Kenneth A. Getz

Data has truly become the currency in our everyday lives, and the volume and speed at which it is being collected, processed and exchanged is staggering. For instance, every minute, 188 million emails are sent, 4.49 million Google searches are conducted, and 4.5 million videos are watched on YouTube.

 This proliferation of information can also be seen in the world of clinical research, where the amount of clinical data per trial has increased by 183% in the last decade.

The growing volume of clinical data illustrates one significant way in which the drug development landscape is evolving. As clinical trials are becoming more decentralized, the variety of data types and sources, including genomics, biomarkers, wearables and images, that are being used in drug development is also expanding. With the amount of diverse data that must now be collected and analyzed, the life sciences industry is faced with increasingly complex studies that present new challenges in clinical data management.

From October 4 to December 30, 2019, eClinical Solutions and the Tufts Center for the Study of Drug Development (Tufts CSDD) conducted a survey to understand the trends in clinical data management and assess how sponsors are responding to current challenges. 149 individuals in senior-level data management, data sciences, biostatistics, information technology and clinical research operations roles participated in the study. Of the respondents, 48% were one of several individuals within an organization responsible for clinical data, 33% were primarily responsible for clinical data and 19% were frequent users of clinical data systems. 

The majority of respondents came from pharmaceutical (41%) and biopharmaceutical (37%) sponsors, though CROs (10%) and independent contracts (6%) were also represented. The sizes of respondents’ organizations varied from small sponsors (30%) that had initiated an average of 2.9 trials yearly, medium sponsors (29%) that had initiated an average of 23.8 trials yearly and large sponsors (17%) that had initiated an average of 112.4 trials yearly.

Sponsors are collecting more data types than ever. Though non-CRF data is the most commonly used in clinical trials among 87% of respondents, a variety of other data types and sources are also being used. Nearly two thirds of sponsors are incorporating direct data capture, apps, devices and medical images as other data sources, while roughly one-third are using electronic health records (43.4%) and omics data (35.5%).

Data sources contributing to significant delays

The growing number of external data sources is associated with longer database lock cycle times with more than half of the study respondents citing non-CRF data as a primary contributing factor. Since 2017, this has resulted in a 40% increase in time from Last Patient Last Visit (LPLV) to database lock. Specifically, large pharmaceutical companies using four or more sources are experiencing a three-week increase in Last Patient Last Visit (LPLV) to database lock.

Last Patient Last Visit (LPLV) to Database Lock Cycle Times

Labor intensive data management activities

Although the availability of richer data sets is essential for gaining deeper insights to advance drug development, sponsors are faced with challenges in all data management activities related to organizing and reviewing the immense volume of data. Among all participants in the Tufts-eClinical Solutions study, analyzing and consuming data was rated as the least challenging of these activities, though over fifty percent of respondents still noted this activity as somewhat or extremely difficult. Of the seven key data management tasks noted in the survey, participants highlighted the process of initiating relationships with data providers as the most time-consuming and labor intensive. This highlights the opportunity for improving vendor relationships as a crucial element in alleviating difficult data management duties.

Data management activity labor intensiveness

Unsophisticated tools to centralize and standardize data

Even though the variety and volume of data continues to evolve, making data management activities more difficult and time consuming, the tools used for data integration and analytics remain relatively unchanged. 75% of companies are still using SAS as the primary tool to convert, explore and analyze data.

With the changing landscape of clinical research presenting challenges in how companies organize and review disparate data types from a multitude of sources, all participants in the Tufts-eClinical Solutions study recognized the need to find solutions. Potential areas for improvement that were highlighted included handling more external data sources, decreasing cycle times through streamlined processes, encouraging patient-centered data acquisition strategies and developing better opportunities for real-world data uses. Life sciences organizations identified several ways in which they are responding to evolving clinical data management requirements in these specific areas.

When an organization outlines a formal data strategy to determine how data will be collected, processed and analyzed, there are clear benefits to clinical research. The greatest impact is to database lock times. Database lock is accomplished 10 days faster with sponsors that have implemented a data strategy compared to those that have not. Since large companies are managing more data sources, they are also more likely to have implemented a data strategy, especially given that 53% of organizations that are using five to six data sources experience longer times to database lock.

Though there are significant benefits, only one-third of the survey respondents have implemented a defined data strategy. Almost half of the respondents were either still examining or planning a data management initiative in the next few months. This means that there is still a great opportunity for all life sciences companies to leverage the benefits of a formal data strategy in reducing trial delays.

To keep pace with the growing number of disparate data, organizations are turning to more advanced tools. The survey findings show that clinical data hubs or repositories are being used more often than other systems, including EDC and SAS-based infrastructure. However, EDC is still being used most often for specifically integrating and organizing direct data capture. Tools like clinical data platforms with robust capabilities to help data managers import, transform and review data will become powerful assets as data sources continue to increase.

Sponsors using a data hub or lake also had more confidence in their analytics capabilities in areas associated with analytics dashboards, interactive visual exploration, the ability to publish and collaborate on analytic content, and augmented data discovery.

Integrating new technology into current systems is just one element in establishing mature analytical capabilities. The survey also revealed a strong association between these capabilities and the implementation of data strategies. Over 50% of respondents who have a defined data strategy noted that their analytical capabilities are more developed.

