Researchers using IBM's Blue Gene supercomputer have created a simulation to show on a quantum-mechanical level how a drug inhibits a target enzyme known to spur the spread of pancreatic cancer. They hope that an atom-by-atom understanding of the process will lead to anti-cancer drugs with fewer side effects, according to an article at Technology Review.
Researchers at IBM's Watson Research Center and others collaborated with peers in China on the work, published in the Proceedings of the National Academy of Sciences.
The potential drug involves a nanoparticle--82 carbon atoms creating a cage around a single atom of the heavy metal gadolinium. It typically is used as a contrast agent for medical imaging--in fact, it was used in the National Institutes of Health study on MRI use in heart catheterization--but it also has been shown to prevent cancer metastasis. The researchers in this study used the drug to show it could slow the spread of pancreatic cancer in mice by inhibiting enzymes called MMPs, which help tumors rewire their blood supply to gain more nutrients.
Using the supercomputer, the researchers not only could pinpoint the active spot where the drug binds to the enzyme, but also an alternative spot as well, a potential new target for anti-cancer drugs.
A better understanding of biology and more powerful computers will be required to fully model entire biological systems, according to Technology Review.
"We want to study all the roles a drug has in the body, including its absorption into and distribution through the bloodstream to the various tissues, its metabolism to other molecules, clearance and removal from the body, and the specific molecular events at its sites of action," Bruce Tidor, a computational biologist at MIT, told Technology Review. "This requires simulation models at many scales--from cell circuits to fluid dynamics to molecular modeling and quantum mechanics--to all be sewn together within the appropriate framework."
Meanwhile, researchers at Stanford and at Mount Sinai have developed computer algorithms to better predict drug interactions. At Mount Sinai, the algorithm aims to help scientists to better understand why different drugs produce certain side effects. The Stanford model allows doctors to differentiate between drug-related adverse events in patients and adverse events from another illness.