Innovation in feasibility tools and data platforms has opened doors to new possibilities for clinical trial design and management. However, amid an increasingly complex data and therapeutic landscape, understanding how to apply these tools based on the specific needs and requirements of each trial can be a daunting task.

Combining the right data-driven resources and operational expertise will help sponsors and clinical trial teams ensure the most comprehensive, effective approach for their clinical trials. This article provides an overview of the key benefits and limitations of common data tools along with some additional considerations to help get clinical studies off on the right foot.

To many, the thought of clinical trial feasibility brings to mind potential site and investigator candidates, and with good reason—selecting the right sites is arguably the most important aspect of the feasibility efforts for any given trial. However, the path to optimal site selection has grown increasingly complex over time due to both the nuanced requirements of clinical trials as well as the variety of clinical data platforms available. The medical technology landscape also plays as role in facilitating this evolution as it has become the backbone for treatments in many therapeutic areas.

In the current environment, the ability to pre-identify sites and investigators that have access to likely eligible patients is a granular aspect of the feasibility process. Fewer therapies operate as a “catch-all” for patients with a specific disease. Rather, with the development of gene, cell, and other biologically-oriented therapies, the idea of personalized or “precision” medicine is becoming more prevalent within clinical research. This has led to patients being sought out for clinical studies not only for exhibiting a target indication, but also for being characterized by a particular biomarker or genetic status.

Notably, as the ubiquity of clinical trial data becomes more widespread, study designs and medical technology should become more refined. Biologically-oriented therapies will become even more commonplace than they are today as a result. In this era of precision medicine, the idea of “patient centricity” itself should be expected to evolve. The availability of patient-level biomarker data will strongly influence this, as the paradigm shifts from patients seeking trials to more trials seeking patients, and more specifically based upon therapies tailored to their specific biological state.

In addition to the evolving industry dynamic, there are a variety of general overarching elements that sponsors and clinical trial teams need to be aware of—many of which should also be considered before initial conversations with a potential investigator. Global epidemiology, as well as country- and region-specific standards of care, are very important in ensuring that trial teams have optimal access to the targeted patient population. For example, many different forms of gastric cancer are dramatically more prevalent in eastern Asian countries relative to North America and western Europe. However, the types of treatments used for these patients can differ from country to country within this region. Therefore, if patient eligibility will be driven by a specific treatment history (which is often the case), selecting the countries in which patients will be most likely to receive this treatment is crucial to the success of the study.

At the site level, a variety of factors weigh into deciphering the ideal “site profile” for a given trial, inclusive of the competitive environment, infrastructure, equipment, and investigator specialty. Along with access to the necessary patient population and the start-up environment, these factors will ultimately drive site selection. While it is standard practice as a part of the site feasibility process to gauge these criteria, it is important to develop a strategy to determine the suitability of a candidate study center before sending questionnaires to potential investigators. Doing so will increase the efficiency of the overall feasibility process—saving time, money, and resources for both a study sponsor and the clinical support team.

Gaining an understanding of the landscape for a planned clinical study that is inclusive of the factors noted is critical for ensuring a trial’s success. This can prove to be a challenging process, especially considering the growing number of data platforms that have emerged in recent years for comparing criteria and planning clinical studies. From tools that mine information from historical and planned studies to those that source and compile de-identified patient electronic medical records (EMRs), there are many options available to help gather insights and aid in devising a data-driven strategy for a clinical trial.

While many niche data platforms exist within the realm of clinical science, the planning and execution of clinical trials has arguably been most impacted by the development and refinement of two types of platforms: those that compile clinical trial data across multiple sources and those that provide patient-level EMR data from sites and investigators. When strategically integrated, these can inform competitive landscape assessments, country selection, enrollment expectations, and site selection.

Data platforms such as Citeline, TrialHub and GlobalData—which allow trial teams to compile current and historical trial data—have become critical for conducting landscape assessments when planning a clinical study. By integrating records from multiple sources of trial information (e.g., publications, press releases, conference abstracts, ClinicalTrials.gov, other region- and country-specific trial registries), these platforms are invaluable for understanding the therapeutic landscape of a target indication. Furthermore, both historic and ongoing trials can be searched according to several different criteria, including but not limited to the country/site of operation, class of intervention, drug mechanism of action, line of therapy, eligibility criteria, and primary endpoints. These platforms help paint a clearer picture of what a study might look like, from the competitive landscape and most optimal countries/regions down to the individual investigator that would be the strongest candidate for inclusion—all without needing to send an e-mail to a single study coordinator.

 Database platforms like TriNetX offer a more granular approach to feasibility based upon real-world data captured from potential study subjects. At the site level, the tools can allow for the pre-identification of patients based upon identifiers such as diagnostic codes (e.g., ICD9, ICD-10), procedural codes (e.g., CPT®), medical laboratory assessments, and genomics information. By leveraging patient-level data, the best site candidates can be pre-identified based upon those with the highest likelihood to have protocol-eligible patients, further strengthening the study’s recruitment potential. On top of this capability, these EMR sources can even inform protocol design by demonstrating the impact of adding/removing eligibility criteria on the potential patient pool.

