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Compared to 2006 when approximately 13 personalized treatment options were available, in 2014 that number increased to 113 personalized medicine drugs, treatments, and diagnostic products, according to the Personalized Medicine Coalition. Personalized medicine is becoming more prominent in the pharmaceutical and biopharmaceutical industries, with new technologies and research coming out constantly to make this vision a more tangible reality.

Deloitte Consulting, a company that provides audit, consulting, financial advisory, risk advisory, tax, and related services to public and private clients spanning multiple industries, released their first ever real world evidence (RWE) benchmark survey.

“The industry-wide shift from volume- to value-based payment models and the move towards more personalized health care have helped fuel interest in using RWE to understand and demonstrate the value of pharmaceutical and medical device innovations,” the company said in the study, adding that the “biggest opportunities for RWE are in market access and R&D, specifically to improve trial design and patient recruitment as a way to personalize care.”

Brett Davis, Deloitte Consulting Principal and General Manager of ConvergeHEALTH, participated in a Q&A with Pharmaceutical Processing about the findings of their 2017 RWE benchmark survey and personalized medicine market trends. His edited responses are below.

Q: Where do you think the demand for personalized medicine originated from?

Brett Davis:

The personalization of medicine is a result of advances in our scientific and molecular understanding of disease, and more recently the digitization of healthcare. The concept of personalized medicine has been around for more than a decade with the promise of the human genome project fueling it in the late 90s. Since then, personalized medicine has advanced beyond genomic medicine. With the proliferation digital health technologies, we can now talk about personalized medicine in a broader context, using not just molecular data but digital phenotypic data to personalize care.

Q: What are some of the benefits of personalized medicine? What are some of the downsides?

Davis:

In many ways, medicine has always been personal—starting with physician to patient interaction. This new era of personalized medicine is that now we can tailor treatment for the right patient at the right time and with the right drug or intervention.  A remarkable amount of health care is being wasted providing treatments for individual patients who don’t actually receive the benefits. With personalized medicine, some of this waste can be removed and can lead to better outcomes for patients, ideally at a lower cost point to the overall system.

With any new technology, there is always the potential for abuses. One example of this is the misuse of genetic/genomic information. We can increasingly use molecular information about an individual to predict with increasing precision the likelihood of a disease later in life. Although this information can be used to direct preventive health care interventions, it also has the potential to be abused if it is used for things such as insurance or hiring decisions. This is where policies, such as the Genetic Information Nondiscrimination Act (GINA), become very important to make sure we harness the power of personalized medicine while guarding against the potential abuse of this information.

Q: How can the RWE benchmark survey findings be applied in the pharmaceutical space?

Davis:

RWE is the information that is generated in health care systems outside of a controlled trial. We are now seeing a proliferation of all of this electronic health information generated by patients directly, through wearables, electronic medical records, insurance claims, and other data sets in the health care environment. As the pharmaceutical industry shifts to value-based, personalized health care, RWE can help answer the hard questions in health care, such as what works, for whom, why does it work, and in what context. All of these questions are at the heart of value-based personalized medicine with value-based care adding “at what cost.”

As the RWE benchmark survey validated, RWE is becoming essential to decisions across every aspect of the pharmaceutical value chain, from the early research and development stage by linking longitudinal clinical data sets with genomic data sets to drive insight.

Moving further down the value chain, RWE is becoming increasingly important in safety as the paradigm shifts from reporting to being able to flag signals across these RWE data sets. Finally, RWE has been historically used for market access and value arguments around how a particular medical intervention is driving health care benefits in today’s real world setting, for both reimbursement purposes and ongoing efficacy evaluation. As we increasingly move towards more outcome-based or value-based payment fees in the U.S. and globally, RWE becomes essential to informing those reimbursement discussions around the value of a particular medical intervention.

Q: In what way(s) could RWE potentially support the approval of new indications for previously approved drugs?

Davis:

In certain situations, RWE can be used to accelerate approvals because the evidence can be used for post-market surveillance after the drug is launched, potentially alleviating the need for an expense late stage clinical trial if the drug shows breakthrough potential in early trials.  We are increasingly seeing RWE potentially be used as control arms in trials, which holds the promise to reduce the cost of clinical trials and accelerate them as well.

Q: How does the 21st Century Cures Act, signed by President Obama in December 2016, impact the pharmaceutical industry—the FDA and use of RWE, in particular?

Davis:

From a regulatory approvals perspective, as the 21st Century Cures Act instructs the FDA, RWE is being used to help expedite the execution of the trial. With the FDA responsible for evaluating the expanded use of RWE, there is a growing need for life sciences companies to demonstrate value to payers and health authorities.

