The life sciences industry has been talking about improving diversity in clinical trials for years. Historically, this talk has amounted to a lot of hot air. But the situation is urgent: while racial and ethnic minorities comprise nearly 40% of the US population, they only account for 2% to 16% of clinical trial participation. Within oncology, Black Americans constitute around 22% of cancer cases, but less than 5% of trial enrollees. This isn’t just a US issue; globally, 76% of patients enrolled in oncology trials are white. And this challenge exists within a research landscape where nearly 80% of trials fall short of enrollment targets to begin with.

In the aftermath of COVID-19, real change in our industry feels more possible than ever. But diversity is a multifaceted problem—one that we cannot hope to solve with a single solution, action, or actor. The responsibility of increasing diversity falls on every stakeholder in the clinical trial value chain—from technology providers to trial sponsors and CROs; from investigator sites and their staffs to global regulatory bodies—and, maybe most importantly, patients.

 must commit to developing solutions that are designed for patients’ real-life experiences. To get there, they can’t develop apps in a vacuum; tech companies will have to collaborate frequently with patients and continuously refine their products.

—both in the US and abroad—must align on guidance that enforces regulations. The EU’s European Medicines Agency (EMA) recently announced new recommendations for implementing decentralized clinical trials (DCTs) with the aim to give a greater voice to patients, and that’s a start. But FDA has issued recommendations around diversity for years yet has rarely taken a hard-nosed approach to enforcement or incentivizing diversity. Globally, there must be harmonization.

, such as the Decentralized Trials & Research Alliance, Society for Clinical Research Sites, and the Association of Clinical Research Professionals, also need to come together to collaborate on alignment of recommendations and standards. At Medable, we are actively working with these groups to find common ground on best practices to recommend.

 must embrace new approaches and technologies and prioritize investment in them. New DCT models can improve not only diversity but also patient access and trust—improving trials’ overall outcomes. New DCT models must be adopted that may include a hybrid of ways that more patients from more places can access the trial, as well as tools to ease understanding and communications around enrollment, consent, and treatment. In addition to virtual tools, sponsors might also enlist trusted, community-based pharmacies which have partnered with key technology providers to enable community-based trials. These pharmacies also possess data that can help sponsors create appropriate diversity plans for various therapeutic indications.

 must consider how their patient interactions and representation of local demographics impact patient trust, recruitment, enrollment, and retention. These front-line clinicians are the boots on the ground and must treat this responsibility in kind, even if it requires additional technology training and more patient hand-holding as the industry evolves and learns.

 aren’t off the hook, either. Distrust of clinical research has its roots dating back at least 50 years, to the horribly botched Tuskegee Study of Untreated Syphilis in Black men by the CDC. That history is real and painful—but we can’t let it hold us back in our fight against the world’s most serious diseases. The COVID vaccine trials, while imperfect, made great strides in improving trust among the Black community. We must keep that momentum going. All of us can help through education, evangelization, and active participation in clinical research.

The time is now. The technology is here. The stakeholders are motivated. And there is government backing. In December, Congress approved an omnibus spending package for the 2023 fiscal year that includes riders to improve diversity in clinical trials, as well as increased funding for research and regulatory agencies, including $3.5 billion in appropriations for FDA and $47.5 billion for NIH.

We have all the tools we need to do this right—we just need to work together.

Articles

A Closing Thought

The responsibility of increasing diversity falls on every stakeholder in the clinical trial value chain.

Population Representation: Everyone Must Play a Part 

The life sciences industry is collectively recommitted to better addressing challenges to diversity and inclusion in clinical trials, especially among traditionally underserved populations. What we know is that there is no singular formula to effectively resolve this longstanding issue. As such, clinical trial sponsors, their contract research organization (CRO) partners, and other industry stakeholders are recognizing the importance of approaching each trial program with a deliberate focus on diversity goals during trial design and planning activities.

In that same thought, it is important to ask, “Does motivation to enroll in a study vary by race and ethnicity?” And if yes, how can sponsors plan for those differences to ensure heightened enrollment but also an enhanced patient experience, which is at the heart of it all?

To take a deeper look into trial enrollment and individual reasons for participation, IQVIA conducted a survey among 1,693 US adult participants of all ages and medical statuses who self-identified their race and ethnicity. Roughly equivalent to the US population, the race identified by respondents were white (71%), Black/African American (16%), Asian (6%), American Indian/Alaskan Native (3%), Hawaiian/Pacific Islander (0.5%), or “other race” (4%). In addition, 11% of respondents self-identified their ethnicity as Hispanic. Respondents were questioned about trial design elements that may affect their willingness to enroll. It is through qualitative data such as these that sponsors and study teams can better ensure future trial protocols consider the potential impact on participants early in the process.

Overall, the survey was designed to measure:

The overall willingness to participate in a study.

Which protocol design elements affect interest in participation and by how much.

If responses differ by race and ethnicity.

We first screened out respondents who answered they were “not at all willing” to ever consider participating in a clinical trial. From there, remaining participants were asked whether they would be “extremely willing,” “very willing,” “moderately willing,” “slightly willing,” or “not at all willing” to participate in a clinical trial when presented with 42 questions to assess the impact of various trial protocol design elements.