As the ability to obtain patient-level data continues to increase—with organizations acquiring higher volumes of data from a larger number of hospital systems—there is potential for a dramatic, positive impact in how clinical trials unfold. Since site selection is becoming more influenced by patient-level data, as opposed to investigator-reported patient access, the evolution should translate to an increase in accuracy in which sponsors and clinical trial teams can select the most optimal sites for their studies.

Thus, sponsors will be able to save both time and money throughout the duration of the trial, as well as reduce the likelihood of back-up sites being necessary to complete recruitment.

While trial and EMR data platforms offer numerous advantages, there are limitations that clinical trial leaders must keep in mind with their usage. These tools are only as strong as the breadth of the data they are sourced from. Clinical trial data platforms are often very robust because they integrate data from multiple publicly available sources, but EMR tools tend to be proprietary and source their data from a limited number of centers across variable geographic regions.

Thus, while powerful for instances in which the available data is relevant to the study being planned, they still often represent an incomplete picture of where potential patients may be located.

With the vast amount of data now available at our fingertips, the question for sponsors and clinical trial teams isn’t necessarily whether to use it—but how to apply it most effectively for each study. Leveraging historical trial data databases and EMR sources, for example, has become commonplace in assessing the feasibility of trials in therapeutic areas such as non-small cell lung cancer or type 2 diabetes that possess a large breadth of clinical development history. However, the strength of these platforms is often variable depending upon the targeted patient population. Thus, these data platforms become less advantageous (though not completely so) when planning a rare disease trial.

Notably, it’s important to remember that even the most thorough assessment of available data sources will not add significant value to the implementation of a study without the proper operational expertise needed to take advantage of it. For example, while access to claims and EMR databases can provide insight on the location of potentially eligible patients, these efforts must be supported by a clinical operations study team that understands the nuances of the target therapeutic area. Site and investigator relationships, recruitment paradigms, and the knowledge of site-, country- and region-specific regulatory landscapes, and start-up processes remain key aspects of the clinical trial feasibility process. However, through the proper leveraging of different clinical databases, the efforts put into each of these different areas has the potential to be more fruitful and efficient for the study sponsor.

Furthermore, even the most widely used clinical trial data platforms are only beginning to emphasize efficient ways of searching for historically conducted trials when it comes to specific rare diseases. Some rarer indications also lack established diagnostic standards, such as the International Classification of Diseases ICD-10 code, or lab/genomic indicators which makes it more difficult to pre-identify patients through available EMR data. In these instances, establishing a relationship with a key opinion leader (KOL) for the indication of interest may still offer the greatest value when planning a trial.

A KOL can act as an influencer in applying their clinical expertise in a specific therapeutic area. In the context of clinical research, a KOL is typically an experienced physician who can play a critical role throughout the development of a particular drug. This could take many forms, including advising in protocol development, serving as a principal investigator on the trial, and/or engaging with patient advocacy groups. Having a KOL on board can also help expedite study enrollment, as they typically have very large patient databases and can refer patients that they believe would be strong candidates for a trial. All of these factors are especially pertinent in the context of rare disease trials, for which patients will often travel for hours and across state or country lines to be seen by KOLs within a given field.

Amid ongoing transition in the clinical trial space, as the sheer amount of data continues to grow along with an increase in complexities across many therapeutic areas, the demand for tools and resources to help navigate these elements will also expand. New data-driven solutions will help facilitate the advancement of many exciting new studies, from trial concepts to final site selection, but clinical trial leaders must remember that there is more to the equation in ensuring an efficient and comprehensive process.

The ability to implement tailored program strategies, navigate the unique day-to-day challenges of a clinical trial, and drive development in alignment with regulatory governing bodies all takes proactive problem-solving capabilities. By taking operational and relationship dynamics into account along with procedural aspects, sponsors and clinical trial teams can ensure they are best set up to achieve clinical trial success.

, PhD, associate director, global feasibility, Rho

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Considerations to help tailor new tools as part of a custom clinical trial strategy.

The Benefits and Limitations of Clinical Data Platforms

Clinical research is evolving rapidly, with the digitalization of trials creating a new 

 and a tsunami of new opportunities for critical thinkers with an eye for data quality.

According to the job’s website Recruiter, clinical data management vacancies have increased by almost 94% since 2019.

 Such a surge in demand means organizations are struggling to find the data scientists they need to usher in the new era. Speakers at a recent Society for Clinical Data Management (SCDM) leadership conference, for example, said they were facing huge challenges and were increasingly looking to recruit from other data-driven sectors to secure the requisite skills to build clinical research 2.0.