Another benefit to having definitive data strategies can be seen in the areas of artificial intelligence (AI) and data science competencies. Organizations that have implemented a data strategy are more mature in terms of researching or implementing AI, machine learning and deep learning capabilities. These companies are also more likely to be developing new data sciences functions or specific data sciences roles. In fact, 47.2% that have implemented a data strategy have also established a data sciences function.

Developing analytical and AI capabilities is crucial to preparing for the digital transformation that is sweeping across the drug development industry. Life sciences companies must now meet the increased demand for clinical data and real-time insights from a variety of distinct sources. By automating time-consuming and labor-intensive data tasks, sponsors are able to support greater access to and deeper insights into data to drive innovation and accelerate drug submissions and discoveries.

Respondents to the Tufts-eClinical solutions survey agreed that there are tangible benefits to laying the foundation for AI and data science through formal data strategies. 85% of all respondents rated these qualities as somewhat or very beneficial:

Increased visibility of data to stakeholders for faster decision making

Decreased cycle times through more collaboration and shared analytics

Cost-savings through resource optimization and reduced rework

Faster acquisition and benefits from new real-world data sources that contribute to development


Conclusion: a new era of clinical research

In this modern era of drug development, clinical data has become one of the most valuable assets for life sciences organizations in powering digital transformation. As the Tufts-eClinical Solutions study demonstrates, delays in clinical trials, measured through database lock cycle times and through manual efforts and time-consuming processes associated with assembling data sets for review and insights will continue to increase without the implementation of new strategies. The diversity and volume of clinical data is also growing exponentially, and the tools for managing these must improve to keep pace.

Industry leaders are recognizing that evolving their approach to the research process is critical. By adopting formal data strategies, implementing modern technologies like clinical data platforms and expanding data science competencies, analytics and AI capabilities, sponsors can gain more control over the research process and drive actionable insights from new data streams to continue to push and accelerate drug development.

Anticipating the Impact of the Patient Engagement Movement on Clinical Operations

, Presentation at CROWN conference, Slide #9, January 202

Articles

With an increasing amount of diverse data that must now be collected and analyzed, the industry is faced with increasingly complex studies that present new challenges in data management.

 Enabling Digital Transformation: Managing External Clinical Data Sources to Advance Drug Development

The COVID-19 pandemic caused major disruptions to clinical trial execution in the U.S., impacting key stakeholders across the industry. Investigative site capabilities experienced upheaval, driven by staff furloughs, social-distancing protocols, financial losses, and concerns over patient safety. Sponsors, CROs and other organizations that support drug development shifted to remote working environments. An estimated 80% of non-COVID-19 trials were stopped or interrupted as a result of the COVID-19 pandemic.

An April 2020 study suggests investigative sites demonstrated flexibility and ingenuity in adopting new approaches in order to cope with challenges presented by COVID, with over half of investigative sites transitioning to virtual approaches to interact with patients. More recently, follow-up studies performed in August 2020 have identified persistent impact of COVID, with over 60% reporting an “average” or greater level of impact on ongoing trials and initiation of new trials. Respondents specifically highlighted challenges in patient enrollment and recruitment.

Recent FDA guidance (March 2020 and updated July 2020)

acknowledged that the impact of COVID-19 may require companies conducting clinical trials to consider virtual patient visits or put new processes in place regarding their current protocols. Therefore, there are now more opportunities for using remote healthcare including conducting virtual or decentralized trials, site-less clinical trials and use of other non-traditional approaches that do not involve in-person visits. This shift, driven by the effects of the pandemic, prompted the team at the Tufts Center for the Study of Drug Development to conduct a qualitative study researching the impact of COVID-19 on clinical trial execution. 

The study examined the effects of COVID-19 on clinical trials by gathering insights and perceptions from clinical operations professionals at pharmaceutical companies in the United States. The goal was to understand the modifications that organizations have made in response to the pandemic, especially with regard to remote or virtual approaches. In addition, the study explored how companies have approached planning and implementing remote and virtual approaches to trials, as well as their experience and lessons learned. Last, Tufts researchers sought to understand initial perspectives on how COVID-19 will impact trial execution in the long-term.

A total of 25 interviews with pharmaceutical and biotechnology executives within the clinical operations department from unique companies were conducted using Zoom. All executives worked in the United States and played a role in U.S. clinical operations. Participants were selected from Tufts CSDD proprietary lists of contacts who work in clinical roles and through other collaborations on prior studies or from professional conferences and meetings. Participants were recruited via email from June to early August and have extensive experience conducting clinical trials across diverse therapeutic areas. Interviews were 30 minutes in duration and were conducted by Tufts CSDD researchers using a comprehensive discussion guide encompassing the following areas: (1) COVID-19 Impact on Ongoing Trials, (2) Organizational Preparedness, (3) Trial Amendments and Remote Technologies, (4) Role of Regulatory Agencies (5) Role of Third-Parties, (6) Long-term Impact of COVID-19.

Interviews were conducted with U.S.-based staff at 25 organizations comprised of pharmaceutical, biotechnology, and non-profit research institutions. There were 15 large organizations represented and 8 mid-sized or small companies based on R&D spending.* Two were non-profit organizations. These were included given they often sponsor government-funded clinical research.

