Breaking Down Barriers Between Pre-clinical and Clinical Adoption of Personalized Medicine
Personalized medicine will change how health care is delivered and disease is prevented and treated. But first, how disease and health is defined, as well as the clinical development and adoption of new therapies must align with current theories of disease and treatment.
These points highlighted one of the many engaging panels of the 2017 Business of Personalized Medicine Summit recently held in San Francisco. Titled “Clinical Adoption of Personalized Medicine: Challenges and Solutions,” the panel delved into the barriers between pre-clinical and clinical adoption of biomarkers, companion diagnostics and targeted therapies in the pharmaceutical industry. In order to realize the promise of personalized medicine — delivering the right drug to the right person at the right time, in the right amount —three major, related hurdles must be overcome. Panelists, Nicholas Dracopoli, Vice President and Head of Oncology Biomarkers at Janssen Research & Development, Maurice Markman, President of Medicine and Science at Cancer Treatment Centers of America and Gregory Frank, the former Director, Business Development at 23andMe suggested three different reforms to current paradigms of drug discovery and development to fully realize the promise of personalized medicine.
First, the industry must rethink the research-and-development stage of drug adoption, which is currently highly siloed, with each R&D budget focused on developing a blockbuster drug. Siloed priorities and drug development prevents cross-sharing of information that could both speed the process and lead to new discoveries. Dr. Nicholas Dracopoli of Janssen pointed to the clinical-trial stage as an example of where the industry could rethink the historical mindset. Currently, clinical trials are used to validate a drug’s efficacy, but industry should look at them as a way to discover new insights, he offered. Rather than running each trial in isolation, the data from each clinical trial should be collected in a single database, which could yield rich insights for every research project. And those trials should be used not just to confirm information we already have, but to unearth new insights that can lead to new discoveries.
The second reform is updating the U.S. Food and Drug Administration’s (FDA) mandate and the way the agency reviews and approves drugs. While medical and pharmaceutical science has powered into the 21st century, the FDA’s mission and methods remain largely unchanged from the mid-20th century. The panelist content that it’s not the agency’s fault – good, smart people who work there just aren’t equipped to deal with the new ways of collecting, processing and analyzing data. As Dr. Maurice Markman noted, FDA still requires the same process for drug testing and data review from clinical trials as it did in the 1950s. The result is that biomarkers have to be identified at the start of a trial, rather than treated as an inherent part of every stage of the trial. And, he noted, FDA could make exponentially faster determinations by embracing the data analytics that the scientific community has been applying for years.
Speeding the trial and approval process would help deliver on the third critical step: bringing payers into the personalized medicine fold by providing a clear demonstration of its value proposition to the folks who decide whether to pay for it. The whole idea of personalized medicine is creating highly tailored treatments, which means smaller patient populations and high costs that tend to scare away payers. Much of the science behind personalized medicine has focused on showing efficacy. But we need to also demonstrate value to payers – pointing them toward clinical outcomes that both improve health and replace or reduce costs in the system.
None of this will be easy. But I believe it’s achievable – and it’s absolutely worth the effort. As Gregory Frank, the former Director, Business Development at 23andMe, said on the panel that day, there is much more science and technology – data science companies are raising lots of capital to collect massive amounts of data, to which they intend to apply machine learning.
We just need to take steps to pave the way for all that technology, so it results in a healthier, longer-living population.