Traditional medical treatments have long struggled to deliver a desirable level of effectiveness for all patients. To address these deficiencies, the medical technology and life science industries, broadly defined, are turning to precision medicine – treatments designed to work for the individual.
Almost 30% of novel drugs approved over the last five years were precision medicines, which have provided unique opportunities for innovative therapeutic, diagnostic, and other medical technology companies. As figure 1 illustrates, the development process for companion or complementary diagnostics lines up well with the development process for therapeutics. There are now 37 approved companion diagnostics paving the way forward for more precise interventions. Also, there are other FDA-cleared diagnostics for use in precision medicine, for example pharmacogenetic tests that can identify individual differences in drug metabolism. In 2017, the FDA approved the first drug, Keytruda for cancer, based on a biomarker rather than the location of the tumor.
The Personalized Medicine Coalition suggests that efficacy rates for drugs can vary from 25% to 60%. Drug compliance is also a major concern – a number of studies have shown that compliance is often only around 50%, driven by in part by lower-than-expected efficacy and negative side effects. While drug treatments often deliver significant improvements in quality of life, many patients continue to struggle with the compromises that come with taking a drug designed to be one-size-fits-all.
As shown by results over the past half-dozen years, sorting patients based on their genome using evolving technologies such as DNA sequencing and other novel tests, has the potential to overcome a number of problems – including efficacy and safety. These and other ‘omics technologies may also reduce unnecessary treatments and prevent spiraling treatment costs, as well as save on R&D expenses for developers and manufacturers.
The challenge of precision medicine
The Tufts Center for the Study of Drug Development estimates that the R&D cost for developing a new drug is between $1.4 billion and $2.9 billion – a huge investment for any organization. In addition, Health Affairs has highlighted the growth of step therapy, i.e., trying a lower-cost drug before another more expensive drug (sometimes called fail first). Its 2018 study showed step therapy was considerable for specialty drugs and varied significantly among healthcare plans, ranging from less than 5% up to almost 50%. An earlier study in Health Affairs showed step therapy for all drugs among employer-sponsored healthcare plans was greater than 70%, despite studies showing that, in a number of cases, delaying treatment can increase mortality or worsen other outcomes.
While many believe that precision medicine, and the companion diagnostics to implement it, will be the best way to achieve better and cheaper healthcare, there are still a number of real or perceived barriers, as shown in figure 2. These barriers keep pressure on driving therapy research and development back towards the one-size-fits-all approach, rather than encourage the development of treatments that can be personalized. Some of the barriers include requiring more clinical studies, manufacturing expenses, diagnostic expenses, and sales, marketing and other expenses to serve smaller markets. For companies already investing heavily in traditional therapeutic development approaches, it may be difficult taking on the challenge of precision medicine.
Targeting future research efforts and collaborating strategically for success
How can medical technology businesses overcome these challenges to achieve the promise of precision medicine?
Medical technology companies across all fields should focus their energies on developing and utilizing tools that can match patients to the right treatment at the right time, as depicted in figure 3. To do this, they need to harness the potential of diagnostic and prognostic tools and resources, such as genome sequencing, novel protein and metabolic biomarkers, and, in the future, machine learning and artificial intelligence. They also need to identify the next research capacity bottleneck, rather than just chasing their current competitors, so that they can lead the way with evolving technologies.
Targeted investment in developing technologies, such as ‘omics technologies, may also help with screening for compounds that have broader uses for targets with low genetic variation. Using state-of-the-art tools to target novel biomarkers may also be pivotal to success. Other valuable investments might explore blood-testing technologies to replace invasive biopsies, so-called liquid biopsies. Accumulating data on other biomarker types, such as immunoassays and metabolomics, should also provide an invaluable data bank for future research.
Medical technology companies will need to work with pharma and biotech firms early to identify, validate and gain approval for companion diagnostics. In addition, identifying companion diagnostics early in the drug development process may prove useful for selecting patients during clinical studies and getting timely co-approval during the regulatory process. Ultimately, creating the infrastructure for efficient trials will enable organizations to adjust their approach and work towards nichebustersrather than blockbusters.
Finally, diagnostics, information technology / AI / machine learning, and life science tools companies also need to develop the conversation with medical device companies to identify additional areas where a precision approach may be effective. Medical device trials could also benefit from better and more precise patient selection. By pursuing complex health economic analysis in partnership with a range of therapeutic, diagnostic and medical device firms, medical technology and life science companies can drive and take charge of innovations in precision medicine.The application of precision medicine may not only improve patient outcomes and lower healthcare costs, but also provide valuable business opportunities for perceptive medical technology firms. Organizations utilizing machine learning, artificial intelligence and other big-data technology can also contribute to precision medicine, benefiting commercially as well.
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