Eleven organizations halted ongoing trials and enrollment for at least one month. Shutdowns were most frequently driven by concerns that patients would not be able to safely travel to and receive care at research centers. These concerns were especially poignant in indications that were considered “high-risk” for COVID infection (e.g., cardiology, pulmonology, immunology) or indications where patients had difficulty traveling or for example, had neuromuscular and/or neurodegenerative conditions. In these cases, studies often resumed only once patient safety was assured and R&D organizations had amended protocols to include suitable pandemic contingency plans. An interviewee discussed the impact of COVID on their ongoing trials:

“We put our entire portfolio on hold for two months—it became clear we were not ready to move to a virtual situation.”

Another 12 organizations reported moderate impacts on ongoing trials, most frequently citing delayed enrollment as the primary challenge related to COVID. Several factors may have reduced impact on ongoing trials. Trials in indications such as dermatology and psychiatry may have faced less severe disruption, given more frequent use of telehealth in clinical practice prior to COVID or relative ease of transitioning collection of outcome measures to remote methods (e.g., measuring skin lesion size over telehealth). Additionally, organizations that demonstrated a higher level of pre-COVID preparedness or greater organizational agility tended to report lower impact. Finally, organizations with modest impact often reported partial, geographically isolated closures or understaffing at select sites, rather than full shutdowns reported by those experiencing greater impact. The impact on an organization’s investigative sites is described by a respondent: 

“Many of our sites were partially closed, and we had to incur additional cost to sustain our study. We are maybe approximately 25% behind on enrollment as a result.”

Most R&D organizations that reported some level of COVID impact suggested that the ability of sites to respond to the pandemic was a key indicator of the level of impact. Given COVID-19 outbreaks occurred regionally in the U.S., differences in site response emerged based on location and time period. Respondents identified four key factors that tended to influence sites’ abilities to respond to the pandemic. First, staffing furloughs were often site-dependent, and often resulted in delays in data processing, as well as scheduling and execution of patient visits. Additionally, many patients felt hesitant to visit research centers, especially in harder-hit regions, with select organizations citing higher rates of patient withdrawal from studies. Sites with poor or non-compliant IT faced significant challenges pivoting to telehealth visits, providing access to EMR to investigators, or completing CRA site visits virtually. Finally, sites set different restrictions based on hospital- and state-imposed protocols (e.g., no lab-only visits, additional PPE needed, etc.) that resulted in potential deviations from protocol or delays.

Figure 1. 23 of 25 organizations reported some level of impact on ongoing trials due to COVID-19.

A couple of organizations (n = 2) reported experiencing small or no disruptions. These organizations were often running early stage trials (e.g., Phase I) where clinical follow-ups to identify adverse events could easily be performed over telehealth. An interviewee conducting studies in early phases noted that “there is a fairly large dichotomy between early phase and late phase—the impact for Phase I and Phase II is a little less.” Organizations with less disruptions also were often running trials in disease areas where patients required urgent treatment and/or had few options for treatment (e.g., oncology, rare disease).

Organization response: successes and failures

R&D organizations reported a spectrum of success in responding to the challenges presented by COVID. R&D organizations that self-reported “successful” responses to COVID-19 often demonstrated at least one of four key success factors: organizational agility, prior investment in remote technologies, development of COVID-19 task forces, and/or strong R&D capabilities in Asia-Pacific (APAC) regions.

Five organizations cited organizational agility as a key factor in the success of their COVID response. Smaller, flatter organizations were less hindered by bureaucracy, and were able to rapidly implement changes to studies.Decisions were made at a study-team level and upper management were informed of decision-making. In these cases, it was critical that leadership trusted their employees to execute trial changes without significant oversight.

Another 5 companies were piloting remote technologies and services prior to COVID and reported transitioning more easily to “virtual” trials.Several organizations were piloting telehealth, remote data monitoring, home health, and other virtual approaches in ongoing studies. Comfort with existing remote infrastructure allowed for rapid expansion into indications that did not initially leverage but could benefit from virtual approaches (e.g., measurement of skin lesions over telehealth in dermatology trials). The trend of shifting to a more remote model was explained by an interviewee:

“We were already in the process of things becoming more virtual anyway. This has definitely accelerated the process because we’ve had to adapt.

We are thinking more about how we collect data, verify data from a remote perspective.”

Establishment of a task force was instrumental for larger companies to prepare and rapidly amend trial designs to include remote modalities, with 5 of interviewed organizations reporting implementing such task forces. For larger organizations, the creation of response teams was critical to preparing for COVID’s impact. Organizations that successfully adapted trials to COVID-19 typically had upper leadership that trusted their task force to execute without significant oversight. In select cases, leadership indicated desire to press forward with additional changes to future trials, in order to continue building upon the trend toward virtual trials. The COVID-19 task force typically encompassed representatives from all key trial functions. Experienced team members with backgrounds in operations, regulatory, and data management were all required for implementation of novel remote technologies. Following general guidelines from the COVID-19 task force, study leads often were responsible for managing direct COVID-19 impact. Study leads needed to consider each investigative site separately, given variable COVID-19 activity across regions in the U.S. Task forces that took a study-by-study and site-by-site approach reported greater success than those that attempted a “uniform” approach. The importance of a relatively independent task force that could communicate with study teams and leadership was explained by two interviewees:

“There wasn’t a one-size-fits-all approach to our trials, given they were in different therapeutic areas and geographic regions. Thus, it was important to allow study teams to make recommendations that we could then adopt.”

