When it comes to discovering new therapeutic agents for treating cancer, the time and expense involved are staggering. “If you look at traditional ways of discovering new drugs, it can take 15 to 20 years, and cost upwards of $1 billion,” says Philip R.O. Payne, PhD, director of the Institute for Informatics at Washington University in St. Louis. And even before the clinical trial phase, a new cancer drug is studied for at least six years on average.
But there is hope that the development process can be accelerated. A promising approach lies in discovering new uses for already approved medicines — known as drug repurposing or repositioning. Because repurposing relies on earlier studies that already passed toxicity and other tests, new discoveries can reach clinical trials more quickly. In addition, the Food and Drug Administration is able to complete a faster review. The entire process can be achieved in a matter of two or three years, and for a cost as low as several million dollars, Dr. Payne says.
Combining already approved drugs for new purposes holds great potential as well. “When it comes to cancer, we’re starting to see a lot of opportunities to use combination therapies to achieve even better outcomes,” Dr. Payne says. “So the question is, how do we build a discovery pipeline that allows us to repurpose or reposition existing drugs?”
To tackle that challenge, researchers are relying on genetic science and the innovative tools of informatics. This includes an informatics concept known as machine learning, in which computers are programmed to comb through large amounts of clinical and drug data to discover meaningful patterns that can reveal new uses for existing medicines.
Dr. Payne points to his research with malignant melanoma as one example. While frontline therapies for the disease can be very effective at causing tumors to disappear, patients often become resistant to those drugs within 12 months after the first treatment. Dr. Payne’s research is examining what happens at a genomic or clinical level that leads to this resistance. The goal is to find a genetic signature that can be used to determine if there are drugs already developed for other diseases that could reverse the resistance.
“We do this by using a variety of computational methods, and we’ve actually found very promising agents that can be combined with those frontline therapies to prevent or delay the onset of resistance,” says Dr. Payne, whose lab is also conducting research on chronic lymphocytic leukemia and acute myeloid leukemia, as well as rare neurodegenerative disease.
In the past, discovering new and combination uses for medicines has relied on researchers using instinct to choose which drugs may be candidates for further study. Informatics, by contrast, is data driven, with computer programs analyzing billions of variables. “We’re doing it on a much faster and a much broader scale,” Dr. Payne says. “That way, we can get to the most important combinations more quickly than if we just relied on good luck and intuition.”
“When it comes to cancer, we’re starting to see a lot of opportunities to use combination therapies to achieve even better outcomes,” Dr. Payne says.