Executive Director and Professor, Tufts University School of Medicine

Ken Getz, MBA

During the past several years, in the wake of the COVID-19 pandemic, investigative sites have faced unprecedented challenges executing clinical trials. In additon to administering more complex protocols, finding patients under more stringent eligibility criteria, and retaining patients under more onerous participation requirements, sites have had to accommodate the use of a variety of remote and virtual technologies to collect and manage clinical trial data. At the same time, chronic operating challenges including inadequate study budgets, protracted contract negotiations, punitive hold-back payments, and poor CRA quality and continuity have continued to plague investigative sites. And recently, investigative sites have encountered higher levels of staff turnover and shortages.

The Tufts Center for the Study of Drug Development (Tufts CSDD) recently conducted a survey to understand the current mindset, experience and outlook of a global community of investigative sites. The survey endeavored to garner the perspectives of the “typical” investigative site and to understand variation in perspectives and experience by site type and by personnel function.*

This article presents highlights of the survey results and offers insights into ways that sponsors and CROs can strengthen their relationships with sites in this current, post-pandemic clinical trial operating environment.

Survey methodology and data characteristics

The survey was conducted online between October and December 2022. To reach a global community of investigative sites, Tufts CSDD used proprietary contact lists and partnered with the Association of Clincial Research Professionals. A total of 205 responses were collected.

The majority of respondents are employed by integrated clinical care/research sites (81%), and the remaining (19%) are employed by dedicated sites/site networks. 64% of respondents held a clinical site staff role and 36% held an administrative role. Clinical staff included roles such as PIs or Sub-Is, Clinical Research Coordinator (CRCs), and Clinical Research Nurses (CRNs) whereas administrative staff included site directors, research managers or equivalent roles.

Overall,respondents were experienced with a mean tenure in clinical research of 17 years with a range of less than one year up to 50 years working in the industry.

While the majority (70%) of respondents is employed in North America/Central America, 16% are employed in Europe, 10% in Asia (10%) and 4% in other regions of the world (Table 1). 

In general, the majority of respondents reports that many study start-up, execution and other operating activities have worsened substantially over the last 5 years (Table 2). Some of these challenges are related to the COVID-19 pandemic whereas others are long-standing issues.

Hiring and retaining staff topped the keylist of primary challenges with budgets and contracts as well as study initiative activities a close second and third. Interestingly only 42% of respondents feel that implementing new technology had worsened somewhat or greatly. These findings are in contrast to some other recent site surveys where technology-related factors appear to have a bigger impact on site operations.

No significant differences are observed by site type, although it is noteworthy that only 20% of respondents from dedicated sites/site networks feel that interacting with sponsors had worsened compared to 35% of respondents from sites where research is integrated with clinical care. Administrative staff hae more positive views about sponsor interactions than do clinical staff.This could perhaps be a result of the investments sponsors have made engaging sites through professional research sites through various industry collaborations such as through SCRS, ACRP, TransCelerate, and the type of site staff who participate in these initiatives.

Perceived change in site operating activities during the past five years by site type and role

Unsurprisingly, site staff who feel that things have worsened substantially over the last 5 years have a more pessimistic view of the future outlook of clinical research at their site in the next five years. Conversely, site staff who feel these operating activities have not worsened greatly or somewhat over the last 5 years, have a more optimistic viewpoint.

Site optimism and factors driving the optimism

The results also reveal that nearly 80% of respondents hae a ‘Somewhat’ or ‘Very Optimistic’ view of the future (Table 3).

Optimism* for the future overall and by site type and respondent role

*Question: My outlook for the future of clinical research at my investigative site over the next five years

No significant differences are observed by site type or role although administrative staff reports being very optimistic in somewhat higher proportion compared to the clinical staff. One interesting finding is that site staff with less tenure have a more positive outlook than more experienced staff although the differences are not statistically significant (Figure 1). This may be explained by the fact that less experienced staff have a limited baseline on which to compare trends. Similarly, more seasoned veterans may have become more jaded in their attitudes or are more frustrated with the lack of progress in some areas.

Viewpoints based on tenure of clinical research experience

The most astonishing finding however, was the degree to which sponsor-site relationships affect positive or negative perceptions; specifically the degree to which sponsors seek, value, and apply site feedback is significantly related to site optimism about the future. Higher percentages of respondents who agree that sponsors seek (87%), value (86%), and applied (87%) site feedback are optimistic about the future (Table 4).

Relationship between site optimism and perceived degree that sponsors seek, value and apply site feedback

Those that felt that sponsors they work with value input even if they are unable to apply it are over 4 times more likely to be optimistic about the future. Similarly, those that perceive that the sponsor applies site feedback and those that feel the sponsor seeks out site feedback are over 3 times more likely to be optimistic about the future. Several respondents underscored this point, noting that:

“Soliciting and implementing changes based on the feedback of experienced sites would help sites succeed in implementing study protocols and likely would prevent many failed trials.”

“Sponsors who do not ask for feedback from study coordinators regarding study design and feasibility generally have poorly designed, non-feasible studies. Sponsors should seek consultation from study coordinators, as we are the ‘boots on the ground’ and can tell a sponsor if something does not appear to be doable or will be too much burden on participants and/or study staff.”