“Communication with leadership was important to keep them informed of changes to our trials. They really just let us run with it. Now that we have a plan, leadership is pushing to make ‘the next step’ and make sure we COVID-proof our trials going forward. This includes leveraging more virtual approaches.”

A minority of organizations (~2) reported leveraging their organizations’ R&D capabilities in China and Japan, who had early COVID experience, to prepare ongoing studies in the U.S. Select global R&D programs indicated that their colleagues in China and Japan warned them about the impact COVID may have on trial execution, which allowed them to prepare for COVID and begin to develop protocols in case sites were shut down. Of note, this was a benefit that was not reported consistently by global R&D organizations and may be dependent on specific colleague-colleague relationships.

Adoption of remote monitoring technologies and decentralized approaches

Interviewed stakeholders implemented a wide range of novel remote monitoring technologies and decentralized approaches to make trials more patient-centric in the COVID era.

Figure 2. Telemedicine and eConsent were the most frequently utilized remote technologies, while home health was the most frequently utilized remote service.

Telemedicine and eConsent were the most frequently mentioned technologies adopted during COVID-19. Telemedicine was implemented by the majority of respondents, with 19 interviewed organizations reporting use of telehealth in ongoing trials. Telemedicine was most frequently used for routine follow-ups to identify adverse events, especially in Phase I studies. Telehealth was also used to measure select objective outcome measures (e.g., lesion size in dermatology or plaque psoriasis). Though investigators recognized the potential to collect other measures via telehealth (e.g., depression scales), they expressed concern that the method is not validated and requires proof of concept before regulators would accept data generated through telehealth. eConsent was used by 10 companies and was perceived to be one of the simplest innovations to implement. Respondents highlighted the potential for eConsent to facilitate communication with patients, by leveraging novel modalities such as video explanations, quizzes to assess understanding, etc. Interviewees offered their insights on remote technologies:

“I expect eConsent and telemedicine will become routine and integral parts of clinical trials. They are pretty straightforward steps towards a more patient-centric trial, and could easily be adopted in most, if not all, protocols.”

“We had piloted eConsent on a couple of studies and it (COVID) moved us out of the pilot very quickly. We’re trying telemedicine even more. How do we ‘hybridize’ a trial even outside of COVID to reduce patient burden?

Broader use of telemedicine may encourage enrollment and make trials more patient-centric.”

ePRO was the next most frequently utilized technology, with eight companies citing use. Notably, ePRO was already used in ongoing trials prior to COVID. However, in an effort to further reduce patient burden, research organizations opted to allow patients to use their own devices.

Use of data integration was perceived to be highly useful to R&D organizations, given R&D organizations could access trial data in real-time. Additionally, utilization of EMR provided opportunities for R&D organizations to supplement clinical research data, as potential alternatives to monitor adverse event and clinical care patterns. However, only select companies (n = 4) actually leveraged real-time integration of trial data or EMR during COVID. Several respondents cited challenges with implementation of integrated EMR, given poor investigative site IT infrastructure, investigative site preferences, or challenges with CRO staffing or capability to visit sites. Privacy restrictions, interoperability and compatibility challenges represent additional key barriers to utilization of these technologies. One interviewee discussed the barriers that have emerged in monitoring due to COVID:

“Monitoring is our biggest challenge right now and we’re trying to get back out to sites. Across the board we have major Phase III database locks and we cannot get to some of the final sites that have data. If we don’t have access to their EMR is there another way? We are discussing remote approaches and technologies that can be implemented.”

A number of respondents expressed interest in the future use of integrated EMR, and suggested they were actively seeking CRO partners to implement the technology once sites reopened. In addition to novel technologies, respondents adopted a range of approaches to decentralize trials and minimize patient exposure to investigative sites in the face of COVID-19. Overall, nearly half of respondents implemented at least one approach to decentralize ongoing trials.

Approximately seven organizations implemented some method of home health in their ongoing trials to ensure patients that were unable or unwilling to travel received IV medications and were able to provide blood samples. Rare disease trials reported greater use of home health, given the long distances patients may have to travel to access the investigative site and the need for frequent IV infusions. However, investigators also noted that home health was “hit-or-miss,” given some patients resided in locations inaccessible to home health while others refused to allow nurses in their home due to concerns with the spread of COVID-19. “Home care and direct-to-patient drug delivery are steps to move towards a more patient-centric trial design,” commented one interviewee. Direct-to-patient drug delivery was implemented by five companies and perceived to be a relatively low-risk method to transition trials to be more patient-centric, especially for oral agents with safer expected toxicity profiles.

Decentralized lab work was utilized by five organizations. Many sites did not allow “lab-only” visits during COVID-19, presenting challenges to collecting biomarker-related outcomes in oncology, cardiology, diabetes, etc. Investigators allowed participants to have blood work done at local labs, and in select cases provided co-pay cards to cover the cost of lab work (e.g., via Scout). Decentralized lab/blood work was perceived to be highly effective for oncology and rare disease patients, especially for those that had to travel longer distances to investigative sites.

