Conventional test methods screen compounds in animal studies in advance of human trials in the hope of identifying the side effects of promising therapeutics. A team at the University of California, San Diego, led by Philip Bourne, Ph.D., professor of pharmacology and Lei Xie, Ph.D., of the San Diego Supercomputer Center at UCSD instead uses the power of computational modelling to screen specific drug molecules using a worldwide repository, the Protein Data Bank (PDB), containing tens of thousands of three-dimensional protein structures.

To identify which proteins might be unintended targets, the UCSD researchers take a single drug molecule and look for how it might bind to as many of the proteins encoded by the human proteome as possible. In this published case study, they looked at Select Estrogen Receptor Modulators (SERMs), a class of drug that includes tamoxifen. “The computer procedure we developed starts with an existing three-dimensional model of a pharmaceutical, showing the structure of a drug molecule bound to its target protein; in this case, the SERM bound to the oestrogen receptor,” said Bourne, who is co-director of the PDB. The scientists then use computer analysis to search for other binding sites that match that drug binding site – like looking for other locks that can be opened by the same key. In this study, the team found a previously unidentified protein target for SERMs. The identification of this secondary binding site explains known adverse effects, and opens the door to modifying the drug in a way that maintains binding to the intended target, but reduces binding to the second site.

Bourne explained that using this computational technique to find another “lock” could result in one of three things: the new lock might show no effect; the lock could explain an adverse side effect of the drug; or the research could potentially discover a new therapeutic effect for an existing drug – drug repositioning.

MEDICA.de; Source: University of California, San Diego