Structure dictates function. That is the mantra uttered by many a life scientist. In fact, there is a branch of life science called structural biology that is completely based on this mantra. To the pharmaceutical scientist, this mantra takes on a different meaning: the structure of an investigational new drug can impact its function. And the more planning and design that goes into a drug structure, the better its predicted function.
This is a major reason why so many pharmaceutical scientists use structural biology tools, and why they both attended and presented at the recent CHI conference on structural biology.
Receptos is using structural biology to get an accurate picture of the binding pocket of drug targets. The structural biology of a binding pocket can be used to determine if binding to particular residues can lead to optimal drug-target interactions and where (if any) modifications can be made to a drug to increase its interaction with its target.
Receptos uses standard structural biology tools to work on G-protein-coupled receptors (GPCRs). Because membrane proteins are difficult to crystallize, the company uses lipidic cubic phase to crystallize GPCRs, explained Michael Hanson, Ph.D., associate director of structural biology.
“One of the major problems of generating crystals of any membrane protein is that you need to satisfy the hydrophobic requirement of the protein. Prior to lipidic cubic-phase development, the only way you could do that was to solubilize the protein in a detergent micelle and then you would try to crystallize the protein and the detergent at the same time,” explained Dr. Hanson. The lipidic cubic-phase method involves inserting the GPCR back into a lipid membrane that is capable of supporting crystal growth.
Dr. Hanson went on to demonstrate how data collected from lipidic cubic phase can be applied to small molecule drug discovery. Receptos is currently using this structural biology tool to screen molecules against specific drug targets in a specific GPCR family.
“Structural biology is the approach of choice for these studies because it gives you a wealth of information that you can use to interpret all the other information that you are gaining through the more traditional routes such as cell-based assays and a structure-activity relationships that you are doing as part of the process,” noted Dr. Hanson.
“You can now interpret all that information in the context of the structure, and that tends to make a lot more sense and eliminates some of the dead ends that you would normally go down.” Structure-based drug discovery also reduces the amount of time and money that a company would traditionally encounter in drug development.
According to Dr. Hanson, just two years ago it was not possible to do structure-based drug design on GPCRs because the technology was not available. “We now have a rapid turnaround of structural information that will actually inform medicinal chemistry of a compound’s potential in less than one year.”
Dr. Hanson believes that this work could have a significant impact on the industry.
Emanuele Perola, Ph.D., research fellow I at Vertex Pharmaceuticals, presented a recently published study based on the binding efficiencies of 60 pairs of commercially available drugs and their originating leads. The goal of the study was to develop some guidelines on how to use binding efficiencies to help improve drug discovery programs.
A number of papers on binding efficiency have been published in recent years, but clear guidelines on the use of this metric had yet to be established. “I wanted to derive some useful lessons that could be applied prospectively in drug discovery programs,” said Dr. Perola, who added that “a critical part of the study consisted of mining the literature for data.”
Dr. Perola had to search a number of databases, examine numerous papers, and explore various other sources to retrieve the origin of each drug. The main data mined from these sources—the chemical structure of the drug and its parent compound as well as their binding affinities—was used in the meta-analysis.
Dr. Perola’s paper was published in the Journal of Medicinal Chemistry earlier this year. Although it is a bit premature to judge the paper’s impact on the pharmaceutical community, Dr. Perola said that he thinks the study will help people at different stages of drug discovery programs—from the selection of viable lead series to the development of effective lead optimization strategies.