—Clinical research coordinator/site director

Even if site feedback cannot be incorporated, respondents value hearing from sponsors that their feedback was considered.

Patient centricity and site centricity are one and the same

Overall sites feelthat more traditional patient-centric services such as pre-paid debit cards and transportation assistance programs have a much greater impact on a volunteer’s willingness to participate in clinical trials than some of the newer approaches (Figure 2). Over half of the respondents feelthese had a greater influence compared to remote visits, home-health services or local pharmacy-based visits.

Solutions that greatly enhance volunteer participation

Nonetheless, respondents feelthat overall, any of the DCT-related solutions aimed at enhancing volunteer participation also enhance site implementation success (Figure 3). These findings suggest that if the site perceives a solution will enable their success and if they are bought-into the solution, then it may translate into enhanced volunteer participation. Or, at a minimum, a perceived positive influence on participation rates.

Perceptions of impact of DCT-related solutions on participation and site implementtion

One industry expert noted that “the most patient-centric thing sponsors can do is to be site-centric.” The results of the Tufts CSDD survey support that observation.

Supporting healthy sponsor-site relationships by tackling age-old issues

Nearly every industry survey on site perceptions reinforces the importance of addressing the enduring challenges of study budgets, CRA quality and study complexity.

 The results of this study were no exception (Figure 4).

Top factors that greatly enhance site sustainability

When asked which factors would greatly enhance the site’s likelihood of remaining in business going forward, the number one factor, reported by two-thirds of the respondents, is ensuring that study budgets reflect the true work efforts of the site. This is likely related to the intensity and complexity of the study as well with nearly half of the respondents suggesting that reduced intensity could facilitate better study implementation and future site viability. As one respondent noted:

“The resources to implement procedure-intensive studies are just so limited it would be great to have sponsors think carefully about what matters in getting the drug approved.”

Similarly, over half of the sites also reinforce the qualifications of the CRAs/monitors as having a great influence on the viability of clinical research operations in the future. This was reinforced by another respondent who commented:

“If we were able to significantly increase the number of competent monitors for clinical trials, I think a large number of the issues that we face would dissipate.”

There were some significate differences noted between clinical and administrative staff for each of these factors with administrative staff ranking all of these factors as having a greater influence than the clinical staff. This speaks to importance of tailoring site interactions to address the needs of the different roles.

While these themes were reinforced through a vast number of qualitative responses, one site director summed it up most succinctly:

It stands to reason that sites who feel respected and heard by sponsors will have a more optimistic view of the future. What may be less intuitive however, is the importance of pairing investments in new technologies with investments in behaviors that foster relationship quality with site staff, particularly more seasoned veterans. Simply focusing on novel approaches to conducting trials or implementing new technologies will only go so far in terms of becoming the sponsor of choice or accelerating clinical trials. Site staff who feel heard and that their experiences are valued, may withstand any number of burdensome procedures, novel technologies and procedure-intensive studies.

The results of this survey suggest that it may be less about the complexity of the study design, implementation model, or use of a given technology and more about the relationship the sponsor has with the site (in general) and specific roles at the site (administrative versus clinical staff).In addition, focusing on persistent challenges related to the adequacy of study budgets and CRA quality may have a greater impact on both the quality of the site relationship, as well as study performance.

In upcoming research, we hope to further explore the extent to which the qualitative aspects of sponsor-site relationships impact site performance and the adoption of new solutions supporting clinical trial execution.

*For this study, Tufts CSDD differentiated two site types: one that integrates research activities withing clinical care settings including not-for-profit clinics, hospitals, health systems, academic and government-affiliated hospitalsystems, and for-profit clinics and hospitals that provide clinical care and also conduct clinical research studies; and the other that conducts clinical trials within a dedicated site(s) / site networks and that derives the majority of their income from industry-funded clinical trial study grants.

We would like to acknowledge the following individuals who provided input into the survey design or shared their insights on the results:

Jessica Fritter; associate faculty, clinical instructor of practice, The Ohio State University College of Nursing, and partner, BJE Consultants

Victoria DiBiaso; global head, patient informed development & health value translation, Sanofi

Joseph Kim; chief strategy officer, ProofPilot

Therica Miller; executive director, enterprise cancer clinical research, Tisch Cancer Institute at Mount Sinai

We would also like to acknowledge the Association of Clinical Research Professionals (ACRP) for distributing the survey to their members.

Scott, J. Sites Name Tech Acceptance as Essential Factor in Selection of Sponsors, Survey Finds. CenterWatch. 2018. Published online: January 16, 2023.

AVOCA, ACRP. Becoming a Sponsor or CRO of Choice – A Site Perspective. White paper published online: September 2018.

SCRS. Site Perspectives on Decentralized Clinical Trials. White paper published online: 2022.

Articles

Tufts CSDD survey finds fostering healthy relationships with site staff is key to a positive outlook.

Assessing Investigative Site Outlook and Operating Experience Post-Pandemic

For three decades, the widely-accepted and frequently-cited statistic that only 5% of eligible patients participate in clinical trials has served as a clear indication that the clinical research enterprise is failing to attract study participants.

It is true that patient recruitment has been a top challenge that has increased in difficulty over the past 30 years. And we can thank the statistic for helping to bring attention to this challenge. But the figure itself is superficial and flawed and reflects poorly on our growing awareness and commitment to patient-engaged clinical research.

