Researchers develop method to rapidly ID optimal drug cocktails

"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."

Drug combinations can also be used effectively to inhibit infectious diseases because resistance to a single drug is very common, according to Ren Sun, UCLA professor of molecular and medical pharmacology and a member of the research team.

"If we can apply multiple drugs against one infectious agent, it probably will prevent the occurrence of drug resistance," said Sun, who is also a researcher at the Jonsson Cancer Center. "But, of course, when you use multiple drugs, side effects will be strong. With this model, there is a way to optimize the combination to reduce the side effects while maintaining efficacy that will be very beneficial."

"What the search scheme does is it tries to detect trends for optimal output," said Pak Wong, a former UCLA graduate student who participated in the study and is now an assistant professor of mechanical engineering at the University of Arizona. "Basically, the algorithm sees a trend and a direction and drives the trend in that direction. It's like mountain climbing and finding a way to get to the peak. So you keep going, and soon you rapidly find the peak while being guided by a smart search scheme."

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.