Biography:
Dr. Khanfar is an assistant professor in the Faculty of Pharmacy at University of Jordan since October 2011. He received his undergraduate degree with first class honors in Pharmacy at the University of Jordan. He received his PhD in 2010 from the University of Louisiana at Monroe working on the computer-assisted design and discovery of actin polymerization and GSK-3β inhibitors based on marine natural products. Dr. Khanfar completed postdoctoral research at the Center of Molecular Innovation and Drug Discovery (CMIDD), Northwestern University, IL where he conducted research in the laboratory of Professor Richard B. Silverman (the inventor of the blockbuster drug LyricaTM) on the design and synthesis of potential new therapeutics for the treatment of the neurodegenerative disorder Huntington�s disease. Dr. Khanfar's research is currently funded by several national grants and he has been a recipient of several awards and distinctions for his work on drug design and discovery.
Dr. Khanfar's research involves the use of state of the art organic and medicinal chemistry techniques combined with chemical biology approaches to design, synthesize and evaluate new molecules for the treatment of human disease and to probe biological systems with a particular emphasis on cancer and neurodegenerative diseases.
Dr. Khanfar's research also focuses on computational and theoretical approaches to understanding protein-ligand interactions using ligand-based and structure-based drug design and discovery approaches. His research team uses the many existing methods of computer-aided drug design, such as multidimensional QSAR, docking, conformational analysis, and pharmacophore modeling for understanding drug action and to design and discover new therapeutics,
e.g., inhibitors of glycogen synthase kinase 3 beta (GSK-3β), mammalian Target of Rapamycin (mTOR), and NAD-dependent deacetylase sirtuin-2. Dr. Khanfar's research group aims to improve human health through improved understanding of existing drug action and design of new drugs against cancer, and neuronal diseases.
Abstract:
New Virtual Methodology Identified New mTOR Inhibitors as Potential Anticancers.
The mammalian target of rapamycin (mTOR) has important role in cell growth, proliferation, and survival. mTOR is frequently hyperactivated in cancer, and therefore, it is clinically validated target for cancer therapy. Exhaustive pharmacophore modeling was combined with quantitative structure-activity relationship (QSAR) analysis to explore the structural requirements for potent mTOR inhibitors employing 210 known mTOR ligands. Genetic function algorithm (GFA) coupled with k nearest neighbor (kNN) and multiple linear regression (MLR) analyses were employed to build self-consistent and predictive QSAR models based on optimal combinations of pharmacophores and physicochemical descriptors. Successful pharmacophores were complemented with exclusion spheres to optimize their receiver operating characteristic curve (ROC) profiles. Optimal QSAR models and their associated pharmacophore hypotheses were validated by identification and experimental evaluation of several new promising mTOR inhibitory leads retrieved from the National Cancer Institute (NCI) database, our in-house-built database of established drugs, and our in-house-built database of agrochemicals. Six nanomolar to low micromolar drugs were identified as mTOR inhibitors, namely, glyburide, metipranolol, sulfamethizole, glipizide, pioglitazone, and sotalol. Remarkably, we discovered that the olive
oil-derived oleocanthal and the olive leaf-derived oleuropein have potent mTOR inhibitory activities. The most potent hit was captured from NCI database with IC50 value of 48 nM. Lineweaver-Burk plot showed that oleuropein inhibits mTOR via mixed competitive/noncompetitive mechanism suggesting that inhibition is mediated, at least partially, by covalent bonding to mTOR binding pocket. Our findings strongly suggest that mTOR inhibition is at least one of the reasons for the reported anti-cancer properties of oleuropein and oleocanthal.