Put simply—the 5% figure needs a serious reboot.

It is based on a simple descriptive statistic that fails to take into account the many factors we now know to be associated with a patient’s decision to participate in a clinical trial. The figure is also derived from a very specific patient population participating in a small subset of clinical trial activity and should not be generalized to all patients in all disease conditions.

Moreover, the figure is a static and outdated estimate that fails to recognize the evolving nature of investigational therapies in the drug development pipeline, and the highly customized strategies and executional models now required and implemented to engage study volunteers and their support network.

The statistic was first introduced by National Cancer Institute (NCI) researchers Michael Friedman and Dennis Cain at a 1989 workshop of the American Cancer Society and published the following year in the journal 

 Friedman and Cain looked at NCI-funded cooperative group studies of select oncology diseases actively enrolling study volunteers. They divided the number enrolled by the number of new cancer cases referred to the NCI in the prior year and showed referral capture rates ranging from 0.7% to 3.7%.

The intent of the study was to convey the level of clinical research activity and infrastructure among NCI’s Cooperative Group system. But the referral capture rates attracted attention and stimulated a number of initiatives to improve physician referral into, and raise public and patient education and awareness about, NCI-funded clinical trials.

Soon after the statistic was published, it began resurfacing in the literature and in meetings and conferences, gradually morphing into a figure embraced by professionals across therapeutic areas and involved in not only government-funded but also pharmaceutical industry-funded clinical trials.

The calculation is deeply flawed—in part a function of the time when it was conducted. It does not factor protocol screening and inclusion and exclusion criteria in its assessment of "patient eligibility." It is based on the assumption that if a patient has been diagnosed and referred then surely she or he will participate in the clinical trial. The role that competing clinical trials play in limiting the availability of study participants was also not part of the calculation.

But our interpretation of the 5% figure is perhaps the most notable shortcoming. Historically, the primary explanations of the statistic included the failure to execute an effective recruitment campaign, patient distrust, and low awareness among referring physicians. In the early 2000s, the explanations broadened to include public and patient awareness and literacy, and patient attitudes and perceptions about clinical research.

More recently, the burden of participation, access to clinical trials, and the important role that patients’ unique needs and preferences play have received more recognition as factors that influence self-determination of eligibility. What concerns do patients have about the risks associated with the investigational treatment and study assessments and how do they vary by disease condition and participant subgroups? What role does the informed consent process play in discouraging the decision to participate? How easy is it for patients to find and join clinical trials, how convenient is participation based on the protocol requirements, and how do these experiences and needs vary by participant subgroup? This is but a partial list of areas we are now examining that influence eligibility.

The sophisticated, subjective, and active role that clinical care providers—physicians and nurses—play in determining eligibility and facilitating patient engagement is one area that has been largely absent from interpretation of the statistic. It also represents an untapped opportunity, particularly in light of growing interest in leveraging clinical care infrastructure and data to support clinical research.

Studies in the literature have consistently demonstrated that clinical care providers hold unprecedented influence: they are the most trusted sources for health, medical, and clinical trial information. The vast majority of patients report that they would consider participating in clinical trials if their physician or nurse recommended it. A similarly high percentage of global study volunteers confirms that they spoke with their physician prior to making the decision to participate in a clinical trial. And patients who receive information about clinical trials from their healthcare provider are significantly more likely to participate.

My team at the Tufts Center for the Study of Drug Development (Tufts CSDD) surveyed 755 physicians and 1,255 nurses primarily in North America and in Europe and found high self-reported levels of general awareness about clinical research, familiarity with the clinical research process, and high reported willingness and confidence in referring patients. But the reported rate of referral into industry-funded clinical trials was remarkably low: Physicians reported referring a median of five (5) patients into clinical trials annually, a referral rate that is less than 0.2% of their annual clinical care patient volume. Nurse referral volume was even lower—a median of two (2) patients annually—representing a .04% referral rate.

The reasons for referring were in large part associated with the subjective determination of a patient’s ability to participate and confidence in where and who is conducting the clinical trial. Our study found, for example, that eight out-of-10 physicians (80%) and two-thirds of nurses (68%) are most likely to refer their patients to familiar colleagues and research centers. A high percentage of physicians and nurses indicated feeling that the focus and demands of the clinical trial may not be appropriate for a given patient, or may be too difficult for a given patient to adhere to the protocol requirements. Contrary to commonly-held beliefs, the least mentioned reason—only 9% of physicians and 2% of nurses—was the fear of losing patients to another clinical care provider.

Having established more intimate relationships with their patients, physicians, and nurses determine eligibility based on their own subjective assessment of each patient's individual ability and predisposition to enroll and participate.

Given the high and rising complexity of protocol designs, the growing volume of active clinical trials underway, and the increasingly customized ways that clinical trials are executed, it is extremely difficult—perhaps impossible—to derive an accurate and meaningful statistic that characterizes the proportion of eligible participants overall and by disease condition.

What figures should we use? Those that are disease specific; that recognize the unique experiences of patients living with that disease (e.g., percent enrolled to screened; percent complete to enrolled); and those that are particular to a given protocol (e.g., actual to planned timelines).