From a topline view of feedback, the survey indicated that 73% of all respondents were extremely or very willing to participate in a trial (see Figure 1 below). Hispanic respondents were most willing, with 85% extremely or very willing, and 60% of those extremely willing. However, trial design had a greater influence on their level of interest than other groups. Alternatively, while Black/African American respondents were more willing than other races to participate, with 46% noting they were extremely willing, their interest was less affected by trial design.

Confirming this interest is an important first step in actively listening to patients and understanding how to heighten their experience. However, there were notable nuances for sponsors to consider regarding how trial design affects this initial willingness to participate.

To understand how to approach various community groups regarding trial participation, it was vital to also examine what, if any, trial design elements influenced willingness to participate for each race and ethnicity.

Overall, for all 1,693 respondents and white and Black/African-American groups specifically, responses to 26 of the 42 survey questions indicated a design element that would negatively impact willingness to participate to some extent. However, for Hispanic respondents, responses to 39 of 42 questions indicated this negative impact, and for Asian respondents, it was responses to 31 of 42 questions, highlighting that some design elements were only of concern to these two groups.

The degree to which willingness was affected also differed by race and ethnicity:

White and Asian respondents had the fewest statistically significant differences from the overall total respondents, with seven and nine elements, respectively, having more impact on their willingness.

While Black/African-American respondents had only a few more statistically significant differences from the overall group, the difference in 13 elements had less impact on their willingness (i.e., these design elements still negatively affected their willingness but the amount by which they affected willingness was not as large).

Hispanic respondents had two-to-three times as many elements with statistically significant differences, and 19 elements had more impact on their willingness.

Survey insights helped determine what specific trial components were potential hindrances to participation per race and ethnicity (see sidebar).

For example, as the industry has taken a closer look at the patient experience, it is evident that logistical burdens can make patient recruitment difficult. The survey found that the length of time required at clinic visits was one of the top influencers across all respondent groups. However, for Black/African American respondents, visit schedule elements such as visit lengths and even required overnight stays, did not influence their willingness as much compared to other groups.

Procedures a participant must undergo during the trial are another area that may affect patient recruitment. The survey findings indicated that seven noninvasive imaging procedures, such as computed tomography (CT scans) and positron emission tomography (PET scans), did not affect willingness for most respondents, but Asian and Hispanic respondents were more likely to indicate some of these procedures would influence their willingness.

Frequency of blood draws for most survey respondents was correlated with their willingness to participate. As the percentage of trial visits that contain blood draws increased, the negative effect on willingness to participate also increased. However, for Hispanic respondents, not only was there a higher difference from the overall respondents in willingness when blood draws were at 100% of trial visits, but the negative effect on willingness also remained at the same high level even as frequency of visits requiring blood draws decreased.

To make tangible progress in improving sufficient representation in clinical trials, it is critical to remove assumptions regarding lack of willingness and the notion that focusing on diversity adds time and cost to drug development. As these groups are interested in participating in clinical trials, both for themselves and to represent “patients like me,” how can we make trial designs more attractive to them? Fine-tuning trial design and operations based on patient feedback can be one critical piece of the puzzle toward measurable progress in diversity and inclusion enrollment success.

Actively listening to patients via survey feedback and analysis, social listening, patient focus groups, desktop research, and more can provide a solid understanding of patient burden as well as insights into how patients feel about their disease and related treatment options.

Translating qualitative differences in perception of patient burden by race and ethnicity into a quantitative algorithm using data-driven methodologies allows sponsors to calculate and compare the burden of their protocol. Integrating the perspectives of diverse populations during the trial planning stages can help validate the study design plan and make adjustments earlier to help increase enrollment potential and reduce potential protocol amendments.

, director, design analytics, Applied Data Science Center, IQVIA

Feature Articles

Survey examines trial enrollment motivation among diverse populations.

The Impact of Trial Design on Interest by Race and Ethnicity

A lack of diversity in clinical trials can have serious implications for underrepresented populations as everyone reacts differently to medication. However, improving diversity isn’t going to happen on its own. It must be intentionally and proactively addressed in all realms of research, including the technology that patients use during the trial. As artificial intelligence (AI)-powered technologies become increasingly entrenched in drug research and are put in the hands of more patients, they become a critical avenue to making clinical research more inclusive.

Ensuring that algorithms are built with a diverse data foundation and holding the AI industry accountable to rectify biases will help encourage participation from diverse patients and foster trust in clinical research.

What is the danger of biased technology in clinical research?

The capabilities of AI that make it so promising to improve clinical research, such as automated data mining, predictive analytics, and augmented decision-support, are the same that can cause it to do more harm than good if not built on quality, diverse data. But not all AI in clinical trials carry the same weight. For example, an improperly trained algorithm used to help gauge patient engagement could misreport data around dropout rates, which can lead to frustrating operational inaccuracy that could be fixed and doesn’t impact patient safety.

