By Ed Miseta, Chief Editor, Clinical Leader
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Regulatory concerns are still a factor for companies considering the adoption of mobile and wearable technologies in clinical trials. In fact, industry group ACRO (Association of Clinical Research Organizations) recently released a report that showed this to be the No. 1 concern of companies looking to implement mHealth technologies.
But for every company hesitant to move forward because of regulatory concerns, there is another that is willing to forge ahead and show others how it can be done. GlaxoSmithKline is one of the companies making that move.
“As a company that is moving forward with mobile technology adoption, I can tell you that regulators are probably the least of our concerns,” says Rob DiCicco, VP, clinical innovation and digital platforms at GSK. “I think the bigger challenge for many sponsors is they aren’t sure how to invest in new technologies and how to properly incorporate them into a trial design from end to end (i.e., from protocol to report).”
According to DiCicco, the FDA and other regulatory agencies have been very clear about their expectations regarding the use of mHealth technologies and the data and information obtained from them. He believes regulators have been at the table with the industry, both in individual conversations with companies and at conferences sharing their direction and views. Several industry working groups, including the Clinical Trial Transformation Initiative (CTTI), have also been active at conferences discussing regulatory issues.
Glen de Vries, president of Medidata Solutions, a provider of cloud-based solutions for clinical research that has worked with GSK, agrees. While there are companies that do not want to be the guinea pigs, de Vries has found the FDA is always available to sit down with sponsors to discuss studies and specific concerns. “The FDA is willing to set up meetings long before the study starts to address potential issues,” he says. “They are also asking for input from sponsors as to how they should be managing and reviewing this process. We all agree we will get better efficacy and safety data using mobile health devices, and the FDA is asking companies to work with them to devise the right approach to regulating this data.”
Still, incorporating mobile and wearable technologies into a clinical trial can be a daunting task. Every company has clinical standards to uphold, and that makes executives dependent on new devices performing as promised, which is not always easy to verify. “In a hospital environment, machines are maintained and calibrated on a regular schedule,” says DiCicco. “It is much more difficult to do that in an environment where the patient is at home and doing things like sweating, showering, swimming, and other activities that can impact the performance of the device.”
To overcome this problem, GSK is developing platforms that allow the company to test new devices in different settings. One example is Gadget Trial, a GSK program that studied healthy volunteers in a clinical pharmacology unit. Gadget Trial allowed the company to monitor participants and evaluate the performance of sensors. In addition to sensor performance and data acquisition, user acceptance/preference was also assessed. “We asked them to assess which one they liked better, which ones worked better, and which ones were more or less convenient to use,” says DiCicco. “We also tested if the information came out of the device correctly, if there was missing data, how long the battery lasted, and if it provided good connectivity. It’s an important test, because we would want to know all of those things before placing a new sensor into a study requiring significant investment.”
INCORPORATE THE RIGHT DEVICES
According to DiCicco, selecting the right device is an important consideration when incorporating mHealth into a study. GSK currently has a study underway for patients with amyotrophic lateral sclerosis (ALS). For this study, the company selected a medical-grade activity device. With the technology, GSK is able to glean a lot of information from the device, including how many steps patients have taken and their positions over the course of the day.
While DiCicco would not rule out using a consumergrade wearable device (such as a FitBit) in a trial, he does believe companies use them at their own risk. “The appeal is the commonality of it, along with the cost and availability,” he says. “For important decision-making studies or studies that could affect our label, the main focus should not be on cost. Trials will still be expensive, regardless of which devices you use. Medical-grade devices require an additional investment, but that is necessary to get a device that will safely and securely move patient information to the cloud and make proper use of password access. We need that rigor around the data-collection process.”
Although a medical-grade device might impact the cost of a trial, DiCicco is more concerned about risk and believes that is what will make or break the adoption of a device into a trial. However, if you insist on looking at the higher cost of incorporating medical-grade devices, he recommends you also consider the costs involved in not incorporating these devices. Without a device, some trials will require home visits for participants who are not able to make it to a clinic. That is also expensive. Those patients who do make the trip might have to drive an hour, pay $30 for parking, sit in the waiting room for a couple of hours, and then do it all over again the following week. That does not help patient retention.