It is also extremely difficult to end the use of a flawed statistic once it has gone into circulation.

That said, I’m hopeful that this column may encourage colleagues to scrutinize that statistic and to continuously push our understanding of factors influencing the determination of eligibility to participate in clinical trials.

, MBA, executive director and professor, Tufts University School of Medicine

Friedman M.A.; Cain D.F. National Cancer Institute Sponsored Cooperative Clinical Trials. 

https://acsjournals.onlinelibrary.wiley.com/doi/10.1002/1097-0142(19900515)65:10+%3C2376::AID-CNCR2820651504%3E3.0.CO;2-A

Self-determined eligibility and the influence of clinical care challenge this flawed figure.

Rebooting the Statistic That 5% of Eligible Patients Participate in Clinical Trials 

Decades of aggressive timelines, along with demanding and complex drug development activity has facilitated the use of highly structured, sequential, repetitive, and standardized behaviors and practices meant to drive efficiency and speed. Many practices and procedures are intended to protect the safe and ethical treatment of study volunteers and ensure the integrity of the research. But sponsors, CROs, and investigative sites also point to the use of extensive conventional approaches and standardized operating practices to streamline activities, save time, and plan and predict executional outcomes. Throughout this article, we refer to these collectively as ‘linear practices’ in that they follow a sequential order or progression—determined by previous actions and conditions—that rarely deviates.

Anecdotal reports from industry insiders suggest that linear practices may be having the opposite effect of what is intended, resulting in more delays and inefficiencies. As one example, our research has consistently shown that operational planning practices—such as calculating enrollment rates, study timelines and study cost estimates—have been steadily losing their accuracy and effectiveness. Our studies, and others, have demonstrated that as clinical trials have become more complex over time, the difference between actual and planned timelines has widened and cycle times have gotten longer, with greater observed variation around the mean durations.

 In response, sponsors have implemented a larger number of corrective measures, including more amendments per protocol

At this time, given the unprecedented complexity of clinical trials, many sponsor companies are hoping to leverage practical, evidence-based insights to inform more flexible and agile approaches than historical—and ineffective—linear practices.

In this article and others to follow, Phesi and Tufts CSDD share the results of collaborative research focusing on select linear operating practices that can and should be improved with better planning and real-time data. Here we look at a common linear practice in investigative site management: planning the number of Phase III sites required based on Phase II patient enrollment experience. When a Phase II trial is successfully completed, Phase III trials are planned to confirm and build on the Phase II experience. To achieve the same enrollment rate and meet the study timelines, the number of investigative sites will be proportionally increased according to the enrollment experience in Phase II.

In this analysis, we have gathered a sample of clinical trial pairs: a primary Phase II trial with its companion Phase III pivotal or supportive trial. Trials were paired based on a combination of factors and elements including pivotal trial status, indication, primary endpoints, dates conducted, and other select trial design features. Table 1 below provides the NCT numbers, durations (trial start to primary completion date, in months), and therapeutic areas of the associated pairs of clinical trials. Trial start date and primary completion date were taken from clinicaltrials.gov.

Eleven trial pairings were selected from 9 drugs approved by the FDA between 2018 and 2020. Trials included in our sample had start dates between 2008 and 2016, and primary completion dates between 2010 and 2020. Three trial pairings (27%) were oncologics (anti-neoplastic), 2 trial pairings (18%) were immunologics, and the remaining pairs (55%) were from a variety of CNS, gastrointestinal, and cardiovascular disease conditions.

Gross Site Enrollment Rate (GSER) was selected as the key metric for this analysis. GSER is defined as the number of patients per site per month.

 Typically, the number of sites in the Phase III trial is directly and linearly scaled from the Phase II trial in order to increase the number of participants enrolled. Estimated timelines are based on the assumption that the GSER will be the same in Phase III as it was in Phase II. When this linear practice results in a lower-than-expected GSER, the estimated timelines cannot be met, the clinical trial is delayed and often unplanned and unbudgeted remedial steps are taken.

 Trial Durations, in Months (Trial Start – Primary Completion Date)

Table 2 below provides the number of sites engaged and patient enrollment experience for each clinical trial in our sample. Table 3, located below Table 2, provides the GSER—drawn from the Phesi Trial Accelerator™ platform—for each trial.

The results show that the proportional increase in the number of investigative sites does not yield a proportional increase in patients enrolled; indeed, it often results in a lower enrollment rate and longer enrollment cycle times. In these paired examples, when the Phase III trial is larger in scale as measured by the number of investigative sites and the number of patients recruited, the enrollment rates (GSER) are frequently lower.

Gross Site Enrollment Rates are impacted by many factors. For any given multi-site clinical trial, investigative sites are engaged and activated over a period of time. The more sites engaged and activated within a shorter period of time, the better the utilization of the enrollment capacity for allsites participating.

Phesi has measured the utilization of investigative site enrollment capacity and calculated a Site Effective Index (SEI).

 SEI essentially measures the percentage of sites activated, and when, during the enrollment period. When all sites are activated on Day 1, the SEI is 100%. Hypothetically, were all sites to be activated on the last day of the enrollment period, the SEI would be 0%. Phesi research shows that the industry SEI average is approximately 50%.