We know the reason for these huge challenges: decentralized clinical trials (DCTs) have enabled wearables and allowed for at-home and patient-reported clinical data collection. Artificial intelligence (AI), including machine learning (ML) and natural language processing (NLP), is allowing for the more intelligent analysis of trial data. Risk-based approaches, which focus on quality-by-design and critical-to-quality factors, are now mandated by International Council for Harmonization (ICH) regulations, and clinical research 2.0, with its master protocols, adaptive designs, and synthetic arms, are changing the way trials are built, structured, and reported.

Yet while the technologies driving change are well established and readily available, the same cannot be said for the people needed to utilize them. And with the industry undergoing a seismic shift, organizations big and small are all looking for the same skills simultaneously, making for a hugely competitive job market.

The transformation of clinical research, the SCDM has said, is driving an “evolution” in clinical data management (CDM), which has, until now, primarily focused on dataflows and data integrity. In this new paradigm, the CDM remit is broadening to become clinical data science (CDS).

CDS, the body argues in a position paper aimed at helping the industry make the transition, encompasses the processes, domain expertise, technologies, data analytics, and good clinical data management practices that are “essential to prompt decision-making throughout the life cycle of clinical research.” It is a “strategic discipline” that enables the execution of complex protocol designs in a patient-centric, data-driven, risk-based framework while protecting both participant safety and the reliability and credibility of trial results.

There is no denying that these are niche skills needed for roles that, until recently, may not have even existed. It makes recruiting, training, and retaining data scientists one of the biggest challenges facing the clinical research sector today.

Centralized statistical monitoring, which streamlines drug development by allowing for the near real-time analysis of data as it accumulates, is the perfect example of how the changing research paradigm is creating new roles.

The approach replaces retrospective 100% source data verification (SDV), which is inefficient, prone to human error, and unfeasible in light of the increases in data sources, types, and volume. It works on the assumption that data generated at each site should be roughly similar. Datasets are compared to identify site-, country-, or patient-level “outliers” or anomalies and mark them for further investigation. This alerts sponsors to potential issues, enabling them to take early corrective action, optimizing development pathways, and shortening time to market access.

It has also given rise to a whole new role—that of the clinical data analyst. These individuals lead on system setup, which includes data consolidation, and then, throughout the trial lifecycle, apply statistical algorithms to the collected data to identify atypical data patterns.

They describe the pattern or patterns in “risk signals” before presenting them to the sponsor’s study team and supporting them in deciding the next steps.

This relatively new role, which tends to see analysts working in pairs alongside in-sponsor or contract reasearch organization (CRO) study teams, can be incredibly rewarding.

Multiple CluePoints clinical data analysts, for example, worked with Pfizer’s COVID-19 vaccine trial team, helping to accelerate the development of the product, which ultimately ushered in the beginning of the end of the pandemic crisis.

The safety and efficacy of the BNT162b2 mRNA COVID-19 vaccine was a highly complex study, including more than 43,000 people from across 150 global sites. It recruited at a rate of 5,000 people a week, combined Phases I, II, and III, and generated an extraordinarily high volume of data. The team supported Pfizer’s in-house analysts in performing daily analyses to ensure risk signals were identified, investigated, and mitigated in near-real time.

They also developed a suite of data visualizations to communicate potential risk areas, allowing Pfizer to increase efficiencies so that it submitted drug applications in record time—just 266 days after the World Health Organization’s pandemic declaration.

With so many new data roles opening up in life sciences, the sector has started to look outside of the industry to fulfill its needs. And with the opportunity to play a part in developing the drugs that will change people’s lives for the better, there is no shortage of willing converts.

To enter the realm of clinical data analytics, for example, candidates need two main strings to their bow: technical aptitude and critical thinking. SAS programming, data management, and data manipulation skills are required for the data harmonization and system setup part of the role. Still, people also need an analytical mind to review the data, writing skills to describe the signals, and good communication skills to present the signal and work with the study teams to respond to them.

As clinical research continues to evolve, the demand for skills in areas including data science and analytics will continue to grow.

Filling those vacancies could prove a stumbling block to progression. Yet it’s important to note that people working in other data-driven sectors, from finance to social media, have hugely valuable and infinitely transferable skills.

And many are ready and willing to make the jump if it means having the opportunity to help develop safe, effective medicines that could change people’s lives for the better.

Career Outlook for Clinical Data Managers. Recruiter.com, 

https://www.recruiter.com/careers/clinical-data-managers/outlook/

New Position Paper: How to Create a Clinical Data Science Organization. 

How CluePoints Enabled Pfizer’s ‘Hyperspeed’ Vaccine. CluePoints, 

https://pubs.acs.org/doi/full/10.1021/acsguide.40303

Simultaneous and niche skills sought amid evolution in clinical data management.

The Role of Transferable Skills and Data Science in the Move to Digitally-Enabled Clinical Research

In 2021, a Tufts Center for the Study of Drug Development study showed that a typical Phase III clinical trial generates 3.56 million data points.

