Interview: Emerging trends in decentralized clinical trials post COVID-19
In our recent interview with Dr. Jitesh Bhatt, Vice President of Clinical Product at Wellthy Therapeutics, we explore how the clinical trials landscape has shifted in terms of DCT adoption, emerging technologies, and barriers, and a future outlook.
Netscribes: How do you think COVID-19 has accelerated the adoption of decentralized clinical trials? How is the industry shifting to adapt to this change?
JB: In a clinical trial, there are four key stakeholders. First is the sponsor of the trial. Second is the CRO that is conducting the trial. Doctors make up the third group. The fourth and most important is the patient community. When it comes to the relationship between these four key stakeholders in any clinical trial, the shift to decentralized clinical trials began as early as 2015-16. Still, it wasn’t being considered as something we’d do to bring drugs to market faster.
COVID-19 accelerated this shift. People finally have the technology to facilitate this change. This technology did not exist 5-6 years ago. Whether it’s data transfer speeds or secure data collection devices, we needed them. A serious mindset change was ultimately brought about by COVID-19. Those who previously considered this shift to be impossible have realized there’s no better time nor better choice than now.
Netscribes: What changes do you think have occurred in patient attitudes towards DCT adoption?
JB: Clinical trial patients can be divided into two categories: those who are already motivated and those who need motivation. Digitally inclined patients need no other motivation to join virtual trials. They are excited about this opportunity since they will no longer have to worry about hassles such as traveling, visiting the diagnostic center, etc.
Some patients, however, are not always computer savvy and may not be interested in participating in the trial. Clinical research plays an important role for them. Organization (CRO), the sponsor, and their physicians become very important to carry forward the process. Ultimately, a huge amount of training is required for such patients to understand how the virtual trials are conducted in their own home setting.
Netscribes: We’d like to know more about the regulatory landscape that has evolved due to the pandemic. How is it impacting DCT adoption specifically in India, and also globally?
JB: Decentralized clinical trials face the challenge of cross-functional alignment. These days, most clinical trials are done electronically (e-COA). Through the e-COAs, the regulators have made it very easy for sponsors and CROs to process trial data more quickly. As a result, the time-to-market is reduced. e-COAs also make filing for regulatory approvals more convenient.
However, e-COA vendors have a huge dependency on other third-party vendors, who take care of where the data is coming from, how it’s coming, and its compliance. The complexity of global compliance processes has not been reduced even with the advent of COVID-19.
Now we need to look at the existing regulations and figure out how to comply with them. We would eventually benefit from efforts to make the e-COA platforms stronger and more electronically accessible.
Netscribes: So are there any practical issues that are affecting decentralized clinical trials?
JB: Many, actually. But there’s nothing that can’t be mitigated by technology. One area, I would like to talk about is the role of the principal investigator. The principal investigator is responsible for inclusion and exclusion criteria, for protocols, and for ensuring that patients are recruited, protocols are followed, diagnostics are carried out, drugs are given, patients are tracked, and ECOAs are submitted.
It is a practical challenge to train and upskill the principal investigator to be well versed in the new virtual environment. But training is the only solution. Among the components needed to conduct a clinical trial, the human component right now is the weakest link, which requires upskilling.
Netscribes: As we move forward, are there any key therapeutic areas (TAs) where we can see high DCT adoption?
JB: The TA for chronic diseases was the only one in the US that discussed DCTs and DCT-enabled therapies in 2007-2008. Virtual trials were conducted at that time by nurses picking up the phone and talking to patients. The most advanced technology available at that point was a text message.
Rare chronic diseases were the first to explore DCTs. They were followed by common chronic, such as the cardio-diabetic domain. These two have maximum traction in terms of the development of newer therapies. The infrastructure to collect real-world evidence from the patient’s home is now available, which is facilitating virtual trials. Examples include electronic ECGs, mobile apps, connected device ecosystems, digital mental health platforms, etc.
Netscribes: Based on your experience, what are some of the big shifts coming next for clinical trials?
JB: The first shift is the use of Natural language processing (NLP), through the use of conversational AI in DCTs. To help maintain the patient-physician relationship, and engagement, conversational AI is extremely helpful. It picks up a lot of data from the patient, during the trial.
The second shift would be imaging AI or image-based AI solutions. It has a huge role to play, especially in trials that require radiology.
The third shift would be, multiple connected devices on a single platform (Internet of Medical Things or IoMT). These platforms, for example, can integrate an ECG device, a connected weighing scale, a connected blood sugar monitor, a connected blood pressure machine, etc. on one platform. This platform is then able to monitor the vitals of the patients on a higher level, which is extremely helpful.
Netscribes: Are there any prominent technologies that are being used in trials now? What would be their impact on the future?
JB: Once again, I would like to shed light on NLP and imaging AI. These are at an early stage of evolution. Currently, conversational AI is used for sentiment analysis. The system understands keywords as the patient speaks. The technique is mainly used in mental health and helps understand the patient’s feelings and respond accordingly. Within the next five years, this type of AI will be able to collect not only cognitive data but also clinical data.
Secondly, I’d like to discuss predictive analytics platforms. Clinical data analytics is currently divided into two parts. Preventive medicine is the focus of one set of vendors. Another set focuses on predictive analytics. With regard to DCTs, home-tracking devices can alert doctors if a patient’s vitals are abnormal. It is called preventive technology.
In contrast, predictive technology collects all the diagnostic information available regarding the patients over time, as part of the longitudinal patient history. After the patient enrolls in a DCT, a lot of analytics can be done using that data. It helps paint a bigger picture of the patient, in terms of their risk profile, precision medicine, how they would react to a particular drug, etc. Such AI-powered prediction of illnesses, rather than just prevention, is the future of the industry.
Netscribes: How can real-time data collection errors be minimized?
JB: The role of the principal investigator, as well as that of the platform, becomes crucial in this case. Roles aren’t new. Protocol-driven clinical trials have always been the norm. The solution for reducing these issues lies in rewriting the digital protocols. Having it rewritten can reduce the chances of errors, as it will ensure that the necessary precautions are taken.
These digital protocols also need to be rewritten while keeping in mind how IoMT and other emerging technologies are changing clinical trials. Additionally, the new set of professionals who would be conducting the DCTs, need to be trained properly.
Netscribes: Are there any barriers to technology adoption in clinical trials?
JB: Humans are the barriers. As more and more training is given to clinicians, as more mindsets are changed, these barriers would be mitigated. For patients, the clinicians involved in the trial need to come up with value models to improve their attitudes and engagement.
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