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Biomedical subjects

Doug W Hobbs

Publications and source records attributed to Doug W Hobbs.

6 recordsLinked to original sources
PubMed2006

Development of BACE1 inhibitors for Alzheimer's disease.

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.

Alzheimer Disease↗
PubMed2005

Discovery and SAR of 4-amino-2-biarylbutylurea MCH 1 receptor antagonists through solid-phase parallel synthesis.

-4-Amino-2-arylbutylbenzamides such as 1 were identified as micromolar MCH 1 receptor (MCH1R) antagonists via screening using a scintillation proximity assay based on [125I]-MCH binding to recombinant, human MCH1R. Subsequent lead optimization efforts using solid-phase parallel synthesis resulted in the defined structure-activity relationships and the identification of 4-amino-2-biarylbutylureas, such as 11g, as potent single digit nanomolar MCH1R antagonists.

Combinatorial Chemistry Techniques↗
PubMed2004

Deriving knowledge through data mining high-throughput screening data.

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.

Algorithms↗
PubMed2003

Privileged structure-based combinatorial libraries targeting G protein-coupled receptors.

Combinatorial chemistry has become a key component of today's drug discovery process. Privileged structures, with their inherent affinity for diverse biological receptors, represent an ideal source of core scaffolds and capping fragments for the design and synthesis of combinatorial libraries targeted at various receptors. GPCRs-distributed widely in the body and involved in many physiological and pathophysiological processes-have been historically among the most popular targets for drug discovery. Numerous privileged structure-based combinatorial libraries have been designed and synthesized, and these libraries have proved to be an extremely powerful tool to aid the rapid discovery and optimization of potent and selective ligands for a wide variety of GPCR targets. This review focuses on recent developments in applying privileged structure-based combinatorial libraries for the discovery and optimization of GPCR ligands and critically evaluates the advantages of the various types of GPCR-targeted libraries.

Combinatorial Chemistry Techniques↗