287715 An Innovative Computer-Aided Molecular Design Approach to the Rational Design of Novel Small Molecule Inhibitors of Amyloid-β Aggregation

Wednesday, October 31, 2012
Hall B (Convention Center )
Donald P. Visco Jr.1, Jui-Heng Tseng2, Deborah Soto-Ortega2, Chen Suo2, Jie Gao3, Shelby Chastain2, Brandon P. Murphy2, Mihyun Lim4, Fang Xie5, James Chapman3, Qian Wang5 and Melissa Moss2, (1)University of Akron, Akron, OH, (2)Chemical Engineering, University of South Carolina, Columbia, SC, (3)College of Pharmacy, University of South Carolina, Columbia, SC, (4)Department of Biological Sciences, University of South Carolina, Columbia, SC, (5)Chemistry and Biochemistry, University of South Carolina, Columbia, SC

The ‘amyloid cascade hypothesis’, linking aggregation of the amyloid-β protein (Aβ) to the pathogenesis of Alzheimer’s disease (AD), has led to the emergence of inhibition of Aβ aggregation as a therapeutic strategy for this currently unpreventable and devastating disease.  The identification of Aβ aggregation inhibitors has proceeded primarily through the screening of drug-like molecules, leading to the discovery of hundreds of aggregation inhibitors belonging to multiple classes of compounds.  However, only two small-molecule amyloid aggregation inhibitors have proven effective enough to advance to late clinical trials. 

A different strategy to identify drug-like molecules than database screening is to design molecules (from sub-fragments) that are predicted to have an ideal property profile.  This approach, called computer-aided molecular design (CAMD), has the potential to facilitate these drug discovery efforts.  As it relates to the development of Aβ aggregation inhibitors, a bottle-neck in the development of these inhibitors is the lack of a solved molecular structure for Aβ aggregates.  To that end, we propose the use of a novel, ligand-based approach to CAMD whereby the Signature molecular descriptor is used with appropriate quantitative structure-activity relationships (QSAR) in order to generate non-intuitive and novel structures without the need for detailed information about the drug target. 

Data from a diverse library of small molecules, including dihydropyridines, naphthalimides, coumarin analogs, and polyphenols, was employed as an input to this model.  Here, each compound was evaluated for its effect upon aggregation of Aβ monomer.  Aggregation was initiated by incubating monomeric protein in the presence of NaCl and under continuous agitation.  Progression from monomer to aggregates was evaluated using thioflavin T, a fluorescent dye that yields a shifted and enhanced fluorescence when bound to the β-sheet structure of amyloid aggregates.  Uninhibited aggregation exhibited a lag time, characteristic of nucleation, followed by a period of rapid growth, and culminating with a plateau as monomer and aggregate species reached equilibrium.  Compounds were characterized for both their ability to extend nucleation, indicated by a fold-increase in the lag time, and their ability to reduce the quantity of aggregate formed, indicated by a decrease in the fluorescence of the equilibrium plateau.  The evaluated compounds exhibited inhibitory capabilities ranging from no effect to greater than 4-fold extension of the lag time and more than 90% reduction of the equilibrium plateau.  These quantitative measures facilitated the development of informative structure-activity relationships (SARs) that were used to screen molecules generated from our CAMD approach.  To ensure drug-likeness of model outputs, this SAR input was further combined with screens for synthetic feasibility, molecular stability, and BBB penetration via Lipinski’s Rule.  Output from CAMD demonstrates the generation of novel compounds that optimize the best features of known inhibitors and also possess desirable characteristics for potential therapeutics.

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