Analysts at the University of Washington in Seattle and others are predicting a breakthrough in drug development and a new class of medicines that can treat cancer in humans.

They say it could open the door to a better treatment for patients, but the researchers say the breakthrough will take years and the field will likely have to wait until next century.

But it could also help solve the mystery of how to get a cure for the disease.

“It will take time,” said Andrew J. Batson, a co-author of the report published Thursday in the journal Science.

“It will be a lot of work, but it’s possible.”

The discovery that Batson and colleagues have made in the last several years has led them to believe that a class of compounds called metabolomics networks can better detect the types of compounds that cause cancer.

The networks, based on computer modeling, are able to analyze hundreds of thousands of compounds at once and can predict the effects on specific organs, including the liver.

In the past, chemists have been able to do this by analyzing small samples of molecules in a laboratory, or by looking at individual molecules in living cells.

But the researchers’ method was limited to analyzing a single drug.

They believe the networks can do the same job.

The new approach, called dynamic network analysis (DNA), is much more precise, and it takes into account how the drug interacts with other molecules.

The researchers are now developing new tools that can analyze large amounts of a drug and create a graph of all its interactions.

The researchers say their methods can also be used to predict how drugs will affect human health and the environment, as well as how they might interact with other drugs.

For instance, they predict how a cancer drug will affect the body’s ability to make antibodies.

Batson and his colleagues say the network analysis method can be used in other fields, such as cancer screening and immune responses.

They also hope it will help develop new therapies.

“The network analysis model is a huge breakthrough in this field,” said lead author James O’Donnell, an associate professor of chemistry at UW.

“There are many other models that we have seen that are not doing the same thing.

This one does not do any of those things.

It does not predict a lot.”

For example, Batson said the network method does not provide a lot more information about the effects of a cancer therapy on the body.

The DNA method can provide more information, he said.

But he said the networks’ ability to identify cancer drugs is important because it may allow scientists to develop more effective therapies.

The drug, called Lefloxacin, is the most popular drug for treating cancer in the United States, according to the Drug Enforcement Administration.

It is currently on the market in Canada, Germany, the Netherlands, the United Kingdom, and South Africa.

The scientists are working on new drug designs that will take advantage of the networks, but they are also looking at ways to develop new compounds.

In one example, they are testing compounds that bind to a specific protein in a cancer cell called p53, and then targeting the protein with a specific molecule from the DNA database.

The compounds will then interact with the p53 protein and induce a protein response.

The results of this test are promising, said Batson.

“If we can get a drug that is active in this system, that is actually an amazing thing.”

The new method is not yet available in clinical trials.

But, O’Brien said, “it’s pretty exciting.”