Clinical Researcher—June 2022 (Volume 36, Issue 3)

GOOD MANAGEMENT PRACTICE

Kristin Mauri

Decentralized clinical trials (DCTs) are no fad; they are entrenched in the future of clinical development. That is good news for patients with obvious advantages, but the trend may seem daunting for small-to-midsized biotechnology and biopharmaceutical companies.

Indeed, smaller companies may not even consider DCTs, feeling that the risks of treading this new territory—an area with complex data management demands and no clear regulatory guidance—outweigh the potential rewards. Many small and midsized biotech and biopharma companies feel that DCTs are outside their comfort zone and budgets. That is not necessarily true.

Fully Remote? Hybrid? What Exactly is a DCT, and Why Go to All That Trouble?

DCTs are known by many other names: virtual, remote, direct-to-patient, siteless, hub-and-spoke. Far from being 100% remote, they are typically hybrid (onsite/offsite), with a sliding scale of decentralization. Even when conducted by big pharma, DCTs combine onsite therapeutic interventions and testing with remote activities.

A DCT may involve a centralized site, with which patients engage via televisits augmented by remote monitoring. They may use local healthcare providers or optimized digital technologies. For a small or midsized company, a DCT could be as simple as adding an electronic patient-reported outcome (ePRO) system to a study that wouldn’t have previously included that.

The driver for a DCT is that it is inherently more patient-centric. By bringing the study to the patient, patients are more incentivized to participate; it is, quite simply, easier for them. That helps enrollment overall and helps sponsors increase trial diversity by making participation easier for people across geographies, ethnicities, and socioeconomic groups. So, based on recruitment alone, sponsors are wise to make some degree of DCT part of the solution. How much of the solution—determining where on the decentralized spectrum a trial should fall—is driven by factors such as the indication and product, the geography and trial population, and the study phase.

Myth-Busting: DCTs are Expensive, Giving Big Pharma a Natural Advantage

Because DCTs require more bandwidth, additional third-party vendors, and greater financial resources, many people believe that deep pockets give big pharma a built-in advantage in conducting them. The truth is different: DCTs have the potential to enjoy easier recruitment and provide long-term cost savings that may outpace the upfront investment required. Those savings come across the board, starting with the efficiencies gained by reducing the need for face-to-face interactions with patients; for example, the ability to conduct electronic informed consent with patients is an enormous time-saver. DCTs can also reduce trial times by one to three months, delivering substantial savings.

Further, there are additional cost reductions: Untethered from sites, DCTs can recruit from anywhere, which often leads to faster enrollment and fewer screening failures. That same geographic freedom also means fewer sites, thus fewer review boards, potentially lower regulatory costs, and greater flexibility around protocol amendments.

Coloring Inside the Lines: What Do the Regulatory Agencies Say?

Although aspects of DCTs have been gaining traction for years, their popularity exploded during the pandemic. To keep research moving ahead, regulators supported sponsors in pivoting to these new trial models—but they failed to issue any specific guidance concerning the differences in data collection, monitoring, and analysis.

In December 2021, the U.S. Food and Drug Administration issued draft guidance on “Digital Health Technologies (DHTs) for Remote Data Acquisition in Clinical Investigations.” It addresses:

Selection of DHTs that are suitable for use in the clinical investigation
Verification and validation of DHTs for use in the clinical investigation
Use of DHTs to collect data for trial endpoints
Identification of risks associated with the use of DHTs during the clinical investigation
Management of risks related to the use of DHTs in clinical investigations

Yet much of this guidance is related to the actual devices rather than the data the devices collect. In parallel, the International Council for Harmonization (ICH) is rewriting its guidance describing the responsibilities of all participants in conducting clinical trials. ICH E6(R3) Annex 2 will focus on nontraditional interventional clinical trials, such as DCTs. It is anticipated in the summer of 2023.

Without explicit guidance, some sponsors—especially small or midsized developers with no previous experience in DCTs—may feel they are taking a risk. Fortunately, the right technology, backed by a robust risk management strategy, can help effectively mitigate that risk.

From Traditional Trial to DCT: What Can You Adopt? What Needs to Change?

What are the key considerations for small-to-midsized biotech and biopharma companies that want to migrate to the fast-evolving DCT model? Four main areas bear examination: Master trial design, protocols and processes, budget, and vendor selection. To an extent, they are all interrelated.

In a traditional trial, patients go to investigators at trial sites; those investigators enter the data into electronic data capture (EDC) systems. In a DCT, the patients may be entering the data or using data-gathering devices themselves, leaving the sponsors to figure out how to integrate, manage, and analyze the data. What technology will be used, guided by what processes, and at what cost?

First, sponsors should define what their model will look like; then, they can understand what they will need to run it. For example, will some data be gathered at trial sites or the local hospitals? How often? Will patients use remote monitoring devices and ePro devices? Or will the trial combine multiple forms of data collection? How broadly dispersed is the trial? Will visiting nurses or other non-trial healthcare providers have a role?