If we consider that number from another angle —that just 20% of clinical trial data are analyzed, as opposed to 80% that are stored—it could illustrate the level of application of data science used in the pharmaceutical industry. “We have been slow to adopt [artificial intelligence]-driven solutions,” said Wendy Morahan, senior director, clinical data analytics, for IQVIA. She made her remarks, and cited the 20-80 breakdown, in a webcast broadcast last year.

Morahan’s view is shared inside and outside the industry.

“A lack of evolution within the pharmaceutical industry’s analytic capabilities has given rise to inefficiency and a failure to leverage modern technologies within the clinical development space,” says TransCelerate BioPharma Inc. on its website.

Adoption of data analytics in the pharma industry has been slower than in other industries, says Andrew Lo, PhD, Charles E. and Susan T. Harris professor, a professor of finance, and the director of the Laboratory for Financial Engineering at the MIT Sloan School of Management. In research over the last decade, the Lo team has shown how data science, using scores of factors, can more accurately predict a drug’s approval success.

But back to the 3.6 million data points. According to the 20-80 breakdown, a little math shows that if 720,000 data points are analyzed and 2.8 million remain warehoused, a lot of data is left on the cutting room floor. (A 2018

 article also discussed that percentage breakdown.

) The Tufts study also showed that Phases II and III now include 263 procedures per patient, with the generated data used to produce 20 endpoints. These data illustrate the yin and yang of decentralized clinical trials (DCTs): easier on patients, yet often requiring more data integration and analysis.

That data-braiding effort is exactly the sweet spot. Integrating different types of data, including socioeconomic, electronic health record (EHR)- generated information, claims data, and financial information, can provide clearer pictures of health at population levels and holistic pictures at the personal level.

Data analytics, according to interviews and published studies, as the Lo lab has shown, can de-risk financial investments by quantifying a product’s chances for approval; cut costs in clinical trials, including with the elimination of a human control arm and replacing it with a synthetic one; and find evidence, early on, of a drug’s possible harmful effects.

Not everyone buys the argument that pharma

 is behind the times. Longtime pharma consultant Craig Lipset says in his experience, the opposite is true. “Every major pharma company has [invested in] tremendous executive-level hiring and built massive organizations” around the theme of data science across their business, he tells 

Pharma, he continues, is entering into deals with AI companies and hiring intensively. He notes a Janssen data science symposium at which he spoke last year had “massive” attendance. Lo also had a similar story regarding a Novartis internal conference at which he spoke; hundreds of data scientists were present, he says.

Adds Lipset: “I’d say the question now is if these investments are paying off...is data science really the answer or is basic science just really hard?”

Evidence of blanket, c-suite adoption of data analysis might be debatable. But pharma professionals who deal with digits day-to-day are joining groups using data analysis—as much as a disparate health system like ours can—to standardize data, create universal languages, and streamline other processes. These individuals, along with academics and the federal government, are banding together to figure out how to base the clinical trial world, and healthcare improvement, on data science.

For the most part, these people, thousands of them, are volunteering their time—with their employers’ blessing, and in some cases, with their employers’ money, via funding donations or membership fees.

From afar, pharma’s strategy here, if there is one, resembles its approach to R&D: why do it internally if a biotech or asset exists to fill the need?

That said, the volunteers are not competitively shackled, whether volunteering in the preclinical or post-market surveillance space.

“We are committed to better healthcare, we each contribute what we can do,” says George Hripcsak, MD, the Vivian Beaumont Allen professor of biomedical informatics, Columbia University, and director of the OHDSI Coordinating Center. The goal here isn’t for “someone else to make money or because they have a grant,” he adds.

The uninitiated might find the array of acronyms dizzying: OHDSI (Observational Health Data Sciences and Informatics), DiMe (Digital Medical Society), DTRA (Decentralized Trial Research Alliance), Vulcan HL7 FHIR (Fast Healthcare Interoperability Resources).

These organizations and consortiums, regardless of their stated mission, be it language standardization, more rapid advancement use of DCTs, or, in the case of TransCelerate, to improve how clinical trials operate, all see data integration as a requirement in improving efficiencies and in the end, healthcare. “Clinical research, healthcare, and market data are captured across multiple sources in multiple structures preventing system interoperability that, when connected, will enable and drive insights, as well as allow for the integration of various data sources,” according to TransCelerate.

The people creating these pools, or in some cases oceans, of data, do so for just members of their particular group, like the Duke University Health system,

 or, in the case of OHDSI, for anyone who wants to dive in. Other organizations, such as the NIH’s National Center for Advancing Translational Sciences (NCATS) provides funding through its Clinical and Translational Sciences Awards (CTSA) program to help awardees create collaborations between their networks “to implement, assess, and/or disseminate discoveries across the network.”

Some entities’ missions are to make running clinical trials easier and more efficient. The Medical University of South Carolina has built an open-source network with dozens of members in healthcare systems and research institutions; the Universities of Iowa and Utah pitch in to help run the open source functionality.