On the other hand, the stakes can be much higher. If a clinical trial deploys a facial recognition algorithm to confirm depression patients’ adherence to their medication that promises to work with all patients, but in practice, it doesn’t work on those with darker skin, it could lead sponsors to act on misleading efficacy data. This ultimately can cause serious, long-term consequences when that depression drug is deployed in real-world scenarios—those with darker skin would be taking a medication that wasn’t vetted in the same way it was for white patients, potentially putting them at serious health risk.

For the sake of ethics and sound drug development, we need to establish processes that safeguard the quality of our datasets and confirm data is representative of the intended population.

Building diversity into AI with intention

Just like the patient population enrolled in a trial needs to be representative of those who will actually take the medication, the datasets to build AI tools need to be representative of those who will use them. Admittedly, building a truly diverse data foundation to one’s algorithm is a big undertaking. It takes trial and error, concentrated effort, and oftentimes creative approaches that go far beyond available open-source datasets.

For example, facial recognition algorithms need to consider multiple dimensions of diversity, and be able to recognize different skin tones, wrinkles, freckles, people wearing hats or sunglasses—the list goes on. This means an algorithm needs to be exposed to, interrogated, and constantly evaluated under a variety of scenarios before its ready for deployment in a clinical trial.

Doing so also helps expose which scenarios the algorithm does not work in and to use with caution, just like understanding the temperature range of a thermometer and only trusting its accuracy within that range.

In building our own datasets, we knew from the beginning that unbiased data capture was the only way to deliver true value to patients and the drug research process. When we first built our algorithm for detecting patients when taking medications, we quickly learned that the readily available, widely adopted open-source datasets we used were largely built using fair-skinned people. In turn, our algorithm wasn’t working properly with darker skinned patients. We then built our own diverse dataset by recruiting diverse volunteers from sources such as Craigslist to contribute videos to train the AI to distinguish a patient no matter their appearance or environment. Rather than recruiting a specific number of volunteers, we aim for a minimum threshold to train the algorithm until it does not show significant improvement.

It is an ongoing effort to continuously improve algorithm accuracy to achieve the most effective output. This process is a testament to the creative strategies necessary to build a quality, equitable product, and a warning to AI developers who should be careful of assuming that off-the-shelf open-source software fits every population and disease state.

Eliminating biases in AI-powered tools requires elevated governance and standardization for all algorithms to reassure users that they are safe and accurate. As an industry, we could afford to be more skeptical of AI’s conclusions and encourage transparency around the development of these tools. Pulling the curtain back on the historically mysterious nature of AI means developers must prioritize traceability of an algorithm’s development so they can readily illustrate its workflow and how one component feeds into the next.

There may be a day where developers will need to deliver an audit report that traces an algorithm’s inner workings and the quality of its data to act as a seal of approval for its broader use. This report developed by the technology company itself could include insight into how data was collected; how an algorithm was trained and tested and what datasets were chosen for each; what hypothesis the developers set out to solve; what generated a specific output; and more. Because biases can often be caught during the preemptive planning stages, accountability during the development phase is critical.

The work doesn’t stop once the AI is out in the world—there’s an ongoing responsibility and investment needed to monitor and trace its performance in the real world and refine it accordingly. If an algorithm isn’t working as planned, companies should be incentivized to refine it accordingly and incorporate more diverse patients into their testing, rather than introducing patients to inadequate and potentially harmful algorithms.

There is a natural incentive for a company to deliver on the promise of ethical technology, which rewards an organization with a better product, more engaged users, and a better user experience with market success.

Establishing a system of checks-and-balances protocol for AI in which we can detect inaccuracies in real-world scenarios will only help advance best practices and lead to stronger algorithms with fairer outcomes.

Raising the bar for equitable AI-powered tools

In order to make clinical research more equitable, we need to resolve disparities inherent to many of the tools put in front of patients. However, there’s reason to be hopeful. Just the mere fact that we are starting to have more honest conversations about implicit biases in AI is a step in the right direction.

With transparency comes trust, and by advancing responsible AI innovation, all patients can truly be represented in clinical trials to help fuel the development of therapies that can improve outcomes for a broader, more diverse audience. Together, we can hold each other to a higher standard so we can do right by our patients and realize the true promise of these technologies.

Advancing responsible AI innovation to help resolve patient disparities.

Tackling Trial Diversity Through Higher Innovation Standards

Diversity, equity, and inclusion (DE&I) in clinical trials is an increasing point of focus for regulatory bodies and the industry at large. In late December 2022, the US Congress enacted a law that requires diversity action plans for clinical trials that FDA reviews to determine market approval. The agency’s draft guidance for sponsors on preparing Race and Ethnicity Diversity Plans has been available since early 2022. What does this mean for rare disease clinical research, where challenges are multifold compared to clinical research in common conditions?

The importance of DE&I is arguably even greater within rare disease, as clinical trials have immense prominence in the patient journey compared to common conditions where multiple treatment options often exist. With only 5% of the 7,000-plus known rare diseases having an approved treatment, clinical trials are often the only option for patients for a potentially meaningful treatment, but diverse communities are still underrepresented in rare disease clinical trials.

