The intersection of precision medicine and population health
By: Jennifer Christian, MPH, PharmD, PhD | August 02, 2016
Integrating disparate data will usher in a new era of patient-powered research.
Precision medicine is revolutionizing the way we diagnose and treat disease by allowing researchers to better understand whether a patient will respond to a specific treatment. At the recent International Society of Pharmacoepidemiology (ISPE) mid-year meeting in Baltimore I gave a presentation on the intersection of precision medicine and population health as part of a broader panel on new models of patient-powered research.
Most discussions of precision medicine focus on genomics — the ability to identify the unique biologic characteristics and genetic variants in patient populations who will respond well to a specific drug or treatment. However, biologic characteristics are only one aspect of the precision medicine value proposition. To get a full sense of the patient and whether a treatment will be effective, researchers must also take into account the clinical, social and environmental factors that impact treatment outcomes. In some disease categories, such as cardiovascular disease and Type 2 diabetes, environmental and behavioral factors (such as smoking, unhealthy diet, and lack of exercise) play a much greater role in disease risk and progression than genomics.
External factors can be more difficult to identify, track and measure — especially for researchers who are accustomed to measuring everything in a lab -- but unless we consider all of these factors, we can miss critical opportunities to hone our research and deliver the most meaningful results. Consider the case of imatinib. In a Phase I clinical trial to test dosing limits for this oncology drug, 53 of the 54 participants suffering from chronic myelogenous leukemia (CML) who received a dose of 300 mg or more per day reported complete hematological remissions. However subsequent clinical trials reported lower cytogenetic response rates with the same doses. It was an important lesson for researchers that even when there appears to be a clear molecular pathway, other unknown factors can play a role in the anticipated effect and overall efficacy of a treatment.
This example reflects why evaluating clinical heterogeneity in treatment response is critically important for advancing precision medicine. Indeed, at the core of clinical medicine lies substantial variation in clinical response that can be attributed to the extraordinary heterogeneity of individual experience.
Taking a holistic look at all of these factors can generate a clearer understanding of treatment process, and enable the industry to translate individual success stories into strategies that can positively impact broad populations of patients. Fortunately, the industry is investing more time, money and research into building the tools and knowledge needed to advance our understanding of the intricacies related to precision medicine. The UK Biobank project, for example, has recruited 500,000 adults to collect blood, urine and saliva samples along with detailed personal information. Participants have also agreed to have their health followed over many years to help scientists discover why some people develop particular diseases and others do not. And in the United States, The Million Veteran Program is a national, voluntary research program funded entirely by the Department of Veterans Affairs (VA) to collect blood samples and health information from veterans receiving care in the VA Healthcare System to study how genes affect health. This research will be further spurred by President Obama’s $215 million Precision Medicine Initiative, that promises to create a massive pool of patient data to help researchers better understand the underlying causes of disease..
An integrated approach
The collection of these massive datasets is an important first step in this process, but it also underscores the need for researchers to take a more integrated approach to data collection itself. To get the most comprehensive view of the patient experience, researchers need to incorporate data from multiple sources, including patient registries, electronic health records (EHR), secondary research, and patient and physician reported outcomes. But gaining access to these data sets, and establishing standards and protocols to more easily validate and analyze the data while protecting the anonymity of the patients is a considerable hurdle. Lack of interoperability between data systems, the difficulty of validating the quality of data, and concerns about the safety and security make it difficult for researchers to easily mine and analyze these data sets in any meaningful way.
However these are technical issues that can be overcome if researchers are willing to collaborate and create the needed infrastructure and standards to support fair and effective data sharing. It will require significant time and investment, but such integration will ultimately enable greater advances in precision medicine, and a more effective approach to ensuring we deliver the right treatment at the right time, every time to every patient.