Solutions Services Director, Remarque Systems

Kristin Mauri

One of the widespread notions about decentralized clinical trials (DCTs) is that Big Pharma has an advantage because DCTs require more bandwidth and greater financial resources than “traditional” trials. That may discourage small-to-midsize biopharmaceutical and biotechnology innovators, but it's not true.

While small-to-midsize pharmaceutical and biotech companies may feel they lack the expertise to conduct DCTs, and that these trials are too expensive, DCTs can be easier to recruit for. DCTs can also deliver long-term cost savings that may offset any additional up-front investment.

DCT savings result from efficiencies realized through telemedicine's time-saving and burden-reducing conveniences, electronic consent (eConsent), electronic patient-reported outcomes (ePRO), electronic clinical outcome assessments, and use of wearable/sensor technologies. DCTs can also enable the recruitment of a broader base of patients, thereby shortening enrollment timelines. Such advantages can reduce trial durations by one to three months, yielding further savings.

DCTs have become a vital component of the current research landscape more than a whim. Although now fixed in the future of clinical development, DCTs are still referred to variously as “remote,” “virtual,” “site-less,” “direct-to-patient,” or “hub-and-spoke” trials. DCTs have become increasingly common in the wake of the COVID-19 pandemic and are expected to gain market share in the post-COVID era.

DCTs have achieved buzzword status in the industry, generating confusion over what they are. The Association of Clinical Research Organizations (ARCO) defines a DCT as a trial that "brings the trial to the patient by utilizing local healthcare providers, optimizing digital health technologies. Enabling the patient's voice to accelerate medical product development, speed delivery of therapies to patients, and create efficiencies across clinical research processes."

The focus on patient-centricity, with its advantages of convenience and flexibility, makes DCTs especially appealing to patients, contributing to high rates of participant satisfaction. DCTs are also increasingly attractive to small-to-mid-size biopharma and biotech companies, though the perceived challenges of conducting these trials may make some sponsors apprehensive. However, in our experience, those perceptions are often driven by common misconceptions about the DCT model.

One misperception is that DCTs are 100 percent remote. In actuality, DCTs more typically adopt a hybrid model representing varying degrees of decentralization, with some activities conducted on-site and others happening remotely. For example, after the initial visit to the principal investigator's site, the remaining visits may be conducted via telehealth. That can be a compelling inducement for patients to participate, particularly those living with rare diseases and who live hundreds of miles from a trial site.

DCTs also offer geographic and population flexibility, enabling recruitment across regions, ethnicities, and socio-economic groups, often leading to faster enrollment, fewer screening failures, and greater retention. Moreover, with fewer sites, DCTs come with fewer review boards, potentially lower regulatory costs, and potentially less-complicated protocol amendments.

Nevertheless, DCTs require extensive planning and steps to ensure patient safety, regulatory adherence, and clean, robust data production. Therefore, small-to-midsize companies entering the DCT space need to consider the factors cited in the following sections.

Trial design, budget, and vendor management

Regardless of company size, mastering trial design, budget, and vendor selection for DCTs is critical. To a great extent, these three factors are inter-related in that the first factor can influence the other two. Unlike in a traditional trial, in which investigators enter data from patients who visit the site, participants in a DCT may enter data themselves or use remote data-gathering devices. The DCT sponsor must therefore use trial design to determine how to integrate, manage, and analyze the data and which technologies and personnel to deploy for these tasks.

Managing vendors can be a considerable hurdle for small and midsize sponsors, particularly when integrating discrete data collection, analysis, and reporting methods. That's because collecting data across multiple third-party systems can make the process exceedingly complicated—

 expensive. Yet even as costs pile up for staffing, partnerships, shipping, call centers, and visiting nurses, small-to-midsize sponsors need to remember that DCTs can deliver cost savings and should account for those savings in their budget calculations.

Remote data collection in a DCT raises a number of questions regarding the format in which the data are collected and delivered and how to secure the data from the vendor. Partnering with a technology vendor that employs a "source-agnostic" data management system can answer those questions, making a DCT more cost-effective. Such a system can readily aggregate data from multiple data feeds, including the remote, wearable, and leave-behind technologies that can facilitate provision of eConsent, direct-to-patient supply of investigational product (IP), investigator-participant interactions, home health visits, collection of ePRO, and connectivity in general.

 Used with permission, ACRO DCT Data Flow Map

Working within a single operating system across many platforms can still seem challenging to small-to-mid-size innovators who lack the large staffing resources of Big Pharma. However, the solution isn't necessarily to hire a big CRO with a large staff and related bureaucracies. Instead, sponsors should identify a technology partner with a centralized operating platform that aggregates all the data and facilitates risk management, incorporating the kind of automation on which DCTs thrive.