Armed with the answers to these questions, sponsors can determine whether their current standard operating procedures align with this new model and, if updates are required, who will decide them. One useful method to keep track of the answers is creating a data map with the protocol development. The map outlines how the data are collected, the mechanism for collection, how it relates to other data, and how data will be monitored and cleaned.

Staffing comes into play, as well. Big pharma has ample people to manage various data sources and vendors. Small-to-midsized sponsors, by definition, lack those large staffing resources—but the solution isn’t necessarily to hire a contract research organization (CRO) with a large staff and complex bureaucracies. Instead, these sponsors must identify a flexible and agile CRO whose remote technology patient engagement strategies incorporate the kind of automation on which DCTs thrive.

Logistics also play a role. In a traditional study, a therapeutic is delivered to a site’s pharmacy, and that pharmacy distributes it to the patients when they come in for their visits. A research coordinator counts the remaining pills on a return visit to assess compliance. In a DCT, the drug can be delivered directly to the patient. Who will provide patient pharmacy support? How can the sponsor be sure the drug is delivered on time? How will they track compliance? Similarly, if a visiting nurse must be deployed to perform a test, how can a sponsor coordinate that data collection across geographies?

Budgetary questions weave throughout these issues—those tied to staffing costs, partnerships, shipping, call centers, and visiting nurses, for example. Yet, in considering the budget, small-to-midsized sponsors should remember the analogous cost savings that DCTs can deliver and include those in their overall budget calculations.

Finally, each of these factors—protocols, staffing, logistics, and budget—affect vendor selection. For many small-to-midsized innovators, this may feel like the biggest hurdle in adopting a DCT.

Vendor Selection: Which Technology Will Gather the Right Data?

Collecting data across multiple third-party systems can make DCTs exceedingly complicated—and potentially expensive. Sponsors need to understand what format the data are being collected in and delivered, and how they will get the data from the vendor; when multiple systems are in play, complexity increases—especially if vendors insist that all the data remain in their system.

Smaller sponsors grappling with this—especially for the first time—may want to invest in a consultant to help them sort through all their options. They may also want to consider a system-agnostic platform that aggregates data from any source, enabling them to work within a single operating system. This frees them to choose the optimal vendor for each aspect of the study while knowing that their data will always be centralized, with dashboards, workflows, and analytics that allow the sponsors to keep track of every aspect of the study. Such a platform offers seamless oversight that helps make even those sponsors new to DCTs feel confident in their ability to manage risk.

Dashboards, Triggers, and Workflows: Managing the Risk Around Uncertainty

In any trial, the bottom line is data. Are they complete? Are they clean? Are they high quality? Will they unequivocally prove the product’s safety and efficacy?

Risks associated with less than stellar data feel heightened when conducting a DCT. With data being patient-reported or uploaded from a wearable or other remote device, there is a constant concern that the patient may forget a report or the device may fail.

Again, small-to-midsized companies can mitigate such risks by using a platform with dashboards that offer real-time visibility into missing data. This ability to look at reports and status on dashboards that centralize, analyze, and track data and risks in real time and on an ongoing basis means that the sponsor’s size and resources become far less of a factor in the success of any DCT.

Navigating workflows in an integrated data management system eliminates the previous need in traditional clinical trials for slow, labor-intensive information analysis to detect key trends. Now machine-learning algorithms do that instantly—far better, far faster, far more accurately. Further, sponsors can establish triggers or workflows that show when data are out of range, when there’s an anomaly and when something doesn’t seem right. All this helps manage risk around uncertainty by significantly minimizing the uncertainty itself.

Centralizing the Data Mitigates the Risk of Decentralized Trials

Making the switch from a traditional trial to a DCT can be daunting. The processes need to change, people need different skill sets, and a host of new technologies are necessary. For small and midsized sponsors, particularly when integrating discrete data collection, analysis, and reporting methods, the prospect of managing vendors can be a considerable hurdle. Conversely, big pharma companies tend to power through these issues, finding ways to harness legacy data management systems that they invested in before DCTs exploded onto the scene.

The truth: Small and midsized sponsors have an advantage. They can leverage new datasource-agnostic management systems that integrate best practices while mixing and matching best-of-class vendors to optimize DCT results. That frees them to choose their technology at will, enabling them to minimize staffing, confirm logistics, and manage workflows. Critically, it also enables them to track safety risk, clinical risk, and operational risk in real time. That raises a sponsor’s confidence in its ability to deliver patient safety, regulatory adherence, and clean data—thus minimizing the dangers that may be of greatest concern when first embarking on DCTs.

Such high-touch technology that automates data collection, interpretation, and reporting processes is much of what makes DCTs so attractive—not just because the data become more accessible, but because that accessibility is at the heart of DCTs’ patient-centric allure.