Gen Li, PhD

Analysis from virtual clinical development specialist Phesi shows the enrollment and effectiveness of two current COVID-19 vaccine trials conducted by global pharmaceutical development companies. On the surface, both trials appear to be on track to complete enrollment, however, looking deeper, there is a significant difference in the Site Effectiveness Index (SEI) between the two, with trial A scoring 84 percent and trial B scoring 39 percent. SEI is a metric to measure how effectively a clinical trial site activation process is executed.

Trial A completed enrollment on October 22, 2020, with 30,000 subjects, activating 99 sites in three months. After adjustment for site activation, each site enrolled 4.15 subjects per day. Trial B has activated 149 sites by October 22nd, 2020 but has been recruiting for over five and a half months.

Dr. Gen Li, President of Phesi, commented, “We estimate that it will take 175 sites to be activated and six months for Trial B to enroll its target of 43,998 subjects. If we make the same site activation adjustment (SEI 42 percent) it would enroll 3.75 subjects per day, significantly lower than Trial A.”

“This difference contributes significantly to the disparity in the observed SEI levels between the two trials, but another contributing factor is that Trial B has implemented a number of protocol amendments. Making protocol amendments is a known factor in many trials exceeding their projected timeframe and budget,” said Dr Li.

The amendments to Trial B included reducing the restrictions on the age of subjects and altering the target number to enroll from 8640 patients, to 7600, then 32,000, then 29,481. It now targets to enroll 43,998 subjects. Trial B also has an adaptive trial design, which should theoretically lead to an overall shorter development cycle, bringing the vaccine to patients faster – but often at the expense of higher financial costs.

“The data from the two trials show that in the hunt for a COVID-19 vaccine, there is varying success in activating investigator sites and enrolling patients, which must be tackled to accelerate development,” continued Dr Li. “In this comparison, the difference of enrollment cycle times between the two trials is three months vs. six months – a considerable time period in a global pandemic. Bringing innovative and life-saving medicines to patients is complicated and capital intensive. The solution to improving SEI and accelerating development requires a deep understanding of real-time, dynamically refreshed data and a forensic examination of the

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Analysis from Phesi shows the enrollment and effectiveness of two current COVID-19 vaccine trials conducted by global pharmaceutical development companies.

Analysis Shows Differences in COVID-19 Vaccine Trial Recruitment

	The impact of COVID-19 continues to be enormous; since the onset, billions of people have experienced some form of ‘lockdown’. With countries now starting to ease shelter-in-place conditions, the thoughts of many are slowly turning to getting back to ‘normal’, however long away that might be. The field of clinical trials is no exception and finds itself at a potential turning point. Although current circumstances are challenging, there is also now an opportunity, when ‘normal’ service resumes, to make long-lasting changes that benefit biopharmaceutical companies and trial sponsors, but most importantly, benefit patients. One of the key determinants in doing this will be an improved use of data, and the current hiatus is an opportunity to embrace this approach.

	Trial infrastructure has been hit hard by COVID-19; recent data from Phesi analyzing more than 300,000 global sites found a 38% increase in suspensions since the beginning of the year (Figure 1). The initial conclusions seem obvious-different trials for different indications, started at different times, sponsored by different companies, all put on hold as the pandemic began. But it is not quite this straightforward, nor is it entirely the fault of COVID-19. Undoubtedly, many trials have been suspended because of the pandemic, but there are some other longer-standing issues currently being masked by the virus. 

	One of these issues is that many trials are poorly designed from the outset so their execution is intrinsically flawed-and would be highly likely to fail or require rescue, even if COVID-19 had never happened. Our experience shows us that poorly executed site activation is one of the main drivers of slow patient enrollment behind the downward curve in trial activity, but this is currently being apportioned solely to the impact of COVID-19. 

	A second issue is competition between studies and sites; this has a significant impact on patient enrollment and now in many cases will be disguised as an outcome of COVID-19. Figure 2 highlights this problem; the number of recruiting sites in Crohn’s disease increased in the three years before COVID-19 from under 2,000 to more than 9,000-despite a limited patient population. The challenge is compounded by poor country allocation and investigator site selection, not by the pandemic. The reality is that at a future point, some of these programs will inevitably have to be terminated so the focus can be placed on those with the best chance of succeeding.  

	The issue then becomes deciding which sites should be terminated. And without a deep understanding of the data, it may be those chosen to restart will be the programs most likely to fail, rather than those with a higher probability of reaching patients and enhancing treatments. Indiscriminately suspending all trials then bringing them back to life without a true understanding of what might work, inevitably means some of the ‘zombie’ trials that are prone to failure will be resurrected. 