 Built on CTSA funding, the system has two components: one takes researchers’ requests for specific types of data for help with biostatistics, and so on; while the second component integrates six networks of data submitted by the participating members. These data networks include EHR patient information, the spending tracker SmartStream, and Click, an institutional review board (IRB) submissions and tracking product.

The second system, dubbed RINS (Research Integrated Network of Systems), says Leslie A. Lenert, MD, chief research information officer, Medical University of South Carolina and director, Biomedical Informatics Center, allows the center “to match controls. We can do faster enrollment, we track time to start [a trial], from the day the grant arrives to first enrollment,” he explains. The point, adds Lenert, is to reduce time and money spent on a trial, from the time used in its design, acquiring IRB approval, and so on. “All those processes take time at a given site,” he says.

OHDSI, which is coordinated by the Columbia University Department of Biomedical Informatics, has an extensive publication track record—2,000-plus authors, 500-plus articles; 3,000 members from 80 countries.

The OHDSI collaborators page includes the Who’s Who in pharma: Amgen, Bayer AG, Johnson & Johnson (Janssen), Merck—along with universities, research hospitals, IQVIA, and Oracle. It was the predecessors of OHDSI who built the common open-source platform known as OMOP, or Observational Medical Outcomes Partnership.

Since 2009-2010, when OMOP went operational, it has been used to convert 928 million patient records to this common data model, 12% of the world’s population, says Hripcsak. Pharma, he adds, donates money or pharma employees donate their time, benefitting the company. Funding also comes through NIH grants and FDA convener grants. OHDSI works with others as well, including FDA, on a devising common vocabulary, data standards, analytic methods, and software tools that are all open sourced. The NIH has recently asked that research applicants familiar with OMOP to use that language in their request for applications.

One area that data analytics is taking hold is in pharmacovigilance. OHDSI has an active group that has published multiple reports on topics such as the accuracy of an open-source knowledge base and finding negative controls.

Adverse events (AEs) are an important area for concern. Reports of death from AEs have gone up—in 2016, there were 141,851 reports of death; in 2021, 187,493. Reports of serious events during that time also rose, from 825,858 to 1,373,116.

Precision pharmacovigilance is not just a regulatory obligation anymore, says Bruce Palsulich, vice president, product strategy, Oracle. Pharmacovigilance has become a highly curated dataset, an information asset. “Can you leverage this even preclinical? What is the risk profile of certain targets during discovery?” he asks. “That shift toward a predictive and leveraging a pharmacovigilance dataset can be used throughout the clinical trial lifecycle; we are seeing it used.”

With a description of the event, adds Palsulich, humans could read the abstract to get the information or leverage AI to extract it, and then have a human do the quality review. That, he says, could eliminate a high amount of intake process, likely 50% or more. These techniques would allow the intake of information to scale broadly, he explains, and inform more about the safety process. The intent is to move “toward having adverse events in the database to do analytics, find the potential signals or risks, or increasing frequency.”

While there are certain AI improvements that will drive cost reductions, not every use case will have a measurable return on investment,

, according to Palsulich. For pharma, the real gain is with differentiating competition: “We will continue to see pharmacovigilance as being more of a valued asset than a regulatory” obligation, he tells 

Education and the realities of data integration

Ken Gersing, MD, director of information, division of clinical innovation, NCATS, knows about the importance of data harmonization and creation: He is part of the team that created N3C, the nationwide COVID-19 data tracking system. He equated how data integration should be handled to a car assembly line.

“Think of the Ford River Rouge auto factory that Henry Ford built,” Gersing said in an email. “Steel went in the door and out popped a car at the end of the assembly line. Data is similar in that in order to use it at every stage, you need to know how each step impacts the next. If investigators don’t understand the data or if the data is transformed incorrectly the conclusions could be wrong.”

And that final result, he said, very much depends on adequate training; just having the data does not promise effective clinical decision-making or anything else.

Added Gersing: “We need an intelligent workforce that understands how algorithms work, the data that created them, their limitations, the impact of new data, and the context for which they are intended. If not, the car will just not work properly.” 

Think about the impact of wrong information going into a clinical decision support system, he continued. If the conclusions are in a peer-reviewed journal, the author will receive feedback on their work but clinical care is not impacted. But in this country’s modern health care system, decision support could be embedded in a health system EHR.If this were happen, the misinformation would be propagated to all the health care providers.

He and MIT’s Lo said missing data is a huge issue in the field. “All insights are based on the quality, accuracy, and comprehensiveness of that data,” explained Gersing. “The less missing data I have, the more I can say it helps [the patient].”

And so back to the discussion of pharma and its direct involvement with data scientists. Lo, who is now well-known in large pharma circles, says the answer is mixed, and depends on which company that is under discussion.

But in general, “if they don’t hire data scientists they will be left behind,” he asserts.

 For more information on clinical endpoint standardization efforts, read this 

 is a freelance writer for medical, clinical trials, and pharma information.

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Assessing industry’s use and integration of clinical data science across business.

