Dr. Ron Buckanovich: Getting to the Root of Ovarian Cancer
Ovarian cancer researcher hopes to improve clinical trials by building a better mouse trap
We think by using human tissue around the tumor in a mouse, that will change the paradigm of how we screen drugs. When you grow a human tumor in human tissue, it is much more resistant to most of those therapies.
Dr. Ron Buckanovich
To find answers to riddles that haven’t been solved, sometimes you need to create tools that don’t currently exist.
That’s the thinking behind a new approach that cellular biologist Dr. Ron Buckanovich is taking as he works to develop drugs that fight ovarian cancer. An advocate for challenging the status quo, Buckanovich believes that current models for testing new drugs could stand some improvement.
Under the existing protocol, scientists typically screen new compounds first by trying them out on cells in a dish. If they’re successful, they move on to test them in mice. If it works in mice, they try larger animals, and finally they try humans.
But 94 percent of those drugs fail, to the tune of tens of billions of dollars, not to mention the wasted time of the patients in the clinical trials. Buckanovich is convinced that a better model would result in better, more efficient drug development.
“A mouse is not a small person,” he explains. “I’ve taken so much flack for that statement, which seems so obvious, but we’ve used mice as a surrogate for people for 50-60 plus years. And so we’re changing that.”
Buckanovich’s lab is now able to grow a human tumor in human tissue in a mouse, which creates a model that hews much more closely to the environment in which it will need to function.
“We think by using human tissue around the tumor in a mouse, that will change the paradigm of how we screen drugs. When you grow a human tumor in human tissue, it is much more resistant to most of those therapies,” he says.
His hope is that he will be able to change the paradigm, and that eventually every drug that goes into clinical trial is screened through this model first — thus creating a much more accurate predictor of clinical success.
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