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Highlighting the biggest observations from this year's DIA Annual Meeting-topics likely to continue to dominate industry headlines for the foreseeable future.
The DIA Annual Meeting held in late June offered its signature high-quality educational sessions that address numerous topics, with much global regulatory authority presence. The exhibit floor, however, showcased more often than not, services and technology applications for clinical trials and operations, many of which focus on patient centricity. Clearly, as we addressed in-depth in Pharm Exec’s March issue, patient centricity is not just a
way to get patients recruited more quickly into trials; it is a more holistic approach that spans a pharmaceutical company’s overall approach or philosophy to its mission.
At DIA, Parexel held a private roundtable and unveiled research that shows when a company employs patient centricity in its drug development designs, the likelihood of launch increases 19%. Suffice to say, there will be more news coming out around that report. However, other trends or observations heard around DIA comprise the remainder of this column.
Change management. Culture may eat strategy for lunch but change management within an organization hoping to improve and innovate processes through improved technologies is a reality. From implementing patient-centric processes throughout an organization, to the ever-present frustration around implementing risk-based or targeted monitoring, it seems getting people to let go of the old and bring in the new is the norm. Even a reference to companies that still use paper-based data collection will get a chuckle in a room, but, be sure, it is still a widespread practice.
Crossover technologies. I heard from many newer companies coming from the healthcare side, hoping to implement what they do here on the pharma side. Mostly in regard to patient-facing or person-facing technologies, uses include telemedicine platforms, training, home health, and apps. Even technologies used in postmarket drug approval, such as registries for longer-term outcomes data, are seeing the opportunities to apply their knowledge upstream to the research area.
Personalized medicine and powering smaller population trials is here. While the general notion is that this direction is relegated to the rare and orphan disease space and is a longer way in the future, practice says it’s here. And while the rare space has more experience with it, there are larger oncology organizations utilizing adaptive trial designs that address sub-types, to apply the person with the phenotypes best-suited for success. Patients seeking trials will smartly be looking for the ones that present maybe only a fewer percentage points better for their specific mutation, but 5% to 10% differences will matter to the trial in which they commit.
Continued frustration with CROs. As a whole, CROs run the gamut of services, sizes, and provider models, but no matter how a provider may want to change what category they are labeled, CROs are what they are stuck under. And, as a whole, one bad experience with a CRO can leave a pharma company swearing off of all types of CROs forever. One bad apple spoils the bunch, so to speak.
We have reported before in our pages on the less-than-ideal experiences that emerging biopharma is experiencing with their CROs. From inattention to detail, to performing (and charging) for unnecessary patient tests, to execution failures in dealing with smaller patient populations with rare diseases. Emerging biopharma has a range of needs with limited resources, and in some cases, limited knowledge on where to start. CROs need to start upping their relationship game and communication skills to get into the weeds with emerging biopharma on what is truly needed in whatever capacity-trial to commercialization-to get to some core understandings.
Data. It can’t be stressed enough that the only way forward is through data. Even those that collect information and conduct processes on paper, while getting a laugh, are setting themselves up for longer-term pain. Large pharma can afford to buy data sets, to acquire assets to improve, manage, and integrate data toward improvements on all levels of uses for predictive analytics, risk-based approaches to everything, and eventually artificial intelligence to model and simulate scenarios. Smaller companies are left with choosing the best service providers to help them with those needs. Data collection, cleanliness and handling for trials, submissions, and all else requires data experts and data scientists. Look for more organizations to push data scientists closer to all needs for pharma companies. Learn to be data smart.
By the end of this month, the Pharm Exec team will have completed our content calendar for 2020, determining the month-by-month editorial topics for the specific issues. This doesn’t include the majority of our information that is posted online, which also focuses on these and many more trends and observations. But these topics will continue to dominate the headlines for the remainder of this year, and into the next.