Progress Report: Pharma Adoption of Data Analytics

The well-recognized potential of sensor-based digital health technologies (DHTs) as drug development tools has not led to their widespread adoption by drug developers as originally anticipated.

 Part of the problem is the lack of consensus on regulatory terminology and the corresponding regulatory pathways that would enable the effective leveraging of these technologies in medical product development.

FDA defines a DHT as “a system that uses computing platforms, connectivity, software, and/or sensors for healthcare and related uses. These technologies span a wide range of uses, from applications in general wellness to applications as a medical device. They include technologies intended for use as a medical product, in a medical product, or as an adjunct to other medical products (devices, drugs, and biologics). They may also be used to develop or study medical products.”

Although the European Medicines Agency (EMA) does not provide a precise definition of DHT, its description of the digital technologies covered by its remit is virtually the same.

 Hence, the dizzyingly broad definition of DHTs encompasses a vast array of tools and applications, while the uses of the various DHTs in clinical investigations come with different implications and require different regulatory considerations.

The use of body-worn sensors, combined with data processing algorithms in drug development, is particularly challenging. The data generated can be a biomarker or a clinical outcome assessment (COA), but the distinction between the two is, at times, blurred by conflicting definitions. One area of confusion is when these sensors (e.g., accelerometers) are used to collect data on the wearer’s real-world behavior or activity. They can be COAs, but the passive nature of these measurements does not fit within the four types of COAs defined by FDA.

 These have been called “DHT-passive monitoring COAs” within FDA’s COA qualification program

 but remained undefined in FDA’s recent draft guidance titled “Patient-Focused Drug Development: Selecting, Developing, or Modifying Fit-for-Purpose Clinical Outcome Assessments.”

As mentioned, wearable sensor data can also be a biomarker as defined by FDA5 and the EMA.

 It is important to note, however, the definitions from these agencies are not entirely congruent. While FDA clearly defines biomarker categories and characteristics as well as the distinctions from COAs, EMA provides a specific definition for digital biomarkers that, among other things, blurs the distinction between COAs and biomarkers. In addition, the emergence of the term "digital biomarker" has led to further conflation of the definitions of biomarkers and COAs in the scientific literature.

Why do these definitions matter? If there is no consensus on the definitions of and distinctions between COAs and biomarkers in the context of sensor-based DHTs, tribal language within scientific communities emerges and produces inefficiencies. Since drug development is multi-national and multi-jurisdictional, common terminology is essential but unattained. To further illustrate this point, the Mobilise-D project funded by the Innovative Medicines Initiative in Europe uses the following definition in relation to wearable sensor-derived measures of mobility: “A mobility outcome is a biomarker that provides a quantification of each patient’s mobility.”

 This definition looks odd to those of us who would consider mobility a COA concept based on FDA’s biomarker and COA definitions, but it highlights the fact that whether a measure derived from wearable sensors is considered a biomarker or a COA can depend on the jurisdiction. That is a problem.

Similar problems have existed and have been solved through collaboration. Critical Path Institute’s (C-Path’s) eCOA Consortium and Patient-Reported Outcome Consortium have a joint “Lexicon” project aimed at ensuring that eCOA system providers and clinical trial sponsors speak a common eCOA language. It’s time to expand beyond C-Path to engage other stakeholders, such as the European Federation of Pharmaceutical Industries and Associations (EFPIA), Foundation for the National Institutes of Health, FDA, and EMA, to reach consensus on conflicting DHT definitions and terminology in the regulatory context. To help fulfill the potential of DHTs in medical product development, the eCOA Consortium is seeking a productive path toward doing just that.

EFPIA is building on this and other sources of established terminology in an upcoming paper that calls for alignment on DHT-related concepts and terminology. C-Path looks forward to engaging with EFPIA, FDA, EMA, and other stakeholders to help reach the full potential of sensor-based DHTs in drug development through consensus on a common language that is essential for success.

Authored on behalf of Critical Path Institute’s eCOA Consortium by Elena S Izmailova, Chief Scientific Officer, Koneksa Health; Christine Guo, Chief Scientific Officer, ActiGraph; and Stephen J Coons, Senior Vice President, Clinical Outcome Assessment Program, Critical Path Institute

Izmailova, E.S.; Wagner, J.A.; Perakslis, E.D. Wearable Devices in Clinical Trials: Hype and Hypothesis. 

https://pubmed.ncbi.nlm.nih.gov/29205294/

Digital Health Technologies in Drug Development: Accelerating the Momentum in Europe. EFPIA, May 19, 2022, 

https://www.efpia.eu/news-events/the-efpia-view/blog-articles/digital-health-technologies-in-drug-development/

Digital Health Technologies for Remote Data Acquisition in Clinical Investigations: Guidance for Industry, Investigators, and Other Stakeholders (Draft)

https://www.fda.gov/media/155022/download

Questions and Answers: Qualification of Digital Technology-based Methodologies to Support Approval of Medicinal Products 

https://www.ema.europa.eu/en/documents/other/questions-answers-qualification-digital-technology-based-methodologies-support-approval-medicinal_en.pdf