	Site (re)activation costs $30,000 at a conservative estimate and takes around 100 days. Data and tools exist that can improve the planning and execution of site activation-for example, helping to identify high-performing investigator sites or finding the most relevant CRO with a performance record in the indication under study. Sponsors must tap into this knowledge to be able to bring down site activation time and cost and minimize the risk that a trial will be unsuccessful or require a later rescue.

	We knowthat even without COVID-19, around 1/5 of trials fail, so the key to overcoming these hurdles is using data to optimize trial design and execution. This enables sponsors to identify all the variables associated with their study; analysis of the clinical site space for a therapeutic area-including geographical location, existing studies, and patient population-builds better protocols with a higher chance of success. 

	In addition, almost all Phase II and III trials will undergo one or more protocol amendments at some point, so it would be wise for sponsors to take advantage of the current slowdown to refine the design now using available data. It’s also likely that given the reduced level of in-person contact caused by social distancing, synthetic control arms will go from being a ‘fashionable’ term pre-COVID-19, to a fundamental part of any successful clinical development portfolio post-COVID-19, as the reliance on analytics and data (including real world data) grows. 

	The COVID-19 pandemic has caused significant global disruption, bringing the industry to a turning point by highlighting the existing issues with trial design and execution. The industry needs to address these problems now to ensure a long-lasting, positive impact on drug development and commercialization. Those companies who make the switch to become data-driven will better navigate the challenges ahead and will ultimately emerge stronger.

With the COVID-19 pandemic highlighting the existing issues with trial design and execution, the industry needs to address these problems now by becoming data-driven to ensure a long-lasting, positive impact on drug development and commercialization. 

Resurrecting Zombie Trials Will Be Another Consequence of COVID-19 Unless the Industry Acts Now

Planning clinical trials is challenging. A significant portion of clinical trials cost more than $100 million each and last several years. By any standard, this is a major capital investment, requiring extensive planning. The complexity of conducting a clinical study is not only reflected by a huge number of variables from the operations management point of view, but also the fundamental reality that clinical trials are medical and scientific experiments. The questions for a clinical trial planner to answer are often in conjunction with those in operations management and medical development.

	While clinical trial planning has been a weak spot for our industry, it is encouraging that many leaders have recognized the importance of planning properly, evidenced by the creation of dedicated clinical trial planning groups, often known as feasibility groups. However, there has been limited progress: delays are still a chronic symptom, costs are ballooning, and rescue missions are a regular part of the trial process. The challenges facing clinical trial planners remain the lack of data and, more importantly, the lack of a platform to interpret the data.

	We recognized that there was no easy fix to these challenges. Over the past 10 years, along with building an extensive and dynamic clinical development database, a set of innovative and coherent concepts has been created to help us to interpret the data, to enable us to effectively plan clinical trials.

	In this article, we use a hematology clinical trial as an example to illustrate how the PhESi clinical trial planning platform was utilized to aid the clinical trial team and facilitate the communication between the team and key stakeholders, namely the senior management.

	The study team faced a familiar scenario: a promising drug candidate, high expectations, demanding timelines, and limited indication experience. This hematological clinical trial would be the first, large-scale, randomized clinical trial for this indication.

	The PhESi platform takes an integrated approach in clinical trial planning. We assess the competitor landscape, including where and when the planned clinical trial will be conducted. Business processes, especially site activation processes,

 are examined in multiple dimensions. Trial design, specifically patient inclusion/exclusion criteria, is comparatively analyzed against similar trials conducted by the industry, by focusing on how these design elements can potentially impact planning parameters and operational deliverables. Last but not least, all the participating investigator sites in relevant clinical trials are assessed by going through a series of proprietary algorithms and statistical models.

 Those that pass this vigorous selection process are provided to the team as site candidates for the trial being planned.

	It is important to emphasize that while our analysis is structured and systematic, not all the detailed results are presented to the team. We focus on providing a set of actionable recommendations the team can act on, substantiated by our analysis, and specific to the particular clinical trial being planned.

	Briefly, the team was asked to conduct a hematology clinical trial, targeting enrollment of 292 patients. The sponsor was hoping to complete enrollment in 22 months.

	There was a limited number of randomized clinical trials similar to our hematology trial. Simply using historical data for our planning was not possible. Extensive historical clinical trial data analysis has revealed that there is a reliable pattern between number of investigator sites (N) being deployed in a clinical trial, and gross site enrollment rate (GSER, defined as the number of patients per site per month). That pattern, with minor modification, can be established for trials in this hematology indication:

	Clearly, for a trial that planned to use over 100 sites, it was not realistic to expect an enrollment rate of more than 0.11 patients per site per month. However, as a mathematical relationship can tell us, the GSER could drop below 0.10 patients per site per month, though not significantly.