“If we could gather information from them during the course of their normal daily life, study visits could be reduced substantially,” says DiCicco. “In that situation, we dramatically change the cost basis for conducting a trial. Continuous monitoring would improve the outcome of the trial and the quality of the data collected. It would also enable us to learn things from data that we might otherwise not have access to. By making the trial easier on the patient, we could also improve patient retention rates, which save a lot of money in the long run.”
“Mobile health gives us a chance to measure what is happening in real life, but in a clinical trial context,” adds de Vries. “By allowing us to measure the efficacy of a therapy in the real world, these technologies will drive better outcomes for patients. For sponsors, mHealth devices also provide a higher quality hypothesis of whether or not a drug is worth bringing to market. That is a very valuable proposition.”
"I think the bigger challenge for many sponsors is they aren’t sure how to invest in new technologies and how to incorporate them into a trial design."
VP, clinical innovation and digital platforms, GSK
OVERCOME INTERNAL BARRIERS
Despite the advancements being made in mobile and wearable technologies, pharma remains a conservative industry. Trials can cost millions, if not billions, of dollars. Therefore, anything that introduces new risks to a trial will be evaluated carefully. In fact, in a large pharma firm like GSK, any technology change can have a huge impact on many departments and face many detractors. For that reason, DiCicco put together a cross-functional team to help with the effort.
The team included individuals from departments across the company, and each participant had an interest in advancing the use of mobile technologies within GSK. “I think the main thing we did was establish a case for making future investment,” says DiCicco. “A year ago, the group had no formal full-time employees and no budget. Today, it has both.”
There were barriers the team had to overcome, and one of them was certainly cost. Incorporating a wearable device into a trial might cause costs to increase by, say, 20 percent. That can cause some pushback to the project. DiCicco notes this is where the budget can come into play. “If our team believes a piece of technology can help the study or the patient and produce better data, then we use our budget to cover the cost and make that obstacle go away,” he says.
In just the first year, DiCicco has also seen the cross-functional team evolve. Initially, it was very exploratory. The team would seek out academics in the algorithm space and companies that were advancing solutions to handle data generated by mHealth devices. The goal was to learn what products were out there and how they worked. The team was also looking for places where the technologies could be used.
Since receiving an operating budget, the team has flipped things around. Today, it tries to identify problems and pain points within the company, and then looks for digital and mobile technology solutions that might solve them.
ONE TEAM TO ASSESS PRODUCTS
Within GSK are therapy area units (the portfolio owners) and platform groups (charged with implementing clinical trials). Those groups include clinical development specialists, clinical operations, and preclinical regulatory therapists. DiCicco notes that across the clinical trials enterprise, any number of individuals within various departments might have service agreements with the same companies, but no awareness of what the others were doing. Eventually, senior managers recognized that it would be inefficient for every therapy area unit and every platform group to continue to work with companies in this manner.
“Vendors can be very ambitious and persistent when it comes to convincing sponsors that their product will transform clinical development,” says DiCicco. “Sometimes the technology is interesting, and sometimes it’s not. But if one group tells them no, that doesn’t stop them from making their pitch to another group. So in that respect, the group was born out of a necessity to provide information-sharing within the company. Initially, it led to greater coordination across groups, but a year into it we realized that if we really wanted to get traction, we needed to make formal investments in this space.”
Today, a vendor with a sensor would make its presentation to the cross-functional team, which would then evaluate whether it was effective and where it could best be utilized. DiCicco notes the entire evaluation process is done with complete transparency and awareness. The clinical operations group may have no use for a device today, but the value-evidence generation group might. By having one group evaluate how a technology can be used, there is a multidisciplinary assessment of the opportunity and how it can address problems within GSK.