The goal in rare disease clinical research is to include as many people with the particular disease from diverse backgrounds as possible in order to understand and treat it—this is what will drive increased understanding and progress. While some gene variants are known to be more prevalent within certain communities, ultimately any genetic variant can occur in any individual regardless of ethnicity or sex. Not all individuals affected by a rare disease are the same, and not all these conditions have a genetic origin.

Barriers to clinical research that affect diverse populations with rare diseases arise from multiple layers of complications with accessing healthcare and support within health systems, rare disease advocacy, conservative regulatory perspectives, and traditional clinical research models.

We recently discussed this topic with key players in the industry that are dedicated to broadening access to clinical research for underserved populations affected by rare diseases. During our conversation with representatives from the Sumaira Foundation, BridgeBio, Duke University’s ALS Clinic, and RARE-X, we explored ways for drug development stakeholders to employ strategies to improve DE&I in rare disease clinical trials.

This article outlines some of the systemic factors that produce barriers at different stages of the rare disease drug development process and delve into some of the strategies we identified through our discussion with industry leaders that have been working to improve diversity in these trials. The goal of these conversations is to identify and share opportunities where clinical research stakeholders may be able to drive progress forward.

Systemic barriers to achieving representative rare disease clinical trials

Typically, factors that form barriers to achieving representative rare disease clinical trials impact drug development on four levels: strategic (disease and clinical strategy setting); protocol (development of a clinical trial protocol); site (traditional locus of clinical trial research); and patient (potential trial participant). See Figure 1 below.

The impacts of these factors are interactive and cumulative as the drug development process unfolds. For example, at the disease strategy level, limitations in data availability and the pharmaceutical industry’s competitive incentive framework create foundational barriers to achieving representative trial populations.

At a protocol level, a lack of bespoke outcome measures for rare diseases and the conservative practices of regulators creates barriers to more inclusive rare disease trials. For example, homogenous patient populations and restrictive inclusion and exclusion criteria compensate for the use of outcome measures designed for common conditions that aren’t sensitive enough to track changes in rare disease.

The limited pool of expertise and sites with research experience in a specific rare disease creates geographic and socioeconomic barriers to trial participation in studies targeting these diseases. In addition, we find that the investment of time and resources required discourages sponsors from utilizing research-naïve sites that may be closer to potential participants’ residences.

At a patient level, lack of support or outreach between patient advocacy organizations (PAOs), sponsors, and diverse patient communities present barriers to equitable access. Lack of education or awareness about rare diseases and the drug development process, which often comes from PAOs, further impacts underserved individuals in rare disease.

Strategies for sponsors, sites, and PAOs to affect change

Systematic changes can evolve from the groundswell of support for increased DE&I in rare disease clinical trials. Stakeholders of the drug development process, including sponsors, sites, and PAOs, can affect change in a variety of ways across the four levels of the development process. We will explore some of the potential strategies for each stakeholder group ahead.

As the largest stakeholders in the clinical research and development process, sponsors have a variety of opportunities for additional DE&I strategies available to them, from protocol and trial design to site selection and cross-industry collaboration.

As early as possible, sponsors should assess feasibility in terms of cost, timeline, potential, and advantages and explore more inclusive design strategies. For example, selecting novel outcome measures to allow for more heterogenous patient groups, non-placebo controls to improve patient interest and engagement and protocol designs (including potential decentralized trial elements) that limit in-person visits to reduce patient burden.

Other opportunities to reduce patient burden could include implementing concierge services or financial or travel support for patients. To identify potential strategies or barriers, we suggest obtaining input on study designs from as many diverse patient communities within a rare disease as possible. ICON’s free “Rapid Burden Assessment Survey Template” is a resource that sponsors can leverage to ask patients and caregivers for relevant study
design input.

To affect more systemic change, sponsors can deliberately cultivate openness and innovation within the industry. Decentralized clinical trials or novel outcome measures could significantly impact DE&I efforts by removing barriers for diverse patient participation, but they are not often approved by regulators in rare disease trials. Sponsors can partner with relevant PAOs to engage with regulators about the increased trial population diversity that these methods could afford compared to traditional designs.

More immediately, sponsors can foster openness toward new investigators and sites that are nearer to patient populations to mitigate geographic and socioeconomic barriers to rare disease trial participation.

Typically, sponsors prefer not to engage study-naïve sites, but the increased feasibility of participation may offset the additional time and capacity required to support them—potentially saving timeline delays, protocol amendments, and delays/failure to recruit and reaching the market faster.

In direct contrast to the competition within the industry, we see opportunities where sponsors can leverage collaboration by proactively forming or joining collaborative initiatives that support diversity and inclusion. Regeneron has an initiative to improve diversity in genomics databases, which is an important problem to solve as 80% of rare diseases have a genetic cause, yet 80% of genomic databases represent genomes of white European ancestry.

Sites can improve DE&I by removing barriers to access and reducing patient burden. Sites can identify potential barriers and either address them independently or collaborate with sponsors who can potentially support sites with additional participant materials in target languages and various trainings.