Whereas adding investigative sites to any given clinical trial may initially improve enrollment performance, there is a diminishing return on the number of sites engaged due to competition for patients, the declining availability of targeted patients per subsequent site activated, and lower relative site capabilities and effectiveness. Over time, the larger the size of the clinical trial as defined by the number of investigative sites, the lower the proportion of high performing investigative sites is engaged.

The ultimate consequence of linear practices associated with site identification and activation is the delay in achieving target enrollment. Looking at five trials in our sample where there is sufficient information, these delays range from 5 to 21 months.

Simulation to inform Phase III planning would go far in addressing the challenges introduced by linear site management practices. Based on actual data from a cohort of Phase II trials with similar design features and disease conditions, a baseline GSER scenario can be established. Next, various “what if” trial design and execution scenarios can be conducted to accurately simulate the impact of adding or subtracting investigative sites. Figure 1 below shows the application of a simulation exercise. Each sphere represents a clinical trial with the size of each sphere corresponding to each individual trial’s enrollment rate (e.g., the larger the sphere the higher the enrollment rates).

Based on this simulation, the Phase III trial in this model would have benefited from involving more investigative sites resulting in a shorter enrollment duration. The ability to objectively visualize scenarios helps to optimize site management and patient enrollment.

There are a number of other approaches that sponsors and CROs have historically implemented to address the effects of linear site management and patient recruitment practices: Some sponsors and CROs, for example, comprehensively identify and activate top performing investigative sites to mitigate diminishing GSER return. With the average estimated cost to activate and monitor an investigative site at $40,000 - $50,000, the marginal cost of activating a larger number of Phase III sites will be offset by the gains in the speed to achieving target enrollment. At the present time, given the availability and granularity of clinical trial performance data informing site and recruitment management, simulation is likely more cost-effective.

Another approach that select sponsors and CROs are implementing involves more active management and support of investigative sites to ensure higher GSER levels. This support may include providing recruitment assistance, funding and training for dedicated recruitment specialists. The deployment of virtual and remote clinical trial solutions (i.e., DCT)—improving study volunteer access and participation convenience—may also play a role in driving improvement in GSER. At this time, sufficient evidence to evaluate the impact of these approaches is not available.

Linear practices in the identification and activation of investigative sites have resulted in clinical trial delays and it is likely that these practices contribute to clinical team decisions to terminate the development of promising new treatments. Rich operating data and analytics can play a critical role in avoiding linear practices and in delivering better clinical trial performance outcomes.

, executive director and research professor, Tufts CSDD, 

, project manager and data scientist, Tufts CSDD, 

Getz K, Campos R. Trends in clinical trial design complexity. 

Getz K, Stergiopoulos S, Short M et al. The impact of protocol amendments on clinical trial performance and cost. 

Getz K, Smith Z, Jain A, Krauss R. Benchmarking protocol deviations and their variation by major disease categories. 

. 2022; doi.org/10.1007/s43441-022-00401-4.

Li G. Forecast Enrollment Rate in Clinical Trials. 

https://www.pharmexec.com/view/finding-sweet-spot

Spinner J. Sponsors must rethink ‘primitive’ clinical trial approaches: Phesi. 

https://www.outsourcing-pharma.com/Article/2021/05/04/Sponsors-must-rethink-primitive-trial-approach-Phesi#

Phesi and Tufts CSDD share results from research on operating practices that can be improved. 

Leveraging Data Insights to Address the Perils of Linear Practices in Site Identification and Activation

The market for contract clinical service delivery models has continued to evolve and grow in step with a maturing global drug development enterprise.Over the course of more than three decades, reliance on outsourcing has intensified, as sponsor companies seek more capacity and scale, scientific and executional expertise, cost containment and operating efficiency. Indeed, spending by biopharmaceutical companies on contract service delivery models is growing rapidly at 6-8% annually, far faster than the rate of spending by sponsor companies on internal R&D operations and infrastructure.

Underlying landscape trends driving growth in demand include the rising prevalence of small companies with highly limited clinical research capacity and talent; leaner sponsor company operating models; and ongoing biopharmaceutical company desire to continuously assess which competencies offer strategic advantages that should be retained in-house or handled by external collaborators.

Twenty years ago, “full-service outsourcing (FSO)” and “Functional Service Provider (FSP)” were distinct models and biopharmaceutical companies tended to gravitate to one or the other.

Over time however, the distinction between FSO and FSP has become harder to define.Today, sponsor companies use a variety of contract service delivery models simultaneously to support their studies, mixing and matching a wide variety of outsourcing approaches that vary from study to study.

In part a result of the variety and combination of approaches used, at this time the terms FSO and FSP are frequently misunderstood not only between companies but also within companies, across functions and therapeutic areas.As a case in point—here are definitions of FSOs in the literature:

“A company hired by another company or research center to take over certain parts of running a clinical trial. The company may design, manage, and monitor the trial, and analyze the results.“

A CRO that “provides planning and execution of a clinical study, including oversight and accountability for key milestone delivery, from our centralized project management team."

“A person or an organization (commercial, academic, or other) contracted by the sponsor to perform one or more of a sponsor's trial-related duties and functions."

‘Full Service,’ for example, has largely become a catch-all phrase that has limited meaning or relevance. Many sponsors characterize their outsourcing approach as Full-Service when, in fact, their approach falls somewhere between FSO and FSP.