(National Center for Biotechnology Information, December 2020). 

https://www.ncbi.nlm.nih.gov/books/NBK566059/

Clinical Outcome Assessments (COA) Qualification Program Submissions 

https://www.fda.gov/drugs/clinical-outcome-assessment-coa-qualification-program/clinical-outcome-assessments-coa-qualification-program-submissions

Patient-Focused Drug Development: Selecting, Developing, or Modifying Fit-for-Purpose Clinical Outcome Assessments (Draft) 

https://www.fda.gov/regulatory-information/search-fda-guidance-documents/patient-focused-drug-development-selecting-developing-or-modifying-fit-purpose-clinical-outcome

Vasudevan, S.; Saha, A.; Tarver, M.E.; Patel, B. Digital Biomarkers: Convergence of Digital Health Technologies and Biomarkers. 

https://pubmed.ncbi.nlm.nih.gov/35338234/

Viceconti, M.; Hernandez Penna S.; Dartee, W.; et al. Toward a Regulatory Qualification of Real-World Mobility Performance Biomarkers in Parkinson’s Patients Using Digital Mobility Outcomes. 

https://www.mdpi.com/1424-8220/20/20/5920

Data Collection

Despite high potential of DHTs, regulatory picture remains unclear.

Conflicting Terminology in Digital Health Space: A Call for Consensus

A detailed report from key House Committees blasts FDA’s role advising, assessing, and providing accelerated approval in 2021 for Biogen’s controversial Alzheimer’s disease treatment Aduhelm (aducanumab). The critique calls for changes in agency interactions with manufacturers to more carefully document meetings and other communications and to clarify the agency’s role in advising sponsors on product testing and submissions.

The joint investigation by the House Energy & Commerce Committee and the House Committee on Oversight & Reform on FDA approval of Aduhelm slams FDA officials for failing to record multiple interactions with the company, particularly their assistance in developing Biogen’s briefing documents for a key advisory committee meeting. The Democrat-led panels reviewed hundreds of reports and records that document how FDA formed a working group with the sponsor that engaged in dozens of meetings and more than 100 calls, email exchanges, and other interactions to review research data on the drug. The investigators report that FDA staffers helped Biogen revise its clinical trials and then surprised all observers with its decision to adopt the accelerated approval pathway for Aduhelm despite questionable efficacy data and opposition from some agency units.

Concerns about this unusual collaboration in developing and approving Aduhelm was compounded by Biogen’s high launch price for the drug despite the limited evidence of treatment value and benefit. The Congressional report examines closely the company’s decision to set a price of $56,000 for treatment per year, along with its extensive plan for marketing the new drug and details on company board meetings to approve the program. In the end, Biogen’s strategy failed, as the potential for huge outlays for Medicare beneficiaries prompted further assessment by the Centers for Medicare and Medicaid Services (CMS) that led to highly limited coverage of the drug and similar curbs on prescribing by insurers and health plans.

The House report calls on FDA to take actions to restore public “trust in the agency’s processes and assurances of drug safety and efficacy.” This includes fully documenting “all substantive FDA interactions with drug sponsors” and developing a protocol to address the preparation of joint briefing documents for advisory committees. The legislators also recommend that FDA update industry guidance for developing new Alzheimer’s drugs.

Meanwhile, FDA officials have revised the accelerated approval process by limiting time frames for manufacturers to conduct requested confirmatory studies and expanding efforts to remove from the market drugs that fail to document post-approval efficacy. The agency gained support for such action from provisions in the massive Consolidated Appropriations Act for 2023 designed to ensure that sponsors conduct required post-approval confirmatory studies in a timely manner. The legislation clarifies that FDA reviewers may require sponsors to begin post-approval studies prior to product approval and speeds up the process for the agency to withdraw from the market drugs that fail to confirm anticipated benefits in a timely manner.

Sponsors must update the agency on confirmatory trial status every six months, and a new FDA accelerated approval oversight council will review the program and its policies and explain rationales for accepting confirming evidence or pulling a product from the market.

The agency also addresses concerns about defining study requirements for drugs seeking accelerated approval in last year’s revisions to the Prescription Drug User Fee Act (PDUFA), which clarifies the process for FDA and industry to assess likely requirements for post-marketing studies earlier in the development process. At the same time, FDA has intensified actions on drugs granted accelerated approval that fail to provide acceptable confirmatory evidence of product efficacy in a timely manner. 

Added concerns have been raised by the recent year-end reports of new drugs for 2022, which reveal a notable decline in innovative therapies gaining market approval. One interpretation is that FDA is going slow on speeding innovative drugs to market to avoid more criticism of its review process. Only 37 new molecular entities were approved last year, far below the usual 50 or more new therapies in recent years. Eight new biologics were cleared, including several new gene therapies and vaccines, but again fewer than in 2021.