Sites can also harness the voices of diverse communities by empowering potential participants to communicate the obstacles they may experience and advocate for the support they would need to participate in a clinical trial. One way we have facilitated this for potential participants is through the

What to ask when you’re interested in a clinical trial: A guide for rare disease patients and families” that we developed with our Rare Disease Advisory Council. They can also share through the Study Participant Feedback Questionnaire™ (SPFQ) and custom surveys supplied by the site.

The advent of the internet and modern communication technologies enabled disparate patient communities to connect and form PAOs, driving basic research and providing researchers and industry with opportunities for discovery. Rare disease PAOs play a vital role in providing practical and emotional support and education to their members. PAOs are also the proper channel through which pharma companies engage patient communities. Without a representative population in PAOs, there is a lack of diversity in the patient voice that helps to shape disease clinical strategy.

PAOs are often underfunded and under-resourced, making it difficult to enact comprehensive DE&I efforts despite their willingness. However, there are resources available. RARE-X has developed a DE&I metrics toolkit specifically for rare disease PAOs. While the toolkit implementation requires capacity and expertise, organizations may be able to work with other stakeholders, appeal to sponsors for support, or combine resources to implement it.

Understanding the drug development and clinical research process may be fundamental to overcoming distrust among diverse patient communities. Often, rare disease PAOs are patients’ and caregivers’ only source of education about the drug development and clinical research processes. For those patient communities that PAOs don’t engage, these resources may be completely out of reach.

PAOs can collaborate with academia to look to existing patient education programs, such as the ALS Clinical Research Learning Institute, the Parkinson’s Foundation’s Parkinson’s Learning, or the Bladder Cancer Advocacy Network’s Patient Empowerment through Engagement Research (PEER) training program. Approaching the founders of these programs to inquire about sharing their methodology and documentation to be adapted for a rare disease setting may save time and budget while forwarding educational goals underpinning DE&I efforts.

Rare disease PAOs can also strive for large-scale systemic change by joining forces with health disparities advocates in lobbying policymakers for policies that incentivize drug development stakeholders to implement DE&I strategies. A recent example of success in this strategy is the Accelerating Access to Critical Therapies for ALS Act (ACT for ALS),
authorized on Dec. 23, 2021.

The rarity of individual rare diseases introduces many unique challenges and complexities in clinical research, including barriers to achieving representative trial populations and equitable access to trials. Underrepresentation of diverse populations in rare disease clinical trials is the result of systemic barriers that impede inclusion across race, ethnicity, socioeconomic resources, geography, gender, sexual orientation, age, and disability. Progress toward representative trial populations is moving slowly, even though study participation is often the only means of accessing a potentially meaningful treatment for rare disease patients.

Focused effort and dedicated action across all levels of the development ecosystem are essential to creating an equitable clinical research environment and driving progress across the industry. We have outlined several strategies that stakeholders across the drug development process could implement to improve DE&I in rare disease clinical trials and have highlighted some of the resources available to assist in these efforts.

, director, patient advocacy strategy, ICON

Outlining new approaches and resources to help ignite progress in this area.

Strategies for Building Greater DE&I in Rare Disease Trials

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

Clinical Trial Insights

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 

On May 26, 2021, the EU began fully implementing the Medical Device Regulation (EU) 2017/745 (MDR).

 This regulation was developed to ensure medical devices are marketed consistently across the EU member states, ensure minimum quality and safety standards for patients and users, and ensure that data generated from such devices in clinical investigations are reliable and robust.

Members of Critical Path Institute’s Electronic Clinical Outcome Assessment (eCOA) Consortium have been required by their clients to go through the process of obtaining CE (“Conformité Européene”) certification for eCOA applications when they do not in fact fall in scope of MDR. By requiring this certification, which would result in the CE mark being applied to the app, clients are equating the function of the eCOA application with being a medical device, or more specifically Software as a Medical Device (SaMD). If the regulation is examined in depth, it becomes evident that while there may be boundary cases where this may indeed apply to an eCOA app, it is clearly exceptional rather than typical.

To date, there is no known published article or publicly accessible paper that examines the regulations in the context of eCOA systems that would support an informed discussion between sponsors and eCOA system providers. To address this, the eCOA Consortium formed a team in early 2022 tasked with performing an in-depth analysis of the regulations generated by the EU as well as international collaborations such as the International Medical Device Regulators Forum (IMDRF). The analysis resulted in the generation of 

, a self-published white paper that offers guidance to the medical product development community for when eCOA systems may or may not fall in scope for CE certification. The paper also outlines at a high level the steps required to attain CE certification.

The concept of intended use is at the heart of MDR. The European Medicines Agency (EMA) regulations place the burden of proof upon “manufacturers.” In the context of eCOA systems, the manufacturer would be the software developer (eCOA provider) that generates a digitized form of a licensable and/or publicly available COA when contracted to do so. MDR, Article 2, defines specific purposes for which a system may be considered to be a medical device. Of those specific purposes, those that an eCOA app could be considered to support are “diagnosis, prevention, monitoring, prediction, prognosis, treatment, or alleviation of disease.”