	The below chart summarizes the distribution of site size for clinical trials in this hematology indication completed in the past 10 years or so, as measured by the number of patients per site. It is clear that most of the hematology clinical trial sites enrolled 1-3 patients on average.

	The median number of patients per site for trials is 3.0. Knowing that enrollment rate decreases as the number of sites increases, we need to add more sites than the median number of patients per site implies, which requires about 100 sites to enroll 292 patients. Considering these factors, we recommended that the team should use 120 sites, with expected enrollment of 2.4 patients per site.

	One question commonly asked is whether we can proportionally reduce enrollment cycle time by proportionally adding more sites to the mix? The simple answer is no. Among many other reasons, there are operational boundaries in terms of how many sites we can activate that would contribute to enrollment. For a randomized interventional trial targeted to enroll a defined number of patients, we can expect to see an initial shortening of enrollment cycle time when adding sites. As we continue to add more sites, the benefit decreases, eventually resulting in prolonged enrollment cycle time when an excessive number of sites were added to the mix.

 Empirically, we can consistently identify the optimized area (sweet spot), where an adequate number of investigator sites can help us to achieve the shortest enrollment cycle time.

	In planning our trial here, we had to define the optimized number of sites with similar logic, since we could not calculate that “sweet spot” empirically without sufficient historical data.

	Now let us focus on site activation. We examined over 1,000 interventional clinical trials conducted by the sponsor of our trial with 10 or more active sites. We found that only 6% of these studies were able to activate 50 or more sites 100 days after the start date. When we focus on the trials required to activate more than 50 sites, 20% of the trials were able to activate more than 50 sites in the first 100 days. As depicted in the following chart, even if we put more resources into the larger trials that require a larger number of the sites to be activated, the percentage of trials that can activate 50 or more sites in 100 days plateaued at 25% to 27%:

	In other words, only a small percentage (~25%) of large-scale trials can activate 50 sites in 100 days-it is, therefore, an overly ambitious performance goal.

	Is it feasible to enroll 292 patients in 22 months, when using 120 sites?

	We learned from historical sponsor and industry data that it would take an extraordinary effort to activate 50 sites in the first 100 days. From our analysis, it usually took even longer to activate the next 50 sites. If everything played out to our favor, we would be able to activate 120 sites in nine months (270 days). In those nine months, we could expect to have 60 sites open and enrolling patients on average (assuming a straight site activation curve between month one and month nine). We only had 13 months to fully utilize the enrollment capacity from all the 120 activated sites. In summary, we would only be able to utilize 80% of enrollment capacity from the 120 sites deployed [(50% x 9 months + 100% x 13 months)/ 22 months]. That 80% is referred to as the Site Effectiveness Index (SEI), which measures the success of the site activation process.

	This required us to enroll 0.138 patients per site per month. This enrollment rate was still 26% higher than the historical enrollment rate of these much smaller hematology clinical trials [(0.138/0.11-1)]. Neither an SEI of 80%, nor an adjusted site enrollment rate (ASER) of 0.138 patients per site per month was obtainable.

	ASER, which is the GSER when we assume all the sites can be activated in day 1 of the trial. Mathematically, GSER equals to ASER times SEI. For a given protocol, ASER can be improved through a better site selection process.

	In order to help us to define a more realistic SEI, we examined the site activation curve of a trial with a similar indication from the same sponsor:

	In this trial, the site activation index was 60%. This is one of the best executed site activations by this sponsor in the same therapeutic area. For an ASER of 0.11 patients per site per month, GSER is 0.067 patients per site per month (0.11x60%=0.067). This led us to recommend that the sponsor should use 120 sites to enroll 292 patients, and we forecast an enrollment cycle time of 36 months (292/(120x0.066)), instead of 22 months.

	The enrollment was carried out from the beginning of August 2012 to the end of March 2015, a total of 32 months. The following chart shows the actual site activation curve:

	This chart helps to visualize some of the issues in the implementation of this trial. Thirty percent of sites were activated in the last six months of the enrollment period. For that reason, the enrollment contribution from those 77 sites was very limited, leading to the majority of those sites being nonperforming sites.

	While we recommended using 120 sites for the trial, the team actually activated 227 sites instead. However, when we examine the results closely, there were only about 140 sites that contributed patients, much closer to the number of sites that we suggested. From PhESi internal analysis, it is consistent that the nonperforming sites increase proportionally when more than the optimized number of sites are used.

	The SEI for the site activation process was 44%, lower than the 60% SEI value that we recommended.

	The forecasted GSER was 0.067 patients per site per month. The actual GSER, calibrating the impact from the 77 sites, was 0.061 patients per site per month.

