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Personalized Medicine: New Technologies Improve Personalization and Precision
As new personalized medicine technologies such as next generation sequencing find increased clinical use, two key challenges will emerge, Jeff Elton discusses these new hurdles and also how - and why - you need to overcome them.
As new personalized medicine technologies such as next generation sequencing find increased clinical use, two key challenges will emerge: healthcare providers and payers will need a plan to accommodate new insights that may not correspond to current treatment approaches and reimbursed regimens; and pharmaceutical company scientists, clinicians and commercial leaders will need to align the increasing levels of patient genomic data, matched with electronic medical record data, with their own clinical trial and other data. Accenture considers this to be a requirement to drive R&D priorities and commercial strategies to deliver the highest value therapeutic solutions to patients and provider systems.
The genomic information available for example from one new personalized medicine technology - next generation DNA sequencing - can offer great potential if results are fully exploited for the benefit of patients and the health care systems. We’ve found that next generation DNA sequencing is helping to bring the precision of personalized medicine closer to clinical reality, as doctors and their patients increasingly desire refined diagnosis and a broader set of evidence-based treatment approaches. These approaches must be tailored to their individual clinical needs and validated by the science and supporting clinical analytics.
Current diagnostic approaches analyze one gene mutation at a time. But next generation sequencing platforms analyze a panel of genes or the entire genome, providing insights into commercially available and development-phase therapeutics that may be beneficial to specific patients.
The ability to interpret these genetic data is improving at an accelerating rate, but there is a need for substantially greater progress. Scientists and clinicians are using these platforms to find the best therapeutics suited to fight a disease, such as cancer, but they also are finding that the therapeutic best genetically targeted to a disease state may not have received regulatory approval for that specific use, or lacks reimbursement for the specific cancer or indication.
Generating New Data
“Insights gleaned about existing therapies may be more effective to treat an individual patient’s disease state than the existing standard of care based on averages or assumed conformance to published studies”
New technologies such as next generation sequencing are beginning to yield an abundance of information. A recent MassBio panel session in March of 2013 featured leaders from diagnostics companies deploying these new technologies as the basis for their product. From this session, we noted that diagnostic approaches were historically single test, single interpretation based, and that currently much of the information from these emerging technologies is not considered part of the standard of care or fully reimbursable according to current standard definitions.
Yet a closer review indicates that insights gleaned about existing therapies may be more effective to treat an individual patient’s disease state than the existing standard of care based on averages or assumed conformance to published studies. This is unexpected information that challenges traditional clinical approaches to treating a disease, but valid against the biology of the disease state. And, this new information could be generated into a variety of databases, impacting the way diagnoses are performed and how drugs are used.
An Increase in Treatment Options
These types of new technologies will give scientists and clinicians different options. The availability of quality data will make it possible to plan and direct therapies with greater accuracy. Pharmaceutical companies will have certain specifics that they can target to improve a therapy.
Based on the data available, physicians may conservatively choose only to use those therapies for which there is positive evidence that supports successfully treating a disease and for which reimbursement is available. But if the evidence indicates that current therapies don’t work well, doctors and clinicians may place a significant emphasis on the evidence and diagnosis provided by these newer technologies. As a result, they might use therapies not considered to be the ‘standard’ for treating a disease. This is personalized medicine in practice, as precision medicine, driving both efficiencies and outcomes.
Because of the data made available by such technologies, we expect the personalized and precision medicine knowledge base to increase. There will be greater amounts of genomic-genetic data generated, in addition to the large amounts of clinical trial and patient-based electronic medical record (EMR) data already present in a large number of databases.
“Novel insights may evolve from these data that can help companies determine which therapeutics to develop”
The cycle emerging is to generate more data, increase the power of analytics and interpretation, and support the utility of these new decisions by capturing and measuring outcomes. Novel insights may evolve from these data that can help companies determine which therapeutics to develop. But different therapeutics may not be reimbursed. Only those therapeutics that target functionally related genes that are part of a disease-associated pathway could be reimbursable.
However to fully realize the benefits of these new technologies, we reason that there will have to be sound empirical evidence of clinical utility so that therapeutic benefits can be maximized and the risk of negative side effects minimized. A new data infrastructure and analytics must be responsible for interpreting massive amounts of genomic data, finding value patterns in that data. Standards and guidelines must be established to help with the clinical interpretation of data from which treatments can be determined.
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