OVERCOMING DATA CHALLENGES
One thing the team did not have to struggle with was getting data from a device to the cloud. “It is not an issue today,” says DiCicco. “We are doing that reliably and are confident the data is secure and has an acceptable level of integrity. The challenge we now face relates to integrating data from multiple and different devices. In other words, how many different interfaces do we need to put in front of the patients and how many different interfaces does a study team, data scientist, clinician, or statistician need to deal with?”
If patients have to run five apps on their smartphones, that can quickly drain the batteries and require them to be near a power source all day. DiCicco also does not want a patient to have to carry multiple devices, one to perform a clinical outcomes assessment, another to track activity, and still another to measure breathing. The preferred model would be one where patients could use their own devices. On the back end, a study team could work with one vendor or one integrator to pull all of the information together.
“Today there are different data formats and platforms,” says DiCicco. “We also have to deal with different configurations, hardware, and software. That is where we are going to need some help. We have data coming in from electronic health records (EHRs), labs, phones, and sensors. Getting everything in the same standard format will require a concentrated effort by sponsors, vendors, and industry groups.”
Here again, DiCicco believes guidance from the FDA has been clear. He notes the requirements to get a medical device 510(k) approved are straightforward. The FDA has also issued guidance for the industry on electronic source (eSource) data in clinical investigations. The guidance explains the FDA’s expectations in terms of the level of validation needed for the data to be acceptable.
DiCicco believes the bigger problem will be things like device upgrades. “Anyone who has ever had an iPhone or Android device knows the experience of having to download a software update,” he says. “Then you realize you can’t find your pictures. Or the phone has a different look and a different layout. When that software changes, it can also affect an algorithm that was calculating someone’s heart rate. We don’t always know what will be impacted by an upgrade. I suspect we will be able to deal with it, but it’s something we will have to better understand so that we can explain to regulators how we will handle it. With many of these sensors, algorithms, and apps, we are in uncharted water.”
Along with the increases in data volume come improved methods of analyzing it. Statisticians have devised new statistical methods to deal with the volumes of clinical data now coming in, and machine learning is being used to identify outliers and understand what is going on in underlying data that a human might not be able to see. De Vries notes that by grouping similar patients together, statisticians can better identify patterns in the groups.
NEW EXPERTISE MAY BE REQUIRED
As companies move into these uncharted waters, they will also need new skillsets. Some of those skills will likely come from outside the pharma industry. DiCicco thinks that in a few years, project teams within pharma companies will be organized and populated differently. New capabilities will exist, especially in the sensor space.
“I might need someone with a bioengineering background to tell me if a sensor is going to overheat and burn the patient,” says DiCicco. “I might also want to know if the sensor is going to drain the battery, what the battery life will be under varying conditions, if the sensor will transmit when I want it to, and more. If a company is not currently employing wearable devices, those skillsets might not exist. You will also need people to validate the technology and someone with experience in math to help vet the algorithm.”
DiCicco believes new hires in the areas of data science and statistics soon will evolve and become more valuable to the pharma industry. He also believes many individuals with those skills may already be in pharma companies, but the companies don’t know it. Therefore, companies may need to make an effort to discover if needed skillsets may already exist within the company.
“We will absolutely need people to think about the mathematics, statistics, and techniques necessary to create reliable endpoints that regulatory authorities will know are worthy of their attention,” adds de Vries. “We need to get mobile health data that will be substantial evidence to the regulatory agencies.”
But being in uncharted waters also brings more complicated challenges, which DiCicco refers to as preparing for the unknown unknowns. Companies are not able to predict every eventuality when launching a new technology. But DiCicco also believes that is not a reason to hold back on the adoption of mobile technologies. Too many other factors necessitate the implementation of new methods.
“The rising cost of drug development is in the news every day,” he says. “We need new tools to help bring down the costs and get needed medicines to patients faster. We may not always get it right the first time, but we need to do what we can to jump-start that process of evaluation and implementation.”