	Ironically, we recommended multiple batches of site activation in our plan:

Only activate 70% of the sites in the first batch. This is based on a limited number of experienced sites in clinical trials in this hematology indication and draining energy during a long enrollment period. At six months, we can activate the remaining 30% of PhESi-recommended sites, as they are identified as top enrollers in the next six months (post FSI). This is the choice between 30% of the second-class sites with six months-longer enrollment time, or 30% of top-rated sites with six months-shorter enrollment time.

	The implementation of a two-batch site activation would have allowed us to more accurately define the number of sites required in the second batch-and when the second batch of sites should be deployed based on the site performance of the first batch of sites. This would maximize the benefit of shortening enrollment cycle time. Taking this approach, it would have been entirely possible to have achieved a 32-month enrollment cycle time by using 120 sites, as we had recommended.

	Traditionally, we measure patient enrollment by the number of subjects enrolled per site per month, which we call the enrollment rate. This measure implies that patient enrollment is basically a site performance issue. In other words, defining site enrollment performance with a metric that is focused on the site enrollment rate suggests that the way to improve recruitment cycle times is by focusing on getting the best possible sites and site-level enrollment enhancement initiatives. Some sponsors formulate their business processes based on this assumption. Not surprisingly, these organizations often run into costly situations when targeted enrollment timelines are delayed.

	In this case study, as in any others that we have worked on, it has been demonstrated that site activation is an important driver in determining enrollment cycle time.

 There are operational limitations on the number of sites that can be activated in a defined time period, which restricts the total number of patients that can be enrolled.

	Another area of limitation is the question of how many sites can and should be deployed in a trial. The dominant thinking in our industry is still misguided on this point. Sponsors believe that if more sites are added to a trial, they will be able to proportionally reduce enrollment cycle time. The obvious fallacies for this thinking is that it implies that if we can add an infinite number of sites to a trial, then we can reduce the enrollment cycle time to near zero!

	In our analysis, for clinical trials in any extensively studied disease indications, enrollment cycle time can always be minimized (the “sweet spot”) by deploying the adequate number of sites (PhESi internal analysis):

	When we step too far out of the sweet spot, (for example, extrapolating the enrollment rate from a Phase II trial to a Phase III trial in the same development program) we can end up in a rescue situation (PhESi internal analysis):

	Objective feasibility gained from our analysis in this cancer trial also allowed the sponsor to improve its relationship with its contract research organization (CRO) partner. By incorporating improvement actions recommended by PhESi, the trial completed in the time frame we recommended.

	Fortunately, the problems described in this article were identified in the planning phase, which allowed the sponsor to avoid potential conflict among key stakeholders, and prevented another possible rescue mission from being required.

	Retrospectively, our plan was not perfect. We should have explicitly spelled out the nonperforming site issue. If we had incorporated nonperforming sites, we could have recommended to the team to deploy 150 sites, expecting 120 sites to enroll one or more patients. Having learned from this experience, nonperforming site analysis is now a regular component of our analysis.

, PhD, Senior Director, Clinical Operations, 

, PhD, Founder and CEO, PhESi, email: gen.li@phesi.com

Acknowledgement: Valerie Legagneur and Karen Correa were formerly employees of Pfizer, Inc. 

	1. Gen Li, 2015, Forecast Enrollment Rate in Clinical Trials. 

	2. Gen Li, 2009. Finding the Sweet Spot. 

. Published October 2, 2009. Available at: 

http://www.pharmexec.com/finding-sweet-spot?id=&pageID=1&sk=&date=

	3. Gen Li, 2008. Site Activation: The Key to More Efficient Clinical Trials. 

. Published December 12, 2008. Available at: h

ttp://www.pharmexec.com/site-activation-key-more-efficient-clinical-trials

	4. Gen Li, 2009. SITE EFFECTIVENESS INDEX AND METHODS TO MEASURE AND IMPROVE OPERATIONAL EFFECTIVENESS IN CLINICAL TRIAL EXECUTION. U.S. patent publication No. 2010-0250273

	5. Gen Li, 2014, METHODS OF FORECASTING ENROLLMENT RATE IN CLINICAL TRIALS, U.S. Serial No. 14/818,438, filed August 5, 2015, which claims the benefit of U.S. Serial No. 62/033,844 

Case study shows that site activation is a key driver in determining patient enrollment cycle time, which can be minimized by identifying the "sweet spot" of sites to deploy for a clinical trial.  

Enrollment Cycle Times Can and Should Be Optimized

	We recognized that there was no easy fix to these challenges. Over the past 10 years, along with building an extensive and dynamic clinical 

development database, a set of innovative and coherent concepts has been created to help us to interpret the data, to enable us to effectively plan clinical trials.