Last Friday’s report in Reuters that less than 1% of US cancer patients get into clinical trials for new treatments tells a much wider story: that clinical trial and the real world clinical environment are often very different things.
The conclusion of the academics was:
In addition to profoundly low overall cancer trial accrual, vast underrepresentation by age, cancer stage, and site continue to exist. The generalizability of these trials to a real world perspective remains an open question. Physicians, payers, the National Cancer Institute, and other stakeholders need to develop broader cancer trials to benefit the millions of patients with cancer in the United States.
If any clinical trial is made up of demographically similar, symptom-selected patients cohorts who ‘qualify’ for the study, whilst the population the therapeutic is designed to treat is a demographically, genomically and symptomatically-diverse group, it is hardly surprising that clinical trial data often do not hold up after approval and launch.
Whilst this remains inconveniently true, it does no good to indulge in hand-wringing. Far better to look at how real world clinical data can be used to supplement clinical trials and better understand patient diversity and response. Clinical trials can then be adapted to treat what in reality are a diverse set of clustered patient cohorts with separate therapeutics, targeted at patients using biomarkers and diagnostics.
Underpinning this drive towards ‘stratified medicine’ or ‘precision medicine’ is ensuring we can bring together the symptom, demographic AND genomic data and use them properly to select multiple cohorts of patients based on all available knowledge to fully understand the underlying disease, not just applying a subset of clinical and lab data.
None of this is easy but the FDA knows it is coming. Two years ago Lawrence Lesko, PhD, FCP, director of clinical pharmacology and biotherapeutics said:
“For precision medicine, the disease must be diagnosed to the gene,” and he emphasized that vast amounts of data generated by human genome research in recent years creates an opportunity for drug makers to better target medicines to smaller populations of patients. But at the same time, the challenge for drug makers is like turning a large field of long-range radar antennae inward into the body. As Lesko points out, “Who is going to interpret the data?”
Once again, the ability to handle the data is key enabling technology.
IDBS have proven we can do this with real world data and provide the key to unlock insights into better trials and more predictable patient outcomes.