New algorithm dramatically increases the speed of identifying two cancer drugs that work synergistically
An algorithm that can speed up by years the ability to identify from among thousands of possibilities, two or more drugs that work synergistically against a problem like cancer or a viral infection has been developed by bioinformatics experts.
The new algorithm enables investigators to use large existing databases with information about how one cancer drug changed the gene expression of a particular breast cancer cell line, and how well it killed the cell, then mathematically combine those results with the impact of another drug to see if they could work better together, says Dr. Richard McIndoe, director of the Center for Biotechnology and Genomic Medicine at the Medical College of Georgia.
While the algorithm does not immediately make available the kind of information that would set a clinical trial in motion, it does speed up the path to the trials, he says.
“The idea is we ultimately want to find these synergistic drug combinations that will hopefully help patients with cancer,” McIndoe says. “For researchers it becomes a particularly faster way to find those synergistic combinations, without having to screen one drug at a time, which is really not feasible.”
Drug combination therapies can improve drug efficiency, reduce drug dosage (and related toxicity) and overcome drug resistance in cancer treatments,” the investigators write in the journal PLOS ONE, and is becoming an important tool in cancer treatment.
“It’s not uncommon for the cancer to become resistant to chemotherapy drugs so one of the ways that clinicians try to get around that is using combinations, two chemotherapy drugs together,” McIndoe says. “The likelihood that you will develop resistance to both of them simultaneously is lower than if you had just one.”
But given the number of drugs and drug combinations available, there are not efficient, effective ways to identify the best combinations, the investigators say. More