Cancer researchers now can analyze hundreds of potential drug targets simultaneously through the use of "virtual experiments," according to a study published this week in the journal Nature Reviews Drug Discovery.
The researchers used an online integrated cancer database--dubbed canSAR--in conjunction with a new tool that allows researchers to compare 500 drug targets at the same time to uncover 46 potential "druggable" cancer targets out of 479 cancer genes. Paul Workman, one of the study's co-authors and deputy chief executive at The Institute of Cancer Research, believes the new method could save both time and money in the search for viable cancer drugs.
"It will empower scientists to select the very best targets that are most likely to lead to successful drugs, thereby increasing the success rate in the clinic," Workman said in an announcement. He added that it will shift the focus "away from the tried and tested drug targets."
Last fall, researchers used IBM's Blue Gene supercomputer to simulate a drug inhibiting a target enzyme known to spur the spread of pancreatic cancer. They hope their methods eventually lead to the development of anti-cancer drugs with fewer side effects.
What's more, researchers with the University of Queensland's Institute for Molecular Bioscience uncovered hard-to-find cancer treatment targets last spring using computers to sort through gene regulatory networks.
To learn more:
- here's the study's abstract
- read the accompanying announcement