Q: What is the difference between RWE (real world evidence) and RWD (real world data)?

Davis:

RWD is the raw data being generated in the real world. This can be in an electronic medical record data set, a claims data set, self-reported data, data coming from wearables and other medical devices. RWD is the raw data.

RWE is when it moves from data to evidence. That is the derivative insights that are gathered from the RWD. RWE is the downstream insight that comes from analyzing RWD. Increasingly, we are advising our clients to think about evidence holistically and linking evidence strategies from early research and discovery all the way through to commercial, by creating a strategy, informatics infrastructure, and operating model that allows continuous evidence generation across that spectrum. For instance, if a pharmaceutical company is active in oncology, they need to think about how they can create an ecosystem of data partnerships and providers. In addition, they need to think about the right type of collaboration for creating a learning healthcare loop where evidence is not a one-time thing for a particular siloed function but instead something that is a continuous learning cycle.

Q: According to the study, the biggest opportunities for RWE are in market access and R&D, specifically to improve trial design and patient recruitment as a way to personalize care. In what ways can RWE impact trial design and patient recruitment?

Davis:

If we look at the clinical trials process, in a world of personalized health care, one of the biggest challenges the pharma industry faces is designing and efficiently recruiting for targeted trials. More often, every trial starts looking more like a rare-disease trial because patients are hard to find. RWE can be critical for both designing a trial where you know the patients actually exists in the real world for recruitment and also in identifying and recruiting those patients.

Q: How can life science and pharma companies access the ‘right RWD’?

Davis:

Traditional, real world data sets, like EMR and claims data sets, can answer some questions but have many gaps with some of the more complicated questions that the pharmaceutical industry faces as we shift to value-based personalized health care. As a result, one of the bigger challenges is how to get access to these data sets that can fill the data gaps that exist in the traditional RWD. This requires us to start thinking about data providers not as vendors who you procure data from, which used to be the dominant model in RWE 1.0, but more like scientific collaborators where there is a living, breathing, ongoing management of those relationships. The data that fills in those data gaps is not data that can be procured or sold in the same way that some of the de-identified data sets have traditionally been acquired.

Q: What are some of the technologies that would improve companies’ access to RWE?

Davis:

The exciting part about this moment in time in the industry is that not only are the data sources available to us in the real world expanding, but the platform technologies and data science techniques to analyze RWE are also making major strides. As noted in the RWE benchmark survey, the vast majority of the industry that is investing in new capabilities in the RWE space are leveraging cloud data platforms compared to on premise platforms. The cloud can quickly ingest and analyze the data and then also create external data sandboxes to collaborate with external partners who may not be willing or able to ship data to a pharmaceutical company.

Another example is a lot of the Big Data analytic techniques that have been developed and leveraged in other industries, such as retail and finance, can now be leveraged. Pharmaceutical companies are moving away from a traditional warehousing approaches to leveraging tools that are more suited to the variety and complexity of real world data. Last but not least, advances have been made from a methodological perspective in the data science world. Again, here is an example of the pharmaceutical industry beginning to leverage advances in the data science space that have been made in other industries. The industry is moving beyond traditional, statistical approaches to leverage these new data science methodologies and is increasingly tapping technologies like machine learning and other cognitive approaches.

Q: At what point in the product lifecycle is use of RWE ideal?

Davis:

RWE has been around for a while but there is a meaningful shift happening from what I call RWE 1.0 to RWE 2.0. One of the big shifts happening as we move closer to RWE 2.0 is that RWE is not just being used for market access or commercial purposes but it is also being used end-to-end. The RWE benchmark survey validates that, in fact, the industry is starting to think holistically about the use of RWE across many decisions and across the entire value chain. The RWE benchmark survey found that 54 percent of the industry is investing heavily to improve their capabilities both from an organizational perspective as well as from an informatics and platform perspectives.

Increasingly, RWE is moving from siloed use in pharmaceutical companies to a true enterprise, C-suite issue as the industry recognizes that if we use insights and applications from RWE, it is going to be as important as clinical trials. On a relative basis, roughly three percent of electronic healthcare information is generated in clinical trials which means 97 percent of the information is generated outside of clinical trials. When you look at the relative investment, the industry invests billions of dollars each year in clinical trials but a small fraction of that to acquire, manage, and draw insights from RWD. As the industry continues to shift to value-based, personalized health care, the survey validates our prediction that success increasingly will be defined by companies’ ability to engage the power of RWE.

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