View from Washington

Probe targets agency’s role in advising on drug testing and submissions.

FDA Interactions With Industry Under the Microscope

First off, Happy New Year! Our January/February is a single issue, thus the reason for its arrival in your inbox in February. The 

 team just got back from SCOPE, the Summit for Clinical Operations Executives annual meeting and it was a great event. In three words…we are back.

There were over 3,200 attendees at this year’s event, which was not the first one in person since COVID, but the overall mood and appetite for learning, sharing, and networking was off the charts. Appropriately, many of the sessions presented what was learned during COVID and how those learnings should be woven back into said area.

There is also an increased sense of urgency that industry needs to streamline processes and optimize trials for lower overall drug development costs, as well as to answer those efficacy questions on investigational drugs faster.

This year, SCOPE launched its Best in Show awards, which allowed attendees to vote on the best new products in the clinical trials and research space. Thirty-five companies exhibiting at the event entered nearly 40 new products launched since February 2022. This year’s winners are in alphabetical order: Anju Software’s TA Scan 6 (now with integrated diversity data); EmVenio’s Community Research Sites; Medidata’s myMedidata App (v1); StudyKIK’s StudyCash, and uMotif’s eCOA/ePRO and Engagement Platform.

There was plenty of knowledge and intel to share with the larger clinical trials community, which 

 will do throughout the year, but check out the reports on the 

 of our site. These include DE&I, which is moving the needle toward implementation and best practices; AI/ML; how CROs can work more effectively with smaller biopharma; ESG is not only for executives—impacts on climate and society are alive and well in the clinical trials industry; and many more.

Now, the headline…if SCOPE moves out of Orlando, this headline will not age well (I’m not starting rumors here, I heard no such thing). But it’s not “What Happens in Orlando Stays in Orlando.” Who wants that? Our industry needs to share and collaborate. And SCOPE was just the place to reiterate that fact.

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

From the Editor

Highlights from the annual SCOPE conference held in Orlando, FL.

2023 SCOPE: It’s an Orlando Thing

Issue PDF

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

Applied Clinical Trials January/February 2023 Issue (PDF)

Drawing insights from industry news and the findings of a 

 of over 150 biopharma and biotech leaders conducted by the PPD clinical research business of Thermo Fisher Scientific, the following rise to the top as four notable lessons learned in drug development over the past year and what they might mean for 2023.

Lesson No. 1: Decentralized trials have redefined what is possible, but have yet to reach their full potential

As stakeholders across the drug development industry would attest, decentralized clinical trials (DCTs) have been a point of much interest over the past several years. COVID-19 highlighted the value of remote approaches in many fields, but DCTs offered an opportunity beyond the needs of the pandemic: to make clinical research easier for patients to participate in and more accessible to a wider population not served by traditional study design.

The use of decentralized and hybrid clinical trials has increased considerably—51% of respondents in our survey reported that the COVID-19 pandemic encouraged their organization to decentralize clinical trials. Those whose organizations already use DCTs anticipate their use increasing by 24% over the next two years. DCTs offer a potential solution to mounting concerns over patient recruitment and retention, as 

 that decentralized and hybrid models may yield enrollment 1.3 to 2 times faster than traditional approaches and retain those patients at a 90% higher rate. Patients also are enthusiastic about the opportunities offered by these trials. In 

one survey published in JAMA Network Open

, 80% of cancer patients and survivors reported being open to remote interventions, and 77% said they’d join a trial if it were at least as easy to access as regular care.

While technology is expanding rapidly to support the unique needs of these trials, sponsors and sites often face decision paralysis in the face of so many tech options. Many excellent tools are available, but sites and sponsors generally need multiple tools to carry out a study—each of which has dozens of potential options that may not work well together or with the tech at partnering organizations. Good training, user-friendliness and seamless operation are key to making a strong DCT tool, although no perfect option yet exists. The field has a great deal of work to do for DCT tools to reach their full potential. It is also important to note that deployment of technology and decentralized clinical trial design should not take a “one-size-fits-all" approach. A best practice is to perform an intentional, thoughtful assessment for each protocol to determine what makes sense for specific patient populations, schedules of events and procedures, disease areas, and trial objectives.

DCTs and RWD among topics for industry to focus on moving into 2023.

Applied Clinical Trials-02-01-2023

The Benefits and Limitations of Clinical Data Platforms

The Role of Transferable Skills and Data Science in the Move to Digitally-Enabled Clinical Research

Progress Report: Pharma Adoption of Data Analytics

Conflicting Terminology in Digital Health Space: A Call for Consensus

FDA Interactions With Industry Under the Microscope

2023 SCOPE: It’s an Orlando Thing

Applied Clinical Trials January/February 2023 Issue (PDF)

Lessons Learned in 2022 and the Impact on Drug Development in the Year Ahead

Get it Right Next Time? EU Legislation Under Critical Review

Advances in eSource Interoperability Between EHR and EDC

Identifying Patient Needs