Overall, remote technology adoption was more extensive than adoption of remote services. One might speculate that remote technology adoption has been more extensive not only because companies had prior experience with these technologies (e.g., piloting) but also because remote personnel and drug delivery require more lead time to set up, more extensive logistics and certification planning.

More than half of organizations interviewed (13 companies) cited leveraging FDA guidance at the start of COVID-19 to adapt and amend clinical trials. Interviewees cited published guidance on home-dosing and telemedicine as being especially helpful at the start of COVID. Interviewees also perceived the FDA to be responsive and accepting of the need for flexibility, so long as the integrity of trial data was preserved, and patient safety was ensured. Several interviewees spoke directly with the FDA and rated the experience as positive and informative.

However, interviewees also noted that regulators’ stances on how or if they would accept data that had been gathered in amended trials was unclear, and how the utilizations of remote technologies would impact filing. Specifically, several interviewees expressed concerns that select outcome measures may not be accepted without proof of concept (POC) demonstrating that collection via remote technology was the same as in a physician practice, given current FDA standards on how data should be measured.

Perspectives on long-term impact of COVID

Ultimately, 20 of the 25 interviewees believed that COVID-19 would permanently accelerate use of remote technologies in clinical trials. Most interviewees perceived increased use of remote technologies as a “silver lining” to COVID-19. Respondents often cited telehealth, e-consent, and EMR/data integration as the approaches that would most likely continue to persist past COVID. Additionally, respondents mentioned that they would aim to streamline the data collected from trial protocols, seeking to provide greater flexibility to investigators while removing “nice-to-have” outcome measures from protocols. Meanwhile, high upfront investment cost and unclear regulatory stance drove several interviewees to predict that trials would return to “normal” post-COVID-19. This perspective was especially clear for oncology and rare disease, where stakeholders believed in-person care was critical. Ultimately, most interviewees acknowledged that while use of remote technologies had been accelerated by COVID, use would not persist without concerted effort from industry and regulators to continue momentum. Change would focus on convenience for the patient as highlighted during an interview:

"We were forced to dip our toes in the water; we will not return to status quo. Patients are likely to expect some of these technologies now to make their lives easier.”

Challenges to adoption of remote technologies

Interviewed stakeholders identified several barriers to the adoption of remote technologies and services. Several interviewees (n = 5) suggested that preference for care at investigative sites would delay implementation of remote technologies. Patients, especially in rare diseases, may prefer to receive care at investigative sites from an in-person key opinion leader (KOL) rather than via telehealth. Others (n = 4) suggested site-specific protocols would hinder adoption. Select sites demonstrated resistance to adoption of new technologies or had specific IT protocols that were barriers to adoption of novel approaches (e.g., poor security delayed remote EMR access). Finally, 8 companies perceived regulators to pose a significant barrier to adoption. Interviewees believe there is low incentive to adopt new technology, given high early investment cost and unclear guidance from the FDA on how they will interpret these technologies during filing. Thus, a firm stance is needed from the FDA to promote utilization of remote technologies post-COVID.

Steps to continuing progression to “patient-centric” trials

Industry, regulators, and investigative sites will need to take several steps to cement the role of virtual approaches and promote patient-centricity in future clinical trials. A strong regulator stance on the use of virtual and remote technologies in clinical trials is necessary to promote adoption by industry. Currently, lack of clarity from regulators on how remotely-generated data will be accepted leads to hesitancy to adopt novel technologies. Published FDA guidance expressing standards for e-Consent, telehealth, and other tools or decentralized approaches may be necessary for organizations to invest in remote technologies. Conversely, POC may be required for regulators to publish such guidance, resulting in a dilemma for companies. 

Additionally, it is critical that all stakeholders including patient advocacy groups and investigative sites be on board in order to move decentralized models forward. Several interviewees perceived that select patients enroll in trials to get “better” care at academic centers. Patient perspectives must be incorporated into trial design in order to balance the patient-centric and cost-related benefits of remote technology with the preferences of patients. Additionally, investigative sites may not have the budget nor the expertise to implement novel remote technologies (e.g., EMR integration, telehealth, e-consent). Industry may need to support sites by investing in technologies and trainings to ensure standardization across investigative sites.

, Research Anlayst; all from Tufts CSDD; 

, MBA, is Deputy Director and Research Professor, 

, both based in Boston, MA. Email: kenneth.getz@tufts.edu 

Dorn, A. V. (2020). COVID-19 and readjusting clinical trials. 

(10250), 523-524. doi:10.1016/s0140-6736(20)31787-6

Medidata. (2020, August 18). COVID-19 and Clinical Trials: The Medidata Perspective. Retrieved from 

https://www.medidata.com/wp-content/uploads/2020/08/COVID19-Response8.0_Clinical-Trials_2020824_v1.pdf

United States of America, Food & Drug Administration. (2020). 

FDA Guidance on Conduct of Clinical Trials of Medical Products during COVID-19 Public Health Emergency

*Categories of R&D spending based on large companies (R&D >$1Billion); mid-sized (R&D $100 Million - $1 Billion); and small (R&D < $100 Million)

Findings from a Tufts study examining the effects of COVID-19 on clinical trials.