 Typical eCOA systems may be reasonably considered to monitor disease, or more accurately the health status of a trial participant. This is certainly a consideration for sensor-based wearable technologies that are becoming more common in clinical trials, but those devices would themselves be required to attain CE certification.

It is important to understand that the intent of an eCOA system is generally to enable recording of responses to questions that may be part of a standardized assessment and for those responses to be transmitted to a clinical database as source data. In general, eCOAs are not implemented for medical purposes in clinical trials. Sites do not implement medical treatments based solely on these data; the data inform a statistically driven assessment of the effect of a given study intervention or interventions. Commonly, eCOA software does not implement medical treatments based on the data generated either, if it did, it would then be classified as SaMD and require the CE mark.

The regulation does leave room for interpretation, and as such creates the opportunity for misclassification of an eCOA system. To support that assessment, the eCOA Consortium’s white paper incorporates a decision tree that may serve as a guide to support the assessment of whether an eCOA app could require CE certification.

The paper is available for download from the eCOA Consortium’s webpage: 

https://c-path.org/wp-content/uploads/2023/03/eCOA-Systems-and-CE-Certification.pdf

Authored on behalf of Critical Path Institute’s eCOA Consortium by Scottie Kern, Executive Director, eCOA Consortium; Ian Pallett, AD Sensor Solutions, Parexel; and Rinah Yamamoto, Principal Scientist, Clinical Ink

 Legislative Acts: Regulation (EU) 2017/745 of the European Parliament and of the Council 

https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32017R0745

MDCG 2021-24: Guidance on Classification of Medical Devices 

https://health.ec.europa.eu/system/files/2021-10/mdcg_2021-24_en_0.pdf

Data Collection

EU Medical Device Regulation must be followed carefully as it leaves room for interpretation.

eCOA Systems and the CE Mark: Navigating the Regulations

An expectation for the use of quality tolerance limits (QTLs) was formally established in the International Conference for Harmonization (ICH) E6 (R2) GCP guideline published in 2016. The aim of QTLs, as indicated in the guideline, is to “identify systematic issues that can impact subject safety or reliability of trial results. Detection of deviations from the predefined quality tolerance limits should trigger an evaluation to determine if action is needed.”

CluePoints released a QTL module into its risk-based quality management (RBQM) platform in 2019, which has provided an excellent opportunity to view industry trends related to QTL adoption.

Overall, 31.6% of organizations actively using the platform for centralized monitoring since 2019 have also implemented QTLs. A total of 115 studies have used QTLs thus far, and the rate of adoption has increased steadily each year as depicted in Figure 1 below. In 2019, only 5.9% of studies added to the platform included QTLs. However, this increased to 17.2% by 2021 and jumped to 37.7% in 2022.

For studies using QTLs, there is a median of two QTLs implemented per study. The most commonly observed range of QTLs per study is one to three, with only 25% of the studies implementing four or more QTLs to date.

The QTLs implemented thus far are distributed across three general categories as follows:

Compliance QTLs primarily focus on missed assessments (e.g., supporting key study objectives), as well as errors in randomization. Enrollment and retention QTLs focus on study and/or treatment discontinuations, along with enrollment trends (e.g., unbalanced by region, demographics, etc.). Safety QTLs focus on adverse events (e.g., adverse events related to the investigational product or specific laboratory results).

It is clear that the rate of QTL adoption within the industry is now rapidly increasing, and particularly among those organizations that are already conducting RBQM and centralized monitoring. We will continue to monitor QTL adoption trends along with emerging best practices in the coming year.

, data and knowledge manager; both with CluePoints

RBQM

Industry sees high increase in the use of QTLs.

Quality Tolerance Limits: A Review of Industry Trends

At the SCOPE meeting last month, the panel discussion “Operationalizing Diversity in Clinical Trials: Cross-Functional Capabilities Needed to Recruit, Track, & Adapt for Meaningful Change” included input for industry that supports this goal. As moderator Jason Gubb, a clinical operations consultant and former GSK head of global clinical sciences and operations, noted, “The goal [for diversity in clinical trials] is to go beyond intent and get real about making meaningful change. We don’t want a cookie-cutter approach and we don’t want a tick-the-box exercise.”

Jamie Brewer, MD, clinical team lead, Office of Oncologic Diseases, at FDA's Center for Drug Evaluation and Research (CDER), told attendees the best way to engage with FDA is to simply submit the diversity plan as early as possible in your clinical development plan. She explained, “We aren’t just checking the box that they are submitted. We are reviewing them and discussing them internally, reading your justifications for your enrollment goals, asking questions back…asking for you to maybe provide further justification or how the enrollment goal will be met or if it is appropriate.”

It is not a prescriptive but iterative process, which can include further meetings or interactions between FDA and the sponsor on how they are achieving and revisiting their clinical trial enrollment goals, providing retention goals or updates, as well as updates to ensure that goals are being met.

Brewer also noted that industry and FDA are “walking in uncharted territory.” She said that how industry goes about not reinventing the wheel, and utilize what it's learned from developing diversity plans and share in a way that is mutually acceptable to all stakeholders, is not in FDA’s wheelhouse. However, she believes that this resource-sharing would go a long way to help individuals at companies with their own oversight and planning.

