A MULTIPLEXED IN VIVO ASSAY OF SMALL MOLECULE STRUCTURE SPACE

The set of small organic compounds that binds to a given target is frequently conceptualized as a subset of “chemical space” or “structure space”. In this language, locating the subset of structure space that contains inhibitors for a given protein is one of the principle aims of drug development. Navigating structure space is complicated due to the fact that currently there is no widely accepted coordinate system. Computational methods to define structural binding motifs have proven powerful but are limited by the accuracy of the underlying theory, the quality of the parameter sets, and computational power. Very often these calculations leave out important aspects of the system such as the energy of solvation. Recently, an alternative approach toward defining structure space using empirical methods has been reported. In this system, the coordinates of a molecule in structure space can be defined by the in vitro binding affinity of that molecule to a reference panel of proteins. This approach has demonstrated that even a reference panel of less than twenty proteins can accurately span structure space. Despite these encouraging results, definition of structure space is not widely used, partly because the existing assay is resource intensive and laden with the potential for artifacts. Consequently, development of a new empirical approach toward defining structure space would be advantageous.

UCSF investigators have developed a novel approach to defining structure space using a unique in vivo assay. This assay provides a high-throughput screening method that can be used to detect a ligand’s location in structure space. The data resulting from this assay can be used in conjunction with computer clustering algorithms to find new biologically active small molecules based on limited structure-activity relationship (SAR) data. This technology can be used to:

• multiplex current assays into a single vessel format, thus dramatically increasing assay throughput
• convert between different molecular scaffolds in order to optimize factors such as toxicity, bioavailability, and synthetic accessibility
• distinguish between biological targets in the same family
• assess in silico predictions with experimental results

If you would like to receive further information about this technology and potential licensing opportunities, please contact:

Michael Karasik
Administrative Manager
(415) 353-4472 phone
(415) 348-1579 fax
michael.karasik@ucsf.edu

Reference: OTM Case #SF01-099