COVID-19 and Its Impact on the Future of Clinical Trial Execution

The world is anxiously watching and rooting for the global drug development enterprise in anticipation of COVID-19 treatments and vaccines. Pharmaceutical and biotechnology companies, in collaboration with regulatory and government agencies, have taken many steps to try to compress drug development timelines. To name but a few: they have rapidly mobilized scientific and operating teams; entered into an unprecedented number and variety of co-development arrangements; scaled-up manufacturing capabilities; and leveraged technology solutions and advanced analytics to access and analyze data from animal and early human studies and from commercially available therapies treating other diseases.

Regulatory agencies have issued emergency use authorizations and offered research sponsors guidance to assist preparedness, ensure recruitment continuity, and embrace more virtual and remote approaches to conducting clinical trials.

Of the 400+ COVID-19 treatments and vaccines now in R&D, a rapidly increasing number are in human testing with several promising therapies in late-stage clinical trials. The successful development of safe and effective COVID-19 therapies within a forecasted 12-to-24-month time horizon is truly groundbreaking. Indeed, our research at the Tufts Center for the Study of Drug Development (Tufts CSDD) shows that such an acceleration represents a 400% speed improvement: historically, experimental immunological and anti-infectives take approximately eight years to develop from first-in-human clinical trials to FDA approval. Late-stage Phase III vaccine trials alone usually take around three years.

Will R&D strategies and tactics designed to dramatically shorten timelines during the pandemic translate across development portfolios post-pandemic? This is a question that many sponsor companies are exploring and it couldn’t come at a better time.

The most recent Tufts CSDD benchmarks—gathered at the end of 2019 just prior to the pandemic—show that while mean regulatory review times for U.S.-approved drugs and biologics (referred to throughout this column as “drugs”) have declined by nearly two months during 2014 and 2018, average clinical phase times have increased by nearly seven months during the same period. Clinical phase times for drugs targeting rare diseases are one of the few positive news items coming out of our recent analysis: orphan drugs now take more than nine months less time, on average, to complete clinical trials and receive regulatory approval compared with non-orphan drugs.

The results of this recent study present useful baseline measures gathered just before the COVID-19 pandemic from which to assess post-pandemic strategies and tactics to accelerate development and approval timelines.

Tufts CSDD analyzed clinical development and regulatory approval phase times for all 377 new drugs and biologics approved by the FDA’s Center for Drug Evaluation and Research (CDER) during 2008 to 2018. Clinical trial durations and scope metrics were obtained from summaries of drug approvals available on the FDA website, from medical reviews, and from the ClinicalTrials.Gov website. Benchmarks were derived from several subgroup analyses, including by therapeutic area, whether an approved drug was originally with an orphan drug designation, and whether the drug was developed and approved by one of four expedited FDA review programs (e.g, accelerated approval, fast track, breakthrough designation, and priority review).

This study was funded in part by an unrestricted grant from the Biotechnology Innovation Organization (BIO).

The average cycle time for all clinical testing (clinical phase duration) from IND filing to FDA submission (shown in Figure 1 below), for drugs approved during 2014 -2018 is 89.8 months, an increase of 6.7 months, or 8.1%, compared to the earlier 2008-2013 time period. Mean regulatory phase duration—from submission to FDA approval—for all drugs approved between 2014 and 2018 was 11.7 months, a decrease of 1.9 months, or 14%, shorter than the earlier 2008-2013 timeframe. Average total time to bring a successful drug through clinical testing and into the market (i.e., from IND filing to regulatory approval) was 101.5 months—approximately eight-and-a-half years. This represents an increase of 4.8 months, or 5%, between the two time periods, 2008-2013 and 2014-2018.

Increases in the overall clinical phase duration are due to growth in cycle times for individual clinical trials in each phase of development. Substantial growth in protocol design complexity has been a major contributor to phase-specific cycle time increases. Phase I clinical trials have increased more than 7% in duration between the two time periods—from 13.8 months on average to 14.8 months. Phase II clinical trials saw the largest relative increase in duration from an average of 27.1 months to 30 months—or nearly 11%—between the 2008-2013 and 2014–2018 observation periods. And Phase III clinical trial durations have increased by more than 6% between the two time periods, from an average of 26.8 months in the 2008-2013 period to an average of 28.5 months per individual trial in the 2014-2018 timeframe.

Scope increases in clinical trials by phase is another factor driving cycle time increases. With the exception of Phase IV studies, the average number of participants and investigative sites per clinical trial, in all pre-approval phases, have increased.

The highest increases have been observed in Phase II: the mean number of participants, for example, increased 9.2%, from 152 in 2008-2013 to 166 in 2014–2018.The average number of participants in Phase I and III clinical trials increased 5%–7% between the two time periods.

The average number of participants in Phase IV clinical trials has declined sharply—by 26.1%—in part due to the growing proportion of orphan drug approvals now being marketed.

Very high variation was observed around the mean number of participants per clinical trial and this variation has increased over time.

