Suzanne Caruso

	Sponsors in drug development face several complex barriers to success, yet one of the most intractable challenges is patient enrollment. In fact, the issue of delayed enrollment not only impacts trial timelines, but more critically, trials are being canceled because of it, preventing investigators from determining the safety and/or efficacy of what could be promising new therapies. Specifically, in 

a review of more than 8,000 trials on clinicaltrials.gov

, 12% of trials were terminated, and of those, nearly 40% were terminated because of insufficient enrollment. 

	Many sponsors have long been addressing this problem with initiatives designed to find, attract, and retain patients in clinical trials more effectively. These approaches, however, may only scratch the surface of a much deeper-rooted problem: the dearth of available, experienced, and knowledgeable investigators who know how to navigate the increased complexities of today’s clinical trials and efficiently recruit patients.

	Sponsors want their studies to succeed, and most believe their trials will operate in an efficient, streamlined manner. Unfortunately, 

according to the Tufts Center for the Study of Drug Development (CSDD)

, the enrollment numbers are stacked against them: 37% of investigative sites under-enroll and 11% fail to enroll a single patient.

	These numbers come with a financial cost. A study from Covance found that between 2006 and 2010, the global pharmaceutical industry wasted nearly $2 billion on startup costs for non-performing sites.

	Furthermore, changes in trial protocols and development practices that aim to improve the success rate of clinical trials can actually make patient recruitment more difficult for investigators. As sponsors adopt innovative trial designs, such as adaptive designs that incorporate a number of different cohorts, it has become more difficult to meet enrollment targets. This is because in the era of precision medicine, in which clinical trials focus on narrower indications, investigators must be more selective in their recruitment efforts, leaving a smaller pool of patients that fit the eligibility criteria for a given study.

Investigators are being left out of the equation

	As clinical trials become more complex and varied, it is becoming harder for sponsors to find appropriate investigators that have relevant experience. Sponsors were once able to screen potential investigators for their experience in a specific disease. Now, in pursuit of precision medicine, sponsors are embracing so-called basket trials, wherein a drug is tested on a specific genetic mutation, rather than a disease. This requires that they find investigators who have deep experience in narrower indications; however, it’s challenging to identify an investigator’s area of expertise as it relates to mutations, as these data have not traditionally been tracked.

	The number of investigators involved in clinical trials is dropping precipitously. In fact, many investigators are dropping out after only one trial, fueling what’s being called the “one-and-done” syndrome. 

, funded by the Food and Drug Administration (FDA) and the Clinical Trials Transformation Initiative, showed that 54.2% of respondents were classified as one-and-done. Likewise, 

 found that the principal investigator (PI) landscape remains highly fragmented, with heavy turnover, particularly among inexperienced investigators. This conclusion resulted from a CSDD analysis of information from the Bioresearch Monitoring Information Database from 2008–2015, which revealed that half of all PIs who filed a single Statement of Investigator form 1572 each year chose not to file again in subsequent years.

	Our internal data sources show a similar decline in the number of investigators. In the last three years, the percentage of investigators experienced in conducting Phase 3 trials has dropped from 46% to about 22%, leading to increased competition to recruit the same experienced investigators. This problem will escalate as older investigators retire and new investigators attempt one trial, and then quickly drop out.

	When it comes to patient enrollment, investigator experience matters. According to our data, investigators with at least four studies under their belts had non-enrollment rates of about 13%, while those who had conducted three or fewer studies had non-enrollment rates of 25%. Consequently, as the pool of experienced investigators shrinks, enrollment rates in clinical trials will only continue to falter, applying greater pressure on an already fragile clinical trial infrastructure.

A potential solution: support young investigators

	To replenish the investigator pool, sponsors need to identify and invest in new investigators who may start as sub-investigators while being mentored by the PI, and who may possess a tier-2 or 3 track record of enrollment. Their only hurdles to becoming top-tier investigators are a need for clinical research support, and training with respect to their responsibilities. Support could entail:

		Study coordinators who will do on-site medical records research to identify and contact potential enrollees, schedule meetings with them, and conduct screenings

		Timely Institutional Review Board (IRB) submissions and approvals

		Timely budget, contracts, and payments processes

		Bonus compensation for reaching critical milestones

	Sponsors also need to do a better job tracking investigator experience, taking into account factors such as how many patients an investigator has previously enrolled, and the number of studies they’ve completed. They must also start tracking and capturing an investigator’s experience using factors not historically captured, such as experience on trials focused on specific gene mutations, the number of patients screened for previous studies, and/or the number of study coordinators at the site. Finally, a sponsor’s profile should contain data that can help sponsors evaluate protocol feasibility, contract research organizations, investigative sites, study designs, relationships between investigators, and performance data on FDA-regulated investigators.

	Sponsors often gather these data from databases such as Citeline, the Shared Investigator Platform, and 

. However, none of the information housed in these databases is independently verified and, therefore, may be subject to inaccuracy caused by sponsors who may be misremembering or forgetting critical information. On the other hand, WCG Clinical’s 

 is composed exclusively of FDA-verified information. Their FDA-verified information consists of experimental protocols.

