A Computational Model of an Anticancer Nanoparticle
Researchers have used computational modeling to precisely simulate how a drug inhibits a target enzyme known to spur cancer’s spread, capturing the interaction at the quantum-mechanical level. They hope their work will uncover a way to specifically inhibit members of a whole class of cancer-linked proteins without causing as many side effects as existing drugs.
The study, published this week in the Proceedings of the National Academy of Sciences, is one of many cases in which researchers have used computers to build simulations or models of how drugs interact with their biological targets, hoping that an atom-by-atom understanding of a drug’s effects could help them improve the compound or even design totally new ones.
The potential drug in this week’s study is a nanoparticle: 82 carbon atoms in the shape of a so-called buckyball that forms a cage enclosing a single atom of a heavy metal called gadolinium. It was originally developed as a contrast agent for use in medical imaging, but researchers (some of whom are authors on the new study) had shown that it can also prevent cancer metastasis. In the new study, the authors showed that the particle can decrease the spread of pancreatic cancer in mice by inhibiting enzymes called MMPs, which help tumors reëngineer blood vessels to supply themselves with nutrients.
To capture the effects of the heavy metal ion on the nanoparticle and the enzyme, the researchers needed to examine the quantum mechanics of the interaction. That required a supercomputer—in this case, IBM’s Blue Gene. “These kinds of calculations are very intensive,” says Ruhong Zhou, a biophysicist with IBM’s Watson Research Center and senior author on the study. “You need to have a lot of computational power.”
With the computer’s help, the team identified the exact location where one of the enzymes, MMP-9, sticks to the nanoparticle. The model also predicted that the nanoparticle might clump together before interacting with MMP-9, and the authors were able to demonstrate that it does form clusters in aqueous solutions. Using molecular modeling to identify where a drug binds its target protein is not new, but the authors uncovered a previously unknown spot—one outside the enzyme’s active site. Identifying this unique site gives drug designers “a new target for future anticancer drug development,” says Zhou. Drugs that inhibit an enzyme by blocking its active site often cause side effects because the inhibition is not very specific. Drugs that act on the newly discovered site could avoid that problem.
The study illustrates what modeling can do now: give drug designers more insight into what is happening in their test tubes. But as computational power increases and researchers develop a better understanding of molecular interactions, modeling may become even more powerful.
“New machines allow us … to predict what is likely to happen in an experiment,” says Jerome Baudry, a biophysicist at the University of Tennessee, Knoxville, and the Oak Ridge National Laboratory. “We cannot find the needle in the haystack, but we can reduce the size of the haystack a lot, saving a lot of time and money.”
While the recent work is a significant step forward in using computer simulation for insight into how a potential drug interacts with its biological target, it still falls far short of many researchers’ ultimate goal in developing computer simulations: modeling entire biological systems in silico. That won’t happen until researchers learn more about the fundamental physics of biology—and until processing power increases still further.
“High-performance supercomputing is becoming especially important in the study of large, complex, multiscale systems,” says Bruce Tidor, a computational biologist at MIT. “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. 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. This is going to be important for drug discovery in the future and will require supercomputing to do well.”
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