In this most recent study, Tufts CSDD also observed wide variation in overall duration (clinical plus approval phase) by therapeutic area. Looking at the most active therapeutic areas—those with the largest number of drug approvals during the full 11-year (2008-2018) period—anti-infectives had the shortest average overall duration of 84 months. CNS had the longest average overall duration of about 108 months. The average clinical phase duration for anti-infectives was 17.6 months. This is 19.2% shorter than that of CNS treatments.

Oncology approvals between 2008 and 2018 averaged almost 95 months overall per drug—87.2 months in the clinical phase and 7.7 months in the regulatory phase. Some of the variation in review durations can be explained by differences in the proportion of drugs that received a priority review rating from FDA. Whereas 74% of drugs approved in oncology had a priority rating, only 43% of CNS drugs were designated for priority review.

Overall durations (clinical plus regulatory phases) for orphan drug approvals decreased from 109.3 months, on average, in 2008-2013 to 96.6 months in 2014-2018, an acceleration of more than a year of time.Mean clinical duration for orphan drug approvals during 2014-2018 was 5.3 months faster than for non-orphan drug approvals.The difference between orphan and non-orphan drug mean regulatory review durations has increased with orphan drugs now seeing a four-month speed advantage during the later 2014-2018 time period. Mean clinical duration for non-orphan drug approvals increased substantially (16.2 months) in the 2014–2018 period, whereas mean regulatory review duration for non-orphan drugs decreased by one month.

As expected, approvals under one of four expedited or facilitated regulatory pathways (FRPs) were considerably faster than those receiving non-expedited review. The overall duration for approvals of drugs under expedited review was 11% shorter, on average, than non-expedited review approvals during the full 11-year (2008–2018) observation period.

Compared to approvals under non-expedited review, the mean clinical phase duration for drugs approved under an FRP was four months shorter in the 2008-2013 time period (4.7%) and 4.2 months (4.5%) shorter in the 2014-2018 time period. The average regulatory review phase duration for expedited review drugs was approximately 45% faster than than for non-expedited drugs during both 2008-2013 and 2014–2018 time periods.

For approvals under expedited and non-expedited review, mean clinical phase durations increased by 7.4 months (9.1%) and 7.6 months (8.9%), respectively. Meanwhile, the mean regulatory review phase duration decreased by 0.3 months (3.1%) for expedited approvals and 0.7 months (4.0%) for non-expedited approvals.

Tufts CSDD also evaluated the impact of sponsor company size on development and regulatory approval speed. This subgroup was assessed in response to recent claims that smaller companies (i.e., organizations spending less than $1 billion on R&D annually) enjoy a speed advantage over the largest pharmaceutical and biotechnology companies. Looking at all approved drugs during 2008 and 2018, the claim is not supported. Approvals of drugs sponsored by the largest companies enjoy a nine-month average clinical duration advantage and a 1.2-month average regulatory review advantage.

We look forward to assessing the speed records broken by COVID-19 treatments and vaccines when they complete development. Contrasting them against our most recent pre-pandemic clinical and regulatory review phase durations will provide valuable insights into time-compression strategies and practices that can be applied across development portfolios long after the pandemic is behind us.

, MBA, is Deputy Director and Research Professor, 

, both based in Boston, MA. Email: kenneth.getz@tufts.edu

Study offers clues for installing rapid R&D tactics post-pandemic.

Applying Pre-Pandemic Benchmarks to Assess Future Speed Strategies

	Inadvertent and intentional dose non-adherence in clinical trials has been a major drug development challenge for decades. Studies in the literature have demonstrated that a high percentage of study volunteers-as high as 75% -conceal or fabricate information and provided inaccurate self-reports of adherence.

	Participant non-adherence reduces statistical power, and leads sponsors to underestimate the true dose response

 and toxicity from investigational treatments and to overestimate the true dose level required. The anticipation of study participant non-adherence compels sponsor companies to expend resources and personnel to recruit an inflated number of study volunteers and to unethically expose larger numbers of patients to an investigational treatment. A common inflation factor used by biostatisticians is 15%-20%; for a typical Phase III pivotal trial, an additional 120-150 patients are recruited to account for non-adherent study volunteer behavior. Partial and full non-adherence may also cause sponsor companies to miss detecting treatment effects when they do occur, resulting in failure to identify a potentially effective drug.

	Several well-publicized examples provide compelling evidence that non-adherence directly contributes to clinical trial, and even development program, failure. In a recent Phase II clinical trial of a treatment for adult attention deficit hyperactivity disorder (vortioxetine), the clinical team found that non-adherence compromised study validity. The team reported that high levels of non-adherence in the clinical trial lowered the signal effects of the investigational treatment and contributed to the failure to demonstrate differences in the primary and key secondary endpoints for placebo and active study drug cohorts. A similar conclusion was drawn from a recent Phase I clinical trial evaluating the effects of vilazidone on sexual function in healthy adults.

	A large Phase III pivotal trial conducted in the late 1980s on a cholesterol-lowering investigational drug failed to account properly for high non-adherence rates overall and at protocol-specific time intervals. As a result, the sponsor and FDA underestimated the dose response and overestimated the dose level causing patients to pay unnecessarily higher costs to receive the treatment.  