Incomplete or less-than-robust data can doom even the best-designed trial. The risks of suboptimal data may feel heightened in a DCT, in which data are largely patient-reported or uploaded from a wearable or other remote device, raising concerns that patients may overlook their reporting requirements or that a technology glitch may create a device malfunction.

Anticipating and minimizing such risks is especially important for small-to-midsize sponsors. Unlike their large pharma competitors, they often rely on reaching key milestones to satisfy investors and maintain adequate funding for continued operations. A key risk-mitigation strategy is to use a platform with dashboards that can instantly identify patterns of missing or anomalous data. The ability to view reports and status on dashes that continually centralize, analyze, and track data and risks in real time can level the playing field. The sponsor's size and resources become far less of a factor in a DCT’s success. Therefore, small-to-midsize sponsors should perform due diligence to find a tech provider that can deliver a comprehensive data view in a consistent format regardless of source and across multiple platforms.

For sponsors, one of the most appealing features of DCTs is their use of high-touch technology to automate data collection, interpretation, and reporting processes. That is especially important for maintaining centralized safety and efficacy databases, which are essential for monitoring and assessing the status of a DCT. Bringing all the data into one central location helps the sponsor see the complete picture of a trial cost-effectively. Centralization also places the data within easy reach of investigators and other DCT stakeholders, particularly patients; accessibility is at the heart of DCTs' patient-centric promise and appeal.

One of the drawbacks of traditional clinical trials is that they employ time- and labor-intensive analytical processes to detect data inconsistencies or worrisome patterns. In a DCT, an integrated data management system alleviates that drawback by providing workflow oversight and insights. Today, machine-learning algorithms perform those processes in real time—with greater speed, accuracy, and consistency. Moreover, DCT sponsors can establish pre-determined alerts that signal data anomalies requiring a closer look. An integrated, algorithm-driven system can thus address potential risk factors by significantly strengthening data certainty.

For some sponsors and trial personnel, DCTs call for new and different skill sets than those required for traditional clinical trials; they also require the deployment and management of new technologies. Large pharma companies would seem to have the edge here in that they were already heavily investing in data management processes and systems well before the advent of DCTs.

In reality, small and midsize sponsors can seize a significant logistical advantage. Unburdened by established (and potentially outdated) data management processes and systems, these companies can adopt new source-agnostic systems, amalgamate best practices, and coordinate a roster of best-in-class vendors to optimize DCT results.

Moreover, the freedom to select and customize various technologies according to their individual needs allows small-to-midsize companies to minimize staffing requirements and streamline workflows while tracking safety, clinical, and operational risks in real time. Such benefits can also facilitate the protection of patient safety, regulatory compliance, and delivery of clean, conclusive data—thus assuaging any concerns about navigating the evolving DCT landscape.

, Solutions Services Director, Remarque Systems

QbD Manual for Decentralized Clinical Trials: The Quick Reference Guide. Association of Clinical Research Organizations (ARCO). 2021. Available at: 

ACRO Decentralized Clinical Trials Complete Map. ACRO Decentralized Clinical Trials Working Party, Association of Clinical Research Organizations (ACRO). 2021. Available at: 

Articles

DCTs can be a great tool for small-to-midsize pharma and biotech companies despite misconceptions of them being advantageous for only Big Pharma.

Unraveling DCT Misconceptions

Exciting new clinical trial technologies promise greater protection for data integrity, patients, and even the broader public health, while streamlining trials and reducing costs. Industry guidelines not only encourage but demand their use. Yet researchers still may not be taking full advantage of these advances. At issue: lack of clarity in ICH E6 (R2), the current version of the International Council for Harmonization (ICH)'s "Guidelines for Good Clinical Practice." Fortunately, spurred by member organizations, ICH is undertaking a full rewrite of the guidelines to be adopted across all regulatory agencies. The new guidance is anticipated in 2021.

ICH E6(R2) included a focus on a proportionate, risk-based approach to the design and conduct of clinical trials. ICH E6(R3) will further spotlight risk-based monitoring (RBM) and remove barriers sponsors may have encountered in adapting this approach. This should have many downstream advantages:

Reducing the burden on patients and investigative sites, while increasing participation and enrollment in clinical trials

Helping sponsors and contract research organizations (CROs) manage costs, while accelerating regulatory approval of new drug candidates

Enabling sponsors to adopt a quality management strategy that embraces technology and leverages access to real-time information to drive a more structured approach to risk

Since ICH E6(R2), clinical trials have continued to evolve. This new guidance will help ensure that sponsors can harness the new designs and advances made possible by technological innovations.

Some history: ICH was designed to make new drug development and registration more efficient

Three decades ago, as electronic records became increasingly common in the clinical trial landscape, an issue arose. Without common standards, the format of an electronic document could vary dramatically across regions. The need to harmonize became glaringly obvious, and an international committee went to work.