There are a number of reasons why there is so much confusion today. Many sponsor companies are simply too large and too fragmented to consistently adhere to a single outsourcing model.Clinical projects have become so complex that they require support from a larger and more diverse group of providers.

 And many large sponsor companies that have formed outsourcing relationships still have significant in-house development resources creating internal competition with comparable external resources.

Legacy activity underway when new partnerships form also contributes to the problem. Clinical teams typically have established their own relationships with select service providers and resist handing-over existing projects to new collaborators. Companies going through mergers and acquisitions, in particular, are often forced to permit inconsistent practices across organizational functions to ensure that legacy projects are completed as new approaches are introduced.

Another reason is the turnover of key senior staff advocating for, overseeing, and implementing a given sourcing strategy. Outsourcing strategies that have not sufficiently been adopted are prone to fall apart when new executives, who might have different ideas about sourcing models, take over.

As a result, in practice, current outsourcing models being used reflect a wide spectrum of permutations and combinations.

Lack of distinction, poor understanding, and misuse of the terms FSO and FSP ultimately fail to provide sponsor companies with meaningful and actionable insight to inform strategic decision-making and continuous improvement. Although biopharmaceutical companies collect a vast amount of operating data today to inform, predict and ultimately optimize clinical trial performance, without a clear understanding of differentiated outsourcing models used, this data offers insufficient insight overall and by function into the contribution and comparative value of a given model or combination of models.

Moreover, although the original definitions of FSP and FSO outsourcing models have not adapted during the past two decades, sponsor company operating models have continued to evolve.As such, it is essential that we update outsourcing terminology to accurately describe the work actually being performed. This can best be achieved through gathering broad industry input and developing consensus definitions that can be easily applied to outsourcing strategies and decision-making.

We propose that the underlying principle now differentiating the permutations and combinations of FSO and FSP-like services is the employment of the CRO’s infrastructure, processes and potentially the CRO’s standard operating procedures (SOPs) for clinical trial conduct.During the past ten years, sponsor companies attempted to differentiate their outsourcing practices according to the type of contracting services (e.g., unit-based, deliverables, outcomes versus FTE) and/or the level of accountability assigned to the CRO. The reality is that sourcing models are adjusted and adapted across multiple service lines as organizations continue to evolve.

Based on extensive input from ICON’s pharmaceutical company Partners of Choice community and in-depth discussions with individual sponsor companies, the following framework (see Table 1 below) has been developed to characterize the variety of sourcing models used across three primary parameters: (1) outsourcing orientation; (2) infrastructure support; and (3) accountability and contracting structure.

Given their size, configuration, portfolio, and core competencies, many of the mid- and large biopharmaceutical companies leverage “mixed models” and combinations of sourcing models at the clinical trial level.Other sponsor companies, for example, have different sourcing approaches depending on the strategic value of the asset in development (e.g., registration/pivot in one model and late-stage/post-approval in another) and the therapeutic area. Table 2 below presents, for illustrative purposes, an application of our proposed sourcing model framework to better characterize the variation in approaches that sponsor companies are using to leverage contracted clinical services.

We invite comments and input into this proposed new taxonomy and framework.It is our hope that this framework will be discussed, refined, and improved.Versions of this framework will ultimately serve as a more relevant and useful approach to identifying models—and combinations of models—associated with higher levels of performance, efficiency, and quality.

With a lack of differentiation between FSO and FSP approaches today, it is critical for both CROs and sponsor companies to align on definitions of sourcing models in use. Universal definitions will ensure common understanding between partners and across the industry with respect to roles, responsibilities, and accountabilities. Consistency in the descriptions we use to characterize “how we work” is also critical for establishing industry-wide recognition and acceptance of benchmarks that enable more meaningful and reasonable comparisons in the effectiveness of one or a combination of sourcing model(s) over others across the life of a development project.All of which will ultimately lead to higher quality work and improved outcomes for patients.

ICON is currently engaging with the Tufts Center for the Study of Drug Development (Tufts CSDD)—with input and in collaboration with ICON’s biopharmaceutical company Partners of Choice (PoC)—to conduct a primary research study comparing clinical trial performance outcomes through the implementation of more meaningfully defined and relevant sourcing model strategies.

, Executive Director and Professor, Tufts Center for the Study of Drug Development, Tufts University School of Medicine

, SVP, Head of Clinical Development Operations, Bayer

, VP, Head of GCTO Regions and Head Quarters Functions at Merck & Co

Kaitin K. Outsourcing outpaces internal spending but remains tactical and reactive

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Getz K, Smith Z, Kravet M.Protocol design and performance benchmarks by phase and by oncology and rare disease subgroups.

Differentiating FSO and FSP approaches can better align definitions of models for CROs and sponsors. 

Redefining CRO Sourcing Model Terminology to Optimize Outsourcing Strategies

The drug development enterprise is undergoing a steady transformation catalyzed by three broad movements. Unfolding simultaneously, converging, each movement is interacting with and facilitating the others to ultimately realize a future where therapeutic innovation and healthcare delivery are highly customized to serve the needs of the individual patient.

