because of late diagnosis and poor treatment. Currently available chemotherapeutic drugs for breast cancer are associated with severe side effects and are now facing multi-drug resistance. Therefore, identifying new anti-cancer drugs by using structural information of potential drug targets such as the gamma secretase will enhance the fight against breast cancer. The objective of this study was to identify and characterize drug-like natural anti-breast cancer compounds from selected African natural products databases using structure-based virtual screening and chemoinformatic approaches. Exactly11304 compounds from four databases (Afrodb, NANPDB, Afrocancer, and ConmedNP) were curated and filtered to remove structural alerts and compounds that violate drug-like rules according to Lipinski and Veber, using KNIME analytics. The resulting druglikeNP dataset (437 compounds) had its, chemical space, scaffold diversity, and complexity analyzed using scaled Shannon entropy and cyclic system retrieval curves (CSR). The 437 compounds were docked into the binding site of gamma-secretase enzyme and the pharmacokinetic properties of the hit compounds were profiled using the pkCSM server. 60% of the compounds in the druglikeNP dataset contained lead-like physicochemical properties and occupied the same space as FDA-approved drugs. The scaffold diversity of druglikeNP was observed to be higher than that of FDA drugs based on Shannon entropy and CSR. Docking studies identified 12 compounds as potential inhibitors of the gamma-secretase enzyme (with binding energies ranging between -7.6 and -8.8 KCal/mol). In silico ADMET predictions revealed a majority of the 12 hit compounds have good pharmacokinetic and toxicity profiles. In this study drug-like natural compounds of African origin with potential inhibitory properties against a validated breast cancer target; gamma secretase enzymes were computationally identified. This work could be valuable to the ongoing efforts to discovery novel drugs for enhanced breast chemotherapy.