The ICH GCP, first published in 1996, set international standards for both ethical and scientific quality; they addressed the design, conduct, recording, and reporting of clinical trials. As a result, industry-wide consistency led to well-structured drug-marketing applications, a rapid and efficient regulatory review process, and a higher likelihood of first-cycle approval.
However, the clinical trial landscape has fundamentally changed since 1996. Technology has given us wide-ranging new ways to collect, store, and analyze data. New trial designs—adaptive trials among them—have new ways of using data. Clinical trials themselves have become increasingly complex—and investment-intensive. And global operations require progressively greater division of tasks across multiple functional teams, organizations, and locations. This all conspires to put tremendous pressure on sponsors, particularly resource-constrained small biotech and specialty pharma companies.

A 2016 addendum to ICH E6 provided some extra guidance, including updated standards for electronic medical records, but did not change the existing text. As the clinical trial landscape has continued to advance, this has proven problematic. Consider the rise of adaptive trials, for instance.

Adaptive clinical trials: one innovation driving change in ICH E6

Designing a clinical trial entails mastering uncertainty: What is the best measure of benefit? What factors determine optimal dose or duration? Which inclusion and exclusion criteria target the right patient population? These questions are particularly pressing when researchers are forced to define all key parameters at the trial start and hold them constant throughout execution, as is the case in traditional trial design.

Of course, once the trial actually begins, information accumulates and the answers to the questions become clear. Adaptive clinical trials are designed to take advantage of this input, by allowing researchers to modify trial parameters in real time according to prespecified rules.

Not surprisingly, the data needs of an adaptive trial are quite different from the needs of a traditional approach. Traditional trials—and traditional source data verification—focus on ensuring every data measurement has been taken, and each piece of data is validated.

Conversely, adaptive trials require the ongoing, frequent availability of interim data—snapshots that can be accessed and analyzed rapidly. In the short term, errors and missing data are less critical; they can be updated later.

In addressing quality management, ICH E6 (R2) states that the sponsor should ensure all aspects of the trial are operationally feasible and should avoid unnecessary complexity, procedures, and data collection. Yet, the guidelines fail to consider the type of trial and the type of analysis within each trial. They have been over-interpreted, with the result that adaptive clinical trials suffer from multiple layers of well-intentioned additional “GCP-motivated” processes. This extra overhead stifles research in resource-limited settings and makes innovative trials substantially more difficult to implement.

Researchers ask that ICH E6(R3) be structured to actively guard against this unintended consequence, to avoid harm to patients and to free clinical research to take advantage of opportunities to move quickly and precisely.

The evolving landscape creates an industry-wide call for change

Many forces have conspired to create tectonic shifts in the ways data are collected and trials are designed and managed. Technology lies at the root of many of these changes. mHealth allows for remote patient assessments, increasing both safety and convenience. Evidence can now be aggregated from multiple digital sources, including real-world data, patient-generated data, and electronic health records. Specialized platforms, driven by machine learning and artificial intelligence, can parse the collected data to instantly discover patterns and pinpoint anomalies that would be virtually impossible to detect unaided. This ability underpins the risk-based monitoring that identifies fraud and patient safety concerns. It also streamlines and accelerates every part of clinical development—from analyzing the DNA of a disease to site selection to patient inclusion and exclusion criteria—all of which frees the very form clinical trials take. Now researchers can effectively run decentralized trials, adaptive trials, multi-modality platform trials, and biomarker and IND-exempt trials, speeding the development of critical new therapies.

Of course, the technology that underpins all this innovation itself continues to evolve, creating new and enhanced study roles and responsibilities—central monitor, data scientist, and medical monitor—and new terms and concepts, including data driven approaches and topdown data review and quality tolerance limits.

Clearly, along with the push to maximize efficiencies comes the need to update the governing protocols—and to agree on the definitions and functions of each new development. The guidelines themselves must also remain flexible, to support a longer planning horizon as clinical trials continue to shift.

By focusing the ICH on strategy, the guidelines could not only ask “Where are the existing gaps?” but also “What are the emerging needs and opportunities?”

The same worries and desires echo across the industry

Stakeholders gathered virtually in June 2020 at the Clinical Trials Transformation Initiative to share their ICH E6 (R2) concerns and their hopes for the new revision.

Deborah Driscoll, Vice President Quality Assurance, Merck Research Labs, spoke on behalf of sponsors, who want to maintain the intent and spirit of GCP, while building a foundation for modern clinical research that harnesses innovative trial designs, permits studies to be conducted in an agile and flexible way, leverages new data sources—and is enabled with modern technology.

