The word agility is thrown around a lot these days, to the point where it’s practically a buzzword. 

For some industries, like pharma and life sciences, the ability to incorporate agility can directly impact people’s lives. This is especially true when it comes to clinical trials.

When you think of some of the factors that decrease clinical trial agility, the pandemic immediately comes to mind. In 2020 alone, over 79% of ongoing clinical trials were disrupted, whether for pausing recruitment, new site development, or stopping trials altogether. 

As pandemic-related roadblocks dissipated, however, clinical trials continued to face agility issues. Why is that? It turns out that clinical trials struggled with efficiency problems well before the pandemic.

Between 2008 and 2013, for a new series of drugs to be FDA-approved, the clinical phase took an average of 83.1 months. That increased to 89.8 months between 2014 and 2018. In the same timeframe, the study start-up period increased 13.7% (averaging 145 days) and the LPLV timeframe increased 9%.

What this tells our industry is that study duration is increasing, and has been for some time. But what we hear from colleagues and patients is that diseases are impacting modern life at a faster pace. Therefore, the ability to expedite clinical trials from start to completion is more critical than ever. 

Let’s take a closer look at three trial factors that impact agility and how to improve them: 1) Trial and Protocol Complexity, 2) Data/Analytics, and 3) Processes and Technologies.

Factor 1: Trial and Protocol Complexity

Ask: Am I doing enough to assess potential burdens and minimize deviations or added criteria? 

Protocol complexity, in particular, has become a significant clinical research problem, adding unplanned time and cost to trials. What’s contributing to this problem? According to one study, there’s been “an increase in deviations for all trial phases, as well as an increase in the percentage of protocols with at least one substantial amendment.” 

Deviations, whether caused by investigators, site staff, or subjects, interrupt progress and extend the trial timeline. Amendments require additional filing and approval time and can increase site burden while affecting subject retention. Both call into question the clarity of the trial protocol. 

Another contributor to complexity is an increased number of trial objectives, endpoints, and eligibility criteria. From 2013 to 2020, objectives increased 15.9% in Phase 1, 11.6% in Phase 2, and 17.6% in Phase 3. And, over the past 20 years, the average number of endpoints has gone up 86%, while eligibility criteria have risen 50%.

The cumulative impact of these added burdens is slower trial progress at a time when results and potential new treatments are in high demand. 

Try: Changing your approach to protocol preparation.

A good way to counteract protocol complexity is to spend more time planning up front. Carefully identify and assess potential burdens on patients and caregivers, paying particular attention to initial steps. Since Phase I protocols tend to be the most complex, this initial attention to detail could help a trial begin faster.

Another way to encourage agility is to increase participant engagement. Do what you can to streamline their side of the process—from reducing the number of visits to streamlining communication to simplifying technology use—in order to guard against deviations as the trial progresses.

One final way to support more agile trial processes is to adopt data platforms and tools, like modern clinical data management systems (CDMS) and hybrid clinical trial platforms, that can expedite data gathering and analysis. 

Factor 2: Data/Analytics

Ask: Is every piece of data I’m collecting useful and necessary toward achieving my end goal?

Too many cooks can slow down a kitchen. And too many data sources can slow down clinical trial analysis and data evaluation. The result is delays and decreased…you guessed it…agility.

As if they weren’t already complex enough, protocols are now being designed to collect significantly more data. Increased use of external data sources, like wearables, is one reason. The digital evolution that has enabled constant data collection is another. 

But just because you can, doesn’t mean you should. 

Extensive data volume impacts both data management and cycle times in trials. According to CenterWatch: “…the largest challenge is the coordination of multiple data sources, some that include the electronic data capture as well as the use of wearable devices, specialty labs, and a variety of other data sources…”.

Increased data collection increases burden all around. 

Trial participants have more to keep track of, trial staff require longer training periods to understand the technology behind every collection point, and data managers are challenged to incorporate diverse data sets and manage data from disparate sources. This trend even impacts monitors, who now take approximately 72 days to conduct onsite source data verification for a study.

Try: Focusing on the here and now when it comes to data collection.

Oftentimes, data is collected not because it’s relevant to the study, but because it may be useful in the future. Changing your mindset to focus on study-specific criteria would go a long way toward increasing trial speed and decreasing timelines.  

Like trial and protocol complexity, wider use of CDMSs could streamline data and analytics, too. Especially in the start-up phase, where EDC builds take a mean cycle time of 69 days from protocol approval to database go-live. 

Unified clinical systems on the part of the CRO could also help by improving visibility and oversight, as well as study quality. The key is connecting systems so they automatically exchange new records with existing subjects.

Factor 3: Processes and Technology

Ask: Do I have the right technology and trial processes in place, and am I using them the best way?

When it comes to the how and where of clinical trials, some would advocate for siteless trials as a way to increase agility. 

Yes, they’re convenient and, in some respects, help address resource issues. But I’d argue that completely siteless trials can actually detract from agility by putting more responsibility on patients and by slowing down processes due to the extra technology training required. In addition, with continued emphasis on patient-centricity, how can that be maintained in a siteless environment?

Try: Turning to remote monitoring, RPA, and hybrid trials.

Instead of pushing siteless trials, look to other processes and technologies you can use to increase agility while still supporting safety and accuracy.

Remote monitoring: this technology helps streamline data collection. For example, a CTMS plays an important role in ensuring all clinical research team members can work together from a remote standpoint in order to assimilate data. While remote monitoring can be used in siteless trials, it can also be applied to hybrid or traditional trials.

RPA: Robotic Process Automation (RPA) can greatly increase agility and be helpful in meeting new clinical testing criteria focused on using technology to raise study success rates, which remain stubbornly in the range of 40-80%. RPA lets you automate patient matching and approach initial recruitment from a virtual standpoint before clinical staff step in. 

Hybrid trials: With hybrid trials, you get the best of both worlds—more automated processes that may use fewer data sources, combined with a reduced burden on patients and sponsors—to help keep activities moving forward at a faster pace and by taking the opportunity to bring the clinical trial to the patient. 

Conclusion

The number and complexity of clinical trials continues to grow. To make up for progress lost due to the pandemic, and to ensure roadblocks to efficiency can be removed, trial agility must become a top priority. But it can also take time to find the right measures to make a lasting improvement.

Advanced Clinical believes there isn’t a one-size-fits-all approach. The needs of the sites and patients must be considered first. That will ultimately help you uncover the areas of opportunity where you can make a clinical trial more agile.