Each broad movement holds great promise not only for patients living with and managing their disease, but also for driving improvements in drug development science and execution. The first—precision or personalized medicine—is based on deep understanding of the molecular basis (e.g., the genetic and biologic underpinnings) and the social determinants of disease. With support from large databases, informatics, technologies, and advanced analytics we’re able to target highly stratified patient subpopulations—eventually at the individual patient level—resulting in smaller and faster clinical trials, fewer adverse outcomes, greater likelihood of a treatment response, higher probability of approval, and lower overall development costs.

The personalized medicine movement in drug development has already delivered remarkable treatments to patients, most notably in oncology and immunology. And our pipelines have many more to come: Nearly all drugs in R&D for cancer-related diseases, and 60% in R&D for other diseases, now rely on genetic information and biomarker data. And almost 40% of all FDA approvals are now personalized medicines.

The patient engagement movement has instilled enterprise-wide passion and commitment and has produced a variety of approaches to amplify patient voices and inform our understanding of the unique needs that communities and individuals face when living with the burden of disease and when accommodating clinical research in their health journey.

Patient and community engagement informs and enriches clinical trial planning and the clinical trial participation experience; motivates our efforts to bring clinical trials to wherever and whenever patients can most easily and conveniently participate; and improves communication effectiveness, transparency and disclosure.

Ultimately this second movement ensures that we are measuring outcomes that are the most clinically meaningful and relevant as defined by the community living with a given disease; and promises to deliver more feasible, faster clinical trials with better enrollment and retention rates, proportional representation according to disease prevalence by race and ethnicity and other individual characteristics, fewer unplanned and un-budgeted protocol amendments, higher approval rates, and accelerated commercialization.

An assessment of articles referenced in PubMed shows a five-fold increase in the number of clinical trials supported by patient-centric practices between 2015 and 2020. A growing number of clinical trials, for example, are soliciting patient input (e.g., patient advisory boards and input panels) into protocol design and returning plain language clinical trial results summaries. In a small number of cases, they are returning unblinded personalized trial results.

Many clinical trials offer a wide variety of convenience-enhancing options, including transportation assistance; easier reimbursement and compensation; and virtual and remote (“decentralized”) support for clinical trial participants.

The third movement—the advent and adoption of digital technologies and data analytics—is enabling the collection of, and faster access to, much higher volumes of scientific and operating data that can be interrogated and shared, from which rich insights can be drawn and applied. This movement promises efficiency through automation and connectivity, improvements in data quality and research integrity, time savings and acceleration through faster data analysis, faster and more accurate recognition and response to data patterns, and the ability to anticipate risk and predict outcomes.

Growth in the volume and diversity of data that we are already collecting, curating, and managing is astounding. Each pivotal trial today collects an estimated 3.5 million datapoints, a four-fold increase in the average number of datapoints collected 10 years ago.

The many solutions and capabilities available ultimately give each individual patient options to participate in essentially custom-built clinical trial experiences.

Although these movements have been unfolding for at least 10 years, they have yet to achieve their compelling and anticipated benefits to drug development operations and execution. Research conducted by the Tufts Center for the Study of Drug Development and by others indicates that drug development failure rates are the highest they have ever been; the average overall duration of a development program and of a clinical trial by phase is not getting faster—they are taking longer and the durations are less predictable; recruitment and retention rates are at their lowest in history—particularly in oncology and rare diseases (two dominant segments of the R&D pipeline); the perennial 4% to 6% increase in overall R&D spending shows no sign of slowing; and we are observing higher levels of inefficiency and performance volatility.

A partial explanation for these outcomes lies in the paradoxical nature of our collective efforts to realize a future where therapeutic innovation and healthcare delivery are highly customized. Specifically, the closer we get to serving each individual patient, the more R&D personnel, infrastructure, collaborators, vendors, oversight, technology platforms, and solutions that must be engaged, deployed, coordinated, and integrated. Customization is offering seemingly exponential growth in the number of options to meet individual patient preferences and needs, but customization is also dramatically increasing scientific and operating complexity, fragmentation and scope – the very factors associated with and predictive of poor and inconsistent performance, inefficiency, rising cost and risk.

How can we address this paradox? How can these movements realize their full potential and promise?

It has been suggested that we cannot continue to use 20th century drug development operating models, cultures, and mindsets to optimize the value of 21st century solutions. Tufts CSDD research supports this notion. For decades, we have observed outliers and case examples that have delivered distinct performance, efficiency, and cost improvements. To name but a few: patient and community-engaged development planning; adaptive, master and novel protocol designs; highly targeted resource allocation and vendor selection; frequent communication; broad and open collaboration; and data sharing. But these examples have remained isolated instances.

Our remarkable success during the COVID-19 pandemic is widely acknowledged. Together, we have dramatically compressed drug development timelines and shortened between-phase transitions to deliver safe and effective vaccines and a burgeoning pipeline in unprecedented speed and efficiency.

We have long recognized the individual factors that ultimately contributed to our successful response:

Rapid deployment of virtual and
remote technology

During the pandemic, the key was not the individual contributing factors, it was the simultaneous, cohesive, synchronous, and holistic way that these factors were managed, coordinated, and implemented by all stakeholders and partners across the global clinical research and clinical care enterprises. True, our experience during the pandemic exposed gaps and opportunities for improvement in infrastructure, including public messaging, community engagement, and last mile drug supply and delivery.