	Although there is strong and broad consensus that the accurate estimation of non-adherence rates in clinical trials is essential to determining the dose-response relationship and to allowing for a valid analysis of treatment safety and efficacy, accurate estimates have yet to be produced. Reported non-adherence rates in the literature are largely anecdotal, varying widely from 15% to as much as 90%.

	There is virtually no robust data characterizing patient motivation to be non-adherent. Some speculate that inadvertent and intentional dose non-adherence is associated with patient perceptions that their condition has improved and an investigational treatment is no longer needed; that the study drug is not working; or the study drug is causing intolerable side effects.  

	There are also no hard benchmark data characterizing non-adherence rates; this is largely a function of assessment and measurement approaches that are highly limited. Many approaches cannot distinguish between a patient simply missing a dose or deliberately concealing one. Pill counting-one of the most commonly used approaches-is unreliable and tends to underestimate non-adherence rates. Patient questionnaires and diaries rely on subjective assessments that can easily be misstated. Ingestible sensors are more precise but many study volunteers are uncomfortable with this approach.  

	Medication packaging with imbedded memory chips (e.g., MEMS) has serious limitations since this approach cannot detect what the patient is doing once the cap has been removed. And evaluations of drug levels in biologic fluids and biologic markers are impractical because they must be performed at distinct time periods when blood samples are collected and within a finite time frame before the study drug has been metabolized. These approaches are also subject to patient-specific pharmacokinetic variation.  

	The most reliable and accurate method is direct patient observation. But this approach has historically been cost-prohibitive and difficult to carry out in outpatient settings. In recent years, however, artificial intelligence (AI) has improved the feasibility of direct patient observation and presented a unique opportunity for the Tufts Center for the Study of Drug Development (Tufts CSDD) to conduct an academic study in late 2019 to assess the magnitude of intentional dose non-adherence and inform statistical analysis planning.

	Tufts CSDD conducted this study using an AI platform developed by AiCure. The platform relies on computer vision and machine learning technologies using a front-facing camera to capture dosing data followed by human review if deliberate non-adherence cannot be determined. Study volunteers use the AiCure software on their smartphone during all dosing administrations. In all, the adherence behaviors of 2,796 study volunteers and 257,672 doses from 23 recently conducted, IRB-approved clinical trials primarily for CNS and neuromuscular diseases were analyzed.  

	Of the total 257,672 doses observed, 4% were confirmed as intentionally non-adherent. Nearly half of all study volunteers (48%) had at least one intentionally non-adherent dose; Nearly one in 10 volunteers had more than 10% of their total doses deliberately non-adherent over the course of their clinical trial; And one-out-of-20 volunteers (5%) were intentionally non-adherent for more than one-third of their total doses required by the protocol. 

	A number of factors were associated with intentional dose non-adherence. As the duration of the clinical trial increased, the likelihood of intentional non-adherence increased significantly. Less-experienced, low-annual-clinical-trial-volume investigative sites had a significantly higher incidence of study participants who were intentionally dose non-adherent.

	Study volunteers whose first dose within the clinical trial was intentionally non-adherent had a significantly higher mean intentional non-adherence rate throughout the clinical trial. The mean intentional non-adherence rate was 26% for study volunteers whose first dose was intentionally non-adherent-nearly six times the mean for participants whose first dose in the clinical trial was adherent (4%). Study volunteers with at least one intentionally non-adherent dose in the first week of the clinical trial had a significantly higher rate of non-adherence throughout the trial.

	The results of this study offer insights into a number of potential new practices and policies. Raising awareness, training, and additional support focusing on patient adherence specifically for less-experienced, lower-volume investigative sites may help address the higher incidence of intentional non-adherence there.

	Implementing practices and solutions designed to detect and act on intentional non-adherence within the first week of a clinical trial will likely have a major impact. Remedial patient education, for example, and targeting non-adherent study volunteers at the outset of a clinical trial may help increase overall participant adherence. Clinical trial enrichment strategies designed to identify non-adherence during a placebo run-in period might be strengthened by including a prespecified threshold of intentional non-adherence as part of the eligibility criteria for randomization. 

	Tufts CSDD hopes to conduct future research that will look to gather adherence benchmarks across a larger and broader mix of disease conditions-particularly the most active, including oncology, infectious diseases, and endocrinology. This study also only included U.S. clinical trials. Future research will expand our scope to include international clinical trials to derive global baseline measures. 

	The historical lack of accurate measures of dose adherence behaviors has contributed to industry-wide inability to detect product non-use during clinical trials. The results of this study present a useful first baseline measure of intentional non-adherence, and inform practices and policies to address it. At the present time, as pharmaceutical and biotechnology companies face intense pressure to accurately measure dose response and true treatment effects, reducing and preventing intentional dose non-adherence takes on even greater importance.

 MBA, is Deputy Director and Research Professor, Tufts CSDD and Chairman of CISCRP, both based in Boston, MA. email: 

AI presents rare opportunity to assess the scope of this longtime challenge. 

Kenneth A. Getz

Articles

Enabling Digital Transformation: Managing External Clinical Data Sources to Advance Drug Development

COVID-19 and Its Impact on the Future of Clinical Trial Execution

Applying Pre-Pandemic Benchmarks to Assess Future Speed Strategies

Taking Stock of Intentional Dose Non-Adherence