Others concurred. Dr. Carol Légaré of Health Canada began by praising ICH E6 for allowing a single set of clinical data to be recognized across jurisdictions, minimizing unnecessary duplication of trials. Yet, she said there have been issues with overinterpretation. Researchers still feel obliged to focus on less important aspects of a trial—such as the completeness and accuracy of every piece of data—often at the expense of such truly critical aspects as carefully managing risks to the integrity of key outcome data. In sum: Health Canada is looking for more clarity around the flexible options for trial design and data sources and the use of remote technologies.

Carla Brishesi of Brazil’s ANVISA (Agência Nacional de Vigilância Sanitária) noted that COVID has illuminated existing trends; for instance, there has been an uptick in remote source data verification of efficacy endpoints and safety data. This requires remote access to patients’ electronic health records and for other trial documents to be uploaded into the cloud. New ICH guidance must consider these movements.

Taking a global perspective, Celia Whitten, MD, PhD, Deputy Center Director of the CBER/FDA praised ICH E6 as an important global standard as evidenced during the pandemic. With clinical trials increasingly being multinational, she feels international standards for GCP can help facilitate clinical development and ensure the integrity of the results. She, too, pointed at some key areas for review and revision:

Ability to leverage existing infrastructure

Identification and prioritization of critical processes

Employment of all available technological tools and innovations to ensure trial integrity

With each panelist building on and amplifying connected themes, the trajectory was set for ICH revisions.

ICH guidelines are developed through a science-based, consensus-driven, five-step process involving both regulators and members of industry. The working group approves a draft document, which is then approved by the ICH assembly and international regulators. The document then goes to the public for review before the ICH adopts the guidance, and each region implements it.

ICH E6 (R3) will be developed and implemented in two parts:

Annex 1, anticipated in the fall of 2021, will focus on interventional clinical trials, considering the guiding principles as they relate to the use of unapproved or approved drugs in a controlled setting with prospective allocation of treatment to participants and collection of trial data.

Annex 2, due 12-18 months later, will focus on non-traditional interventional clinical trials, considering the guiding principles as they relate to the use of nontraditional clinical trial designs such as pragmatic clinical trials and decentralized clinical trials, as well as those trials that incorporate real-world data sources. It also will delve into novel areas not considered in previous versions and will address a broader approach to clinical trials incorporating the potential impact of technology.

This expected guidance on incorporating technology is likely to be of significant interest to sponsors, as they continue to incorporate new advances.

Anticipated clarification of the role of RBM

The industry is interested in the numerous advantages technology can deliver, with risk-based quality management and its corollary, risk-based monitoring, at the fore. The Clinical Trials Transformation Initiative, a multi-stakeholder public-private partnership co-founded by Duke University and the FDA, conducted a survey probing the areas of ICH E6 (R2) that do and do not need updating. A group of 327 researchers working across 153 countries rated the most pressing need as: “implementing systems that assure quality.” Moreover, 45 percent of those surveyed listed monitoring as a prime focus. The reason can be traced to the 2016 update of ICH E6, RBM became an industry requirement rather than simply a recommendation.

RBM leverages new technologies to assure quality through a comprehensive approach to operational, safety, and quality risks. It uses:

RBM succeeds through a combination of monitoring strategies including a greater reliance on centralized monitoring and statistical assessments to guide site monitoring visits, and a focus on advanced technical capabilities. Using RBM, clinical trial operations and technologies are designed to bring together the relevant metrics and data necessary to increase efficiency, patient safety, and data quality based on data-driven decisions.

Although ICH E6 (R2) encourages greater efficiency in monitoring, reporting, and archiving, it does not specify how this is to happen. At best, researchers have responded by engaging in much additional paperwork; at worst, they have simply not adopted new methods. The industry now looks to ICH E6 (R3) for clarification.

ICH E6 (R3): Flexible guidelines that can adapt to progress

Researchers have found that technology can enhance, speed, and simplify monitoring. Further, these fresh approaches to monitoring support new trial designs, such as adaptive trials, described above. As technology allows trials to become more geographically dispersed (and the COVID-19 pandemic further encourages working from a distance), thorough and accurate remote monitoring is both completely feasible, given new technologies, and increasingly attractive.

These will not be the final changes to clinical trials. Science, technology, and ingenuity will continue to refine methods and advance possibilities. ICH E6 (R3) must be flexible enough to adapt to designs and technologies that have not yet been conceived. It will succeed by focusing more broadly on principles and objectives, including further advancing the concept of risk-based approaches.

Researchers aren't taking full advantage of advances in trial technologies. A lack of clarity in the current version of the International Council for Harmonization "Guidelines for Good Clinical Practice", could be the problem. 

Kristin Mauri

Articles

Unraveling DCT Misconceptions

Clinical Trial Evolution: The Drive to Update ICH E6