IQVIA’s “Advancing Diversity in Clinical Development through Cross-Stakeholder Commitment and Action. 

https://www.iqvia.com/insights/the-iqvia-institute/reports/advancing-diversity-in-clinical-development

TransCelerate BioPharma’s Diversity of Participants in Clinical Trials Initiative, includes program-level and site-level toolkits. 

https://www.transceleratebiopharmainc.com/initiatives/diversity-of-participants-2/

 is Editor-in-Chief of Applied Clinical Trials and can be reached at 

News

Panel discussion at SCOPE highlights strategies to increase diversity in clinical trials.

Diversity Plans, Sharing for Meaningful Change

A deluge is coming. At the end of this month, European Union officials are scheduled to deliver their proposals for a wide-ranging update of the pharmaceutical rules that have been in force for a generation. They have been working on their plans for years—and have repeatedly deferred presenting them to the legislators in the European Parliament and the Council, because all this is such highly sensitive territory. But, finally, changes are on the way, including for clinical trials, and it’s going to take a lot of digesting: a leak of the likely contents seen by 

 runs to well over 1,000 pages. In a bid to limit the overload, here’s a snapshot of officials’ thinking, drawn from the leaked documents, specifically dealing with clinical trials and drug development—with the heavy caution that the only official version of the text has still to be published.

There are measures aimed at supporting drug developers in generating clinical evidence “that meets the needs of the different authorities along the medicines’ lifecycle,” says the leak. A reformed European Medicines Agency (EMA) will be empowered to provide scientific advice in parallel with the scientific advice given by health technology assessment bodies, and to consult other national authorities with clinical trial expertise in providing scientific advice. The regulatory framework is to be given new flexibilities, to deal with emerging developments such as adapted clinical trials, use of real-world evidence, secondary use of health data, and linking the fashionable concept of “regulatory sandboxes”—essentially a looser exploratory space to cooperate on novel medicines.

EMA will be tasked with creating a European network of patient representatives, academics, drug developers, investigators, and centers with expertise in the performance of studies in the pediatric population. This will discuss priorities in the clinical development of drugs for children, in particular in areas of unmet medical need, and coordinate studies relating to pediatric treatments. It is designed to “build up the necessary scientific and administrative competencies at European level, and to avoid unnecessary duplication of studies and testing in the pediatric population.”

At the same time, the regulation of pediatric medicines will be made more agile to respond to the problems that can arise when a full clinical development plan for studies in children has to be agreed with EMA at very early stage. To ease the dilemma of developers who are obliged to make assumptions on the expected results, and then find themselves needing to modify their pediatric investigation plan (PIP), the concept is to be introduced of an “evolutionary” PIP. This foresees the agency agreeing that the development plan will be completed and new information submitted at precise development steps.

But new conditions will apply, too. The PIP waiver that has applied until now for adult products intended for a disease not present in children is to be limited. It will no longer be available where the molecule in question has a mechanism of action that may be efficacious against a different disease in children. “This provision, apart from increasing the number of medicines adequately studied for use in children, is also expected to foster innovation and research,” says the draft of the explanation annexed to the proposals. New and tighter obligations on trial reporting are also expected. After completion of a study, a summary of the results will have to be uploaded to the EU’s database, and for pediatric trials this has to be done within six months of the end of the study, whether or not the trial was terminated prematurely—except for “justified scientific reasons.”

Many other provisions in the unofficial draft text have direct implications for the clinical trials community. The familiar incentives for innovation will be recalibrated, with an emphasis on unmet medical need (UMN) “where research is most needed and investments are more risky.” Basic market exclusivity for orphans will be set at nine years, but at 10 years for meeting UMN—while “well-established use” orphans will be granted only five years of protection. However, a “bonus” market exclusivity extension of one year can be granted where the product is made widely available across the EU. The first two new indications of an orphan will be rewarded with one extra year of exclusivity each.

New criteria are to be set to identify products addressing UMN, including “if at least one of its indications diagnoses, prevents, or treats an orphan condition for which no satisfactory diagnosis, prevention, or treatment method exists or a satisfactory diagnosis, prevention, or treatment method exists and it has been demonstrated by the applicant that such a product will bring exceptional therapeutic advancement. And in both cases, the product will have to provide for “a meaningful reduction in disease morbidity or mortality for the relevant part of the population.” 

View from Brussels

Reformed EMA looks to tackle challenges in drug development, pediatric medicines, and unmet medical need.

Hints on Looming Changes to European Clinical Trial Rules

At a recent multigenerational family gathering, the millennials (and the boomers) were bemoaning the still-in-college-GenZs. “You get to learn all day. I miss that.” I would argue that those in the group that have jobs and responsibilities are still learning every day, but it’s a different learning because of said jobs and responsibilities. There is noise in our modern day life that can inhibit true learning over knee-jerk reactions.

