Precision medicine is the logical outgrowth of years of increased understanding of the biology underpinning human disease. As is often the case with advancing biotechnology, clinical and subsequent commercial solutions lag primary scientific insights.
Once deemed a “market killer”, patient segmentation is the new reality. But for corporate development, researchers, clinicians and the life science investment community, complexities around development and commercialization of precision medicine are immense and uncharted. At the corporate and investment level, visionary and practical thinking is called for to help mankind realize this next medical advance and simultaneously return both therapies and capital.
Visionary thinking has taken us far and precision medicine is now a reality in selected circumstances, improving healthcare outcomes and reducing toxicity, treatment failure and wasted expense seen in more blanket treatment approaches. Industry actively seeks collaborations over adversarial competition in the form of alliances to share data, develop diagnostics and combine treatment offerings to reach the greatest potential outcome for patients and shareholders alike.
As research improves our ability to predict which treatments will work best for specific patients, these collaborations enable companies to do what was previously unobtainable: manage bilateral pipelines of both therapeutics and diagnostic tests, wrangle a data deluge unprecedented in our time, sort out privacy and patient enrollment challenges, manage expenses around sequencing large amounts of DNA and change mindset about payers – to start.
Multi-disciplinary partnerships of scientists, academia, biopharma and medical device companies, patient advocacy groups, philanthropic interests and other parties are striving to push through barriers to bring new treatments and revenue streams to life. Collaborations, such as Bristol-Myers Squibb Company with GRAIL and Rady Children’s Institute for Genomic Medicine and Alexion Pharmaceuticals, allow companies to share clinical trial databases, and shoulder research and resources to navigate the complexity of launching precision medicine initiatives.
The question now around corporate drug development and computational advances in genetic sequencing is how corporations could work precision medicine into their business strategies with an eye toward benefiting patients while ensuring a long-term return.
Thinking beyond competition to collaboration and vision has brought us this far and now corporations seek practical steps to creating a robust pipeline with precision medicine.
The first step is to identify potential partners, and below are practical pointers, reflecting a discussion I held at Convergence Forum in May 2017 with former Sobi CEO Geoffrey McDonough, MD, and Harvard TH Chan School of Public Health Professor of Computational Biology and Bioinformatics John Quackenbush, PhD.
Consider new approaches to analyze data
Recruiting sufficient numbers of patients will be extraordinarily difficult and institutions should look for ways to collaborate to share data and clinical findings. Finding new ways to look at the data to pull out value will be important. New models of observation are needed. For instance, a video taken by a patient’s mother showing radical improvement of the child’s movement provided a breakthrough moment for a research team studying precision medicine. It “. . . gave us the conviction to change our analytics in a different way,” said McDonough. “When we talk about being on the cutting-edge – it’s also changing perceptions about what is data, what is valuable.”
“When we talk about being on the cutting-edge – it’s also changing perceptions about what is data, what is valuable.”
Geoffrey McDonough, MD
Convince others you can achieve a meaningful effect size
Be clear on where that effect size can be seen most consistently. When speaking with investors, go small with the patient pool to show effect and build complete conviction that the initiative will change lives. For payers, this is the bottom line. McDonough emphasizes the need for corporations to work from a base of conviction, “So much conviction that you feel a responsibility to develop that drug.”
Talk to Payers Early and Often
Take time to educate and connect with regulators and payers, at the earliest time before certainty is established. Convince them of your initiative’s potential. Ask for unvarnished feedback and advice. And then ask again. Allow regulators to see effect on one disease and then prove – and prove again – on that basis for subsets. Be patient enough to replicate and scale. In a sense, payers and regulators become partners in the effort to bring effective treatment to fruition.
See beyond minimally effective drugs to transformational treatments
Visionary companies see beyond effect size to the potential of cumulative impact through combination. By its very nature, precision medicine will treat smaller patient pools with greater efficiency. But approaching with an understanding the diseases we are looking to treat and cure can respond to synergistic compound use for a cumulatively larger effect is part of the visionary requirement for corporate planners. Find the right partner and bring your approaches together.
Extract real knowledge from data
Look for the knowledge buried in data, don’t simply collect and amass data. Precision medicine moves beyond sequencing genes to decoding sequences into diseases and how to cure them. Massive data sets are required. Working together, intuitions can amass enough data intelligence to move precision medicine ahead. In fact, collaborations of this nature are foundational to success, philanthropic organizations have stepped in to make data sharing collaborations a reality and patient advocacy groups lead from the front in this area. In March, The American Heart Association announced the AHA Precision Medicine Platform, a global data discovery platform developed withAmazon Web Services open to researchers, physicians, computational biologists, computer engineers and trainees to access and analyze volumes of cardiovascular and stroke data.
Dr. Quackenbush notes that combining more intelligent longitudinal data collection with well-principled ways of looking at data comes down to segmenting population. This can lead to discovering effects that are meaningful. From here, start to build models to make meaningful predictions. Practical data examination means higher level of correlations, more focused on how we impact populations and interpret where they are diagnostically and with therapeutic response.