[Guest post by Nina Tandon, the CEO and co-founder of EpiBone.]
In many ways, the clinical trial hasn’t changed since Biblical times.
The world’s first clinical trial was recorded in the book of Daniel in the Bible. King Nebuchadnezzar of Babylon ordered his people to eat only meat and drink only wine, thinking this diet would help them be better soldiers, and yet some noblemen protested, preferring a diet of vegetables. And so the king proposed a 10-day head-to-head trial: everyone would eat meat and drink wine, except the protesters, who would eat legumes. At the end of this open, uncontrolled experiment, the vegetarians appeared better nourished than the meat-eaters, and the king allowed them to continue their diet.
The modern clinical trial still resembles Nebuchadnezzar’s experiment (see here for a great history of clinical trials): two groups are assigned to different conditions and the overall group outcomes are studied. Like Nebuchadnezzar, we assume that the group’s outcomes tells us something about the effects for many individuals. In other words, medical treatments are tested for efficacy for the “average patient.” And as a result of this one-size-fits-all approach, treatments may be very successful for some and not for others. Or rather, mostly not: in fact, every day, millions of people take medications that will not help them. Consider that the top-10 highest-grossing drugs in the U.S. help between only four percent and 25 percent of the people who take them.
In recent years, various new types of diagnostics, trial designs and therapies have been developed that take patient individuality into account — and have begun to yield some astonishing results. The drug ivacaftor, for example, was designed to treat a particular genetic variation of cystic fibrosis, oncologists now routinely use tumor genetics as a basis for cancer treatment regimes, and regenerative medicine is producing patient-specific implants for body parts ranging from windpipes to bladders.
Our startup, EpiBone, hopes to also someday be part of this story: we engineer patient-specific, anatomically correct implants for skeletal reconstruction. We use CT scans to engineer the precise 3-D shape of the bone needed and patient cells to grow the living implants. You can’t get more personalized than that.
EpiBone CEO Nina Tandon in her lab
However, translating successes of precision medicine to a larger scale will require a national effort.
With Obama’s recent announcement of a $215 million dollar Precision Medicine Initiative meant to help speed up the development of new treatments and improve care, many are excited about the potential to modernize therapeutic discovery. We have new and exciting tools to leverage, including next-generation genomics and large databases full of information from galvanized, empowered patients. The overall fitness of Nebuchadnezzar’s legume eaters is no longer the driving question. With individualized therapy, the patient’s individual response is what’s most important.
But, despite this step in the right direction, it’s still time for a modernization of our medical research methods. And thanks to exciting new techniques and a surge of interest, I’m hopeful that’s what we’re about to get.
Precision medicine requires a different type of clinical research that focuses on individual, not average, responses to therapy.
Recent clinical trial innovations, such as basket trials that categorize patients into cohorts based on patient-specific molecular markers, umbrella trials that test multiple drugs on a single disease, or adaptive trials that vary the interventions on an ongoing basis based on patient responses, all aim to add refinement to the “gold standard” of the randomized clinical trial.
However, despite the nuance, these methods are more like a “prêt-a-porter” adaptation to the “one-size-fits-all” approach, as they still rely on using statistics to determine average responses in groups (albeit more cleverly-assigned). Why? Perhaps because, as humans, we crave the contrast that we generate via multiple comparisons in order to tell if someone, well, has gotten better, and, moreover, to separate the siren song of correlation from causation to judge if treatments are in fact to thank (or blame) for patient outcomes. Data that can be sliced and diced is great starting material for this kind of inductive and deductive reasoning.
So how will we acquire actionable data without losing the individual in the average? Studies that focus on a single person — known as N-of-one trials — will likely be a crucial part of the mix. Physicians have long done these in an ad hoc way. For instance, a doctor may prescribe one drug for hypertension and monitor its effect on a person’s blood pressure before trying a different one. But few clinicians or researchers have formalized this approach into well-designed trials — they usually just take a handful of measurements, and only during treatment, not before or after.
I predict that a Big Data approach to each step of the clinical pathway (i.e. diagnosis, treatment and monitoring) will aid in the formalization of this approach. Next-generation companion diagnostics will probe the myriad factors — genetic and environmental, among others — that shape a person’s response to a particular treatment.
Next-generation treatment algorithms and/or assessments of efficacy may involve correlation of health data with new data sets, from geo-political data to pollution indices to weather patterns.
And next-generation health-monitoring devices will need to identify and (unobtrusively) track appropriate and diverse biomarkers, from the presence of tumor DNA in the bloodstream to the quality of life. Dose-and-response concepts will also need to be adapted for regenerative therapies that are, in a sense, living medical devices that fully integrate into the body.
We’ve only just started considering the questions, but the real impact on patients could come quickly. Remember that King Nebuchadnezzar is perhaps best known for the seven years he spent roaming the wilderness eating grass, which is still shorter than the path of many therapeutics through clinical trials.