There is probably no job or role today that doesn’t contain a modicum of learning. Whether it’s formal learning courses provided by your company, informal learning provided by your team, or personal learning during the course of your day, there are myriad opportunities from which to learn. But do we learn? Do we understand and incorporate the knowledge or information we have gleaned? Or are we too busy to get it and retain it?

 by Brian S. McGowan, PhD, as an expert in adult learning and change (his LinkedIn tagline is “Learning IS a Behavior”), outlines the three scientific fields that are fundamental in effective change management—learning, cognition, and behavioral economics. McGowan proposes that everyone can learn to change when best practices from these fields are integrated and leveraged to transform thinking and training approaches.

In the upcoming months, McGowan is going to share an article each on these three areas that offers a book for further reading, and an example of how it relates to clinical operations. Hopefully, this will lead to the stickiness of change management that is so desperately needed in our industry.

Sometimes, as I mentioned, the day-to-day is too overwhelming to take in new learnings. You’re too busy. The way you are doing things now works just fine. The way you relate to others is fine. You don’t understand why you need to learn this. It’s busy work.

But what if the way you are doing things is not optimal and there is a better way? What if the way you relate to others is causing unknown problems, and learning could make your work life more collaborative? And, finally, you may not know what you don’t know. Our business is about saving lives, and a little internal effort could go a long way in external realizations.

From the Editor

Noise in modern day life may be interfering with our ability to learn.

Is it Time to Adjust Our Approach to Learning?

Issue PDF

Click the title above for a link to open the Applied Clinical Trials March 2023 issue in an interactive PDF format.

Applied Clinical Trials March 2023 Issue (PDF)

Moe Alsumidaie interviews Sanjay Shukla, MD, MS, CEO of aTyr Pharma, about the company's clinical operations and ongoing Phase III study for pulmonary sarcoidosis. Shukla explains the challenges in conducting a large trial for a rare disease and the company's approach to overcoming them. The trial design, incorporating patient feedback and decentralized clinical trials, are seen as essential for clinical trial and commercialization success.

Moe Alsumidaie: Sanjay, thanks for taking the time to talk with me today. To start, could you tell me about the current state of aTyr Pharma's clinical operations and the ongoing Phase III study for pulmonary sarcoidosis?

 We are currently recruiting patients for a Phase III study of efzofitimod in patients with pulmonary sarcoidosis. This study is one of the largest trials ever conducted in this rare disease, with the goal of enrolling more than 260 patients worldwide. The goal of the study is to evaluate the safety and efficacy of efzofitimod in improving lung function and reducing symptoms in patients with pulmonary sarcoidosis, including cough and shortness of breath. Efzofitimod is an intravenous therapy that is administered through a one-hour infusion, once per month. The fast-track designation from FDA is a testament to the potential of efzofitimod and highlights the need for new treatments for this condition. Additionally, we have also received an orphan drug designation—not just in the US, but also in Europe—which is a great recognition of our efforts in developing new treatments for rare diseases.

MA: What are three significant challenges that aTyr faces in clinical operations, and how are you overcoming them?

 The first challenge is recruiting for such a large trial in a rare disease. We have engaged with clinical trial sites in about ten countries to participate in this trial. The second challenge is the ongoing pandemic, which has created fear and concerns for patients. Despite these challenges, we were able to finish recruiting for the last trial during the worst parts of the pandemic, thanks in part to the motivation of the patients to continue coming to the clinic. The third challenge is ensuring that we have the financial resources to carry out this trial, which is why we are fortunate to have strong investors, including groups like Fidelity, a well-known large mutual fund.

MA: Have you involved patients and investigators in the design of the protocol for the Phase III study?

 Yes, we have incorporated patient feedback into the design of our trial. In fact, we were the first company in the lung disease space to include a steroid reduction element in our protocol, which is directly from patient feedback. This has created a paradigm shift in the industry and has been recognized by worldwide regulators as the most critical endpoint to prioritize.

MA: Can you tell me why the trial design is essential for competitiveness and commercialization success?

 The trial design is essential because it focuses on what is most important to patients, which is improving their symptoms and reducing their use of steroids. We are looking at the most validated symptom indexes, such as the King's Sarcoidosis Questionnaire, which tracks cough and shortness of breath, and forced vital capacity, to measure lung function. Additionally, we are also looking at disease related inflammatory markers that have been identified as critical by medical experts. All of these are essential factors that make this trial design one that lends to commercialization success.

MA: Have you taken into account the elements of decentralized clinical trials in your study and what potential obstacles do you foresee?

 Yes, we can incorporate some elements into our trial. For example, we can track steroid reduction through electronic diaries in addition to in-person visits with the investigator. This element can reduce the burden of the number of times patients have to come on site as part of the study. However, for this rare disease, we are not yet at the stage where patients can perform more sophisticated tests at home.

MA: Women and some minorities are at greater risk of developing pulmonary sarcoidosis. How important is it that your clinical trials are diversified?

Sanjay Shukla, MD, MS, CEO of aTyr Pharma, discusses the company's clinical operations and ongoing Phase III study for pulmonary sarcoidosis.

Applied Clinical Trials-03-01-2023