Washington, March 18 : Researchers at the University of California - Los Angeles have developed a feedback control system that can look for the most effective drug combinations to treat a variety of conditions, including cancers and infections.
Researchers say that their findings could play an important role in aiding new clinical drug-cocktail trials.
Drug cocktails are best used in the fight against HIV, the virus that causes AIDS and also have been used to fight several types of cancer. ften, drugs that might not be effective in combating diseases individually do much better in combination.
With the use of the new closed-loop feedback control scheme, an approach guided by a stochastic search algorithm, researchers at the UCLA Henry Samueli School of Engineering and Applied Science and UCLA's Jonsson Comprehensive Cancer Center have devised an important means of identifying potent drug combinations fast and efficiently.
"With the development of this optimization method, we've overcome a major roadblock," said study author Chih-Ming Ho, UCLA's Ben Rich-Lockheed Martin Professor and a member of the National Academy of Engineering.
"There have always been too many choices and too many combinations to sort through. It was like finding a needle in a haystack," he added.
In one test case, the research team examined how to best prevent a viral infection of host cells. Using the closed-loop optimization scheme, they were able to identify, out of 100,000 possible combinations, the drug cocktails that completely inhibited viral infection after only about a dozen trials.
In addition, they found that total inhibition of the virus occurred at much lower drug doses than would be necessary if the drugs were used alone; in fact, the concentrations of the drugs were only about 10 percent of that required when used individually.
"Viruses grow very rapidly and change rapidly as well. Because of that, a virus can become resistant to a particular drug," said Genhong Cheng, a member of the research team at the UCLA Center for Cell Control and UCLA's Jonsson Comprehensive Cancer Center.
"This is why it's so important to be able to use a combination of more than one drug. If the virus mutates to become resistant to one drug, it is still sensitive to the other drugs," he added.
In an example used to illustrate the prevention of viral infection of host cells, researchers started with arbitrarily chosen dosages of the drugs. The percentage of non-infected cells under this initial drug-cocktail treatment was fed into the stochastic search algorithm, which essentially helps guide a random search process.
The algorithm then suggested the next drug concentrations for producing a higher percentage of non-infected cells. This closed-loop feedback control scheme is carried out continuously until the best combination is found. Randomness is built into the search decision, preventing the trap at local optimum levels and allowing the search process to continue until the optimal drug cocktail is identified.
The model also provides an alternative approach to studying cellular functions. Molecular biologists can identify all the players of a particular regulatory pathway in order to decipher how to block or augment that pathway. Cells are complex systems with many redundant functions, and it is difficult to predict how a cell will respond to multiple stimulations at one time.
The model overlooks these details and lets the system determine what works best for itself. If researchers are more interested in how the cellular network functions, this approach can provide an initial bird's-eye view, but it also allows them to home in on the important molecular activities controlled by the best drug combinations.
The findings appear in the March 17 online version of the journal Proceedings of the National Academy of Sciences.