FimH-mediated cellular adhesion to mannosylated proteins is critical in the ability of uropathogenic E. coli (UPEC) to colonize and invade the bladder epithelium during urinary tract infection. We describe the discovery and optimization of potent small-molecule FimH bacterial adhesion antagonists based on α-D-mannose 1-position anomeric glycosides using X-ray structure-guided drug design. Optimized biaryl mannosides display low nanomolar binding affinity for FimH in a fluorescence polarization assay and sub micromolar cellular activity in a hemagglutination (HA) functional cell assay of bacterial adhesion. X-ray crystallography demonstrates that the biphenyl moiety makes several key interactions with the outer surface of FimH including π-π interactions with Tyr-48 and an H-bonding electrostatic interaction with the Arg-98/Glu-50 salt-bridge. Dimeric analogs linked through the biaryl ring show an impressive 8-fold increase in potency relative to monomeric matched pairs and represent the most potent FimH antagonists identified to date. The FimH antagonists described herein hold great potential for development as novel therapeutics for the effective treatment of urinary tract infections.
Alzheimer's disease (AD) is a progressive neurodegenerative disease and the most common cause of dementia. The production and accumulation of beta-amyloid peptides (Abeta) from the beta-amyloid precursor protein (APP) are believed to play a key role in the onset and progression of AD. BACE1 (beta-site APP cleaving enzyme 1) is the protease responsible for the N-terminal cleavage of APP leading to the production of Abeta peptides and the development of BACE1 inhibitors as potential therapeutic agents for AD has generated tremendous interests from both academia and the pharmaceutical industry. A wide variety of BACE1 inhibitors have been reported, several of which have demonstrated highly promising efficacy in animal models of AD. This review focuses on recent disclosures of BACE1 inhibitors in the patent and scientific literature, covering the period from approximately May 2004 to November 2005.
In recent years the trend in combinatorial library design has shifted to include target class focusing along with diversity and drug-likeness criteria. In this manuscript we review the computational tools available for target class library design and highlight the areas where they have proven useful in our work. The protein kinase family is used to illustrated structure-based target class focused library design, and the G-protein coupled receptor (GPCR) family is used to illustrate ligand-based target class focused library design. Most of the tools discussed are those designed for libraries targeted to a single protein and are simply applied "brute-force" to a large number of targets within the family. The tools that have proven to be the most useful in our work are those that can extract trends from the computational data such as docking and clustering or data mining large amounts of structure activity or high throughput screening data. Finally, areas where improvements are needed in the computational tools available for target class focusing are highlighted. These areas include tools to extract the relevant patterns from all available information for a family of targets, tools to efficiently apply models for all targets in the family rather than just a small subset, mining tools to extract the relevant information from the computational absorption, distribution, metabolism, excretion and toxicity (ADMET) and targeting data, and tools to allow interactive exploration of the virtual space around a library to facilitate the selection of the library that best suits the needs of the design team.
The first nonpeptide antagonists of the neurohypophyseal hormone, oxytocin (OT) are described. Derivatives of the spiroindenepiperidine ring system, these compounds include L-366,509, an orally bioavailable OT antagonist with good in vivo duration. The potential use of these agents for treatment of preterm labor and their significance as new nonpeptide ligands for peptide receptors are discussed.
Deriving general knowledge from high-throughput screening data is made difficult by the significant amount of noise, arising primarily from false positives, in the data. The paradigm established for screening an encoded combinatorial library on polymeric support, an ECLiPS library, has a significant amount of built-in redundancy. Because of this redundancy, the resulting data can be interpreted through a rigorous statistical analysis procedure, thereby significantly reducing the number of false positives. Here, we develop the statistical models used to analyze data from high-throughput screens of ECLiPS libraries to derive unbiased true hit rates. These hit rates can also be calculated on subsets of the collection such as those compounds containing a carboxylic acid or those with molecular weight below 350 Da. The relative value of the hit rate on the subset of the collection can then be compared to the overall hit rate to determine the effect of the substructure or physical property on the likelihood of a molecule having biological activity. Here, we show the effects that various functional groups and the standard physical properties, molecular weight, hydrogen bond donors, hydrogen bond acceptors, log P, and rotatable bonds, have on the likelihood of a compound being biologically active. To our knowledge this is the first published account of the use of high-throughput screening data to elucidate the effects of physical properties and substructures on the likelihood of compounds showing biological activity over a broad range of pharmaceutically relevant targets.
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