	Just as precision medicine’s aim is to develop the right drugs for the right people, a sponsor’s aim should be to find the right investigators for the right trial-not necessarily the most experienced investigators, but the ones who are familiar with the appropriate techniques and disease areas for a study. With a little training and support, these investigators are better positioned to deliver the results sponsors need, including an efficient recruitment program that can save a trial from likely failure.

is Vice President of Clinical Solutions, WCG

Articles

Sponsors have been addressing the problem with initiatives designed to find, attract, and retain patients in clinical trials more effectively.

Investigator Engagement: The Key to Solving the Clinical Trial Enrollment Crisis

	The emergence of big data sets, natural language processing, and machine learning has created the opportunity to solve inaccurate, inefficient, and misinformed site selection practices through predictive modeling. Companies that fail to implement predictive modeling solutions that analyze data to determine the probability of achieving their target outcome when making their site selection decisions are at risk of falling behind their industry peers. This article discusses how once-separate data sources can now work together, allowing biopharmaceutical companies to evaluate investigator performance trends. Sponsors should leverage this information to improve their site selection strategy, allowing them to switch from choosing only investigators they know to building new relationships with investigators that deliver predictable strong performance.
	    
	Historically, clinical research teams at biopharmaceutical companies have worked with the same institutions and investigators repeatedly, relying on internal databases and personal referrals for site identification regardless of the results generated. During the past 10 years, biopharmaceutical companies have begun purchasing subscriptions to external clinical research resources in an effort to improve their approach. These data subscriptions draw from more than 50 publically-available global clinical research information repositories. With all of this new information, one would have expected an increase in the number of new relationships between biopharmaceutical companies and investigators. However, that has not been the case. Poor performance cannot be solved simply by purchasing data sets. These data are only as valuable as their most predictive element of future performance and most sources lack the most important one: enrollment data. Furthermore, when key performance predictors (KPPs) such as enrollment information are included in the data set, they must be organized and structured so they can be analyzed. Biopharmaceutical companies are now starting to require that all data are mined to generate structured information that can be converted into a predictive model that allows their clinical teams to evaluate the highest probability of investigator performance.  
	
	Biopharmaceutical companies are recognizing that they cannot lead the industry from a data silo. They need to partner with providers who have these data capabilities in order to build the most robust data engine and subsequent predictive analytics tool using structured source performance information. By partnering in this way, biopharmaceutical companies can take advantage of the largest informed data sets available.    
	
	Patient enrollment rates and, more specifically, randomization rates, have consistently been shown to be the best predictors of future clinical site performance. However, depending on the therapeutic area, there may be additional valuable predictors. For example, when a sponsor is planning to open a heart failure study, which is one of the busiest areas of clinical research today, there are more than 5,000 US investigators to choose from. How should the sponsor narrow the investigator pool to find the best 100 for its study? It is no longer acceptable to choose investigators randomly from a list based on location or familiarity with the medical director. The crowded investigator landscape demands that KPPs, such as the investigator enrollment rate, number of competing trials, and number of patients randomized be used to run a model to determine the 100 investigators most likely to enroll subjects for that clinical research trial. Similarly, with an ovarian cancer study, it might make sense to choose investigators that have experience using specific companion diagnostics, in addition to desirable enrollment rates. It is essential that sponsors have a dynamic data analytics tool that has the flexibility to weigh the KPPs that they value most for their trial. While having actionable intelligence, such as companion diagnostic experience, is of paramount importance, how that information is used is also crucial.  
	
	The amount of information being collected from the 40,000 active global investigators is immense. Without a dynamic way to organize and analyze that volume of information the clinical trial data landscape becomes so unwieldy it is impossible to draw meaningful conclusions. Predictive modeling and trending allow biopharmaceutical companies to remove noise from the data and focus on making the right decisions with actionable data. For example, predictive modeling can analyze the number of investigators who have completed at least three ovarian cancer trials and can determine, on average, based on the number of patients needed to reach statistical significance, how many sites should be opened to meet the target date for the last participant’s last visit. That model could also show how many more investigators would be needed to exceed expectations and surpass the target date for the last participant’s last visit.
	
	Predictive modeling provides a way of evaluating what is “average.” Having real data on which to model performance pushes sponsors and investigators to evaluate how they can improve. If sponsors know that certain investigators always exceed expectations and enroll at a higher rate than average, they can refine their pool to include only high-performing investigators. They can then focus on which of those investigators are familiar with the diagnostic they want to use. The ability to focus on the needs of the specific protocol through the use of predictive modeling is the ultimate differentiator. Hitting average timelines with a mix of high-performing and low-performing investigators is no longer acceptable. Sponsors can now remove the low-performing investigators from their investigator pool and increase the odds of tripling their enrollment rate by making smarter informed choices from the beginning of the study.
	
	Sponsors should use investigator performance information to ensure that their clinical trial timelines are achieved or exceeded. It is only through the use of predictive modeling that big data becomes actionable. It is time for biopharmaceutical companies to stop looking inward and start looking outward toward partners with the appropriate tools to gain access to performance data that inform the most important clinical trial decision a biopharmaceutical company makes, namely, site selection.
	
	

, VP Clinical Solutions, WIRB-Copernicus Group

Predictive modeling has allowed biopharma companies the opportunity to solve inaccurate, inefficient and misinformed site selection practices by leveraging big data.

Suzanne Caruso

Articles

Investigator Engagement: The Key to Solving the Clinical Trial Enrollment Crisis

How to Leverage Big Data to Improve Clinical Site Selection