Our collective, collaborative response to the pandemic represents the “Mother” of all proof-of-concept tests and its legacy will be the way it unlocked a new drug development operating paradigm moving forward.

Can the singular focus of a global health crises be replicated in other areas of unmet medical need? We are already seeing development teams pushing the envelope and applying insights from the pandemic: they are forming larger, more open and agile collaborative teams; conducting more pragmatic and collaborative studies; leveraging more real-world evidence; and coordinating and integrating decentralized and clinical care infrastructure.

Lessons from our pandemic experience affirm much of what we have long known and so much more. They magnify and demonstrate a path forward toward realizing a future where each study volunteer participates in the right trial and where each individual patient receives the right treatment, in the right dose, at the right time. 

The struggle to translate promising movements in R&D.

Contemplating a Pressing Drug Development Paradox 

Providing the public with clear, understandable descriptions and results of clinical trials is imperative to fostering public trust in clinical trials and approved vaccines and treatments. The unprecedented accessibility and spread of misinformation about investigational and approved medical therapies in recent years has increased the need for centralized, accessible, and accurate information on clinical trials and their results. Patients and the public need access to reliable clinical trial results to make educated and informed decisions on treatment, in coordination with their physicians, who also need usable data to inform clinical care. This has recently become even more urgent, given the highly publicized controversies over vaccines for COVID-19, despite data suggesting that they are safe and effective. 

Prior to the last decade, registrations of clinical trials, as well as their results, were generally contained only in highly technical documents, such as clinical study reports and peer-reviewed medical journals. These technical results assume an in-depth knowledge of clinical trial terminology, medical jargon, and scientific concepts. Even expert analysis of such documents can be time consuming. Meanwhile, health literacy rates among the public are quite low. A survey of eight European countries found that half of adults had deficiencies in health literacy skills.

 This number is even lower in the US, where only 12% of adults were found to be proficient in health literacy.

 Thus, for accurate, unbiased, and reliable clinical trial results to be accessible, they must be written in plain language.

In 2004, the International Committee of Medical Journal Editors (ICMJE) addressed these concerns with a trial registration policy for all publications in ICMJE journals,

 where associated clinical trials would be required to be posted in a public registry, such as ClinicalTrials.gov. In 2007, the US Food and Drug Administration (FDA) Amendments Act followed up by mandating that trial sponsors register the results of clinical trials on ClinicalTrials.gov, for all late-stage efficacy studies and approved products.

 The Plain Writing Act in 2010 requires the production of plain language communications and informed consent documents for US federal agencies.

 In 2016, the Final Rule update clarified FDA requirements and expanded the scope of clinical trials that are required to be listed on Clinical Trials.gov.

 The European commission also issued regulation (Annex V of the 2014 European Union Clinical Trial Regulation (No. 536/2014; Article 37), that mandated the creation and dissemination of plain language summaries for clinical trials completed in EU member states.

 These summaries state, in an unbiased, non-promotional, and patient-centric way, what happened in a clinical trial. This mandate went into full effect on January 31, 2022. Taken together, these developments have led to an increase in focus on communicating scientific and medical findings in publicly accessible and plain language. 

Compliance with plain language and clinical trial transparency regulations, however, has not been high. As of 2019, less than 2% of recent clinical trials had disseminated plain language summaries to trial participants.

 Sponsor compliance with clinical trial registration has also been low. While there has been recent improvement in the numbers of trials registered,

 the content quality of registrations is quite variable. Trials are often registered late, with incomplete information,

Inconsistencies between trial listings and other sources, such as publications, have occasionally been found.

 Additionally, endpoint criteria may not always be present, or be unspecific, making consistency difficult to assess.

 And, despite clinical trial registration being required by US federal law since 2007, enforcement and acknowledgment of noncompliance in clinical trial registration by the FDA and NIH is rare. To our knowledge, enforcement (e.g., financial penalty) was issued for the first time in April 2021 as part of a Notice of Noncompliance from the FDA. Up until that point, only 58 Preliminary Notices of Noncompliance had been issued, despite a far larger number of trials being flagged as noncompliant,

 without any associated penalty. This poses a problem for scientists attempting to analyze reproducibility and consistency of clinical trial information. It creates an even larger challenge for the public and patient communities who depend on reliable and informative results in a language and format that is widely accessible.

While many studies have assessed compliance with trial registry listings and plain language summary dissemination, there has been far less of a focus on evaluating the substance of the listings themselves: specifically, the usability, plain language readability, and overall quality of the information that is posted on ClinicalTrials.gov. Compliance with listing a trial on a registry is a hollow achievement without meeting the primary intent of the regulatory requirement or ethical obligation. Usability and readability of the listings are as important as compliance if the public is to receive value from such disclosures.

To address this gap in understanding of ClinicalTrials.gov listings, the Center for Information and study on Clinical Research Participation (CISCRP)—a non-profit public and patient education and advocacy organization—undertook a qualitative study of case examples to assess the usability, plain language readability, and consistency of listings on ClinicalTrials.gov. We used a framework-based approach to understand whether key, high-level information was present in a patient-centric format in individual listings. Our primary aim was to begin to answer the following question: Overall, do the existing listings, even when aligned with regulatory requirements, add value for the typical patient and the general public?