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Identifying and providing clarity on the GCP quality and risk concerns associated with DCT modalities.
Clinical trials that incorporate decentralized components have graduated from experimental pilot projects to a mainstream priority for sponsors. According to ZS’s decentralized clinical trials (DCT) database, the number of industry sponsored DCTs jumped from a 15% compound annual growth rate in 2014-2020 to a 70% growth rate from 2020 to 2021. As the pharmaceutical industry increasingly focuses on scaling decentralized efforts and realizing the value of its investments, it will be critical to identify and address risks DCTs pose to good clinical practice (GCP) quality.
DCTs have moved GCP oversight outside of the investigative site and introduced new processes, technologies and stakeholders—like couriers, home healthcare providers and patients who collect their own data—who are now part of the overall clinical trial execution. Patients can now receive investigative medicinal products (IMP) at home, have procedures performed by home health services, capture data with wearable devices and connect with their physician via telemedicine. However, each of these modalities can raise new risks for GCP compliance. For example, for a patient receiving and self-administering IMPs at home, the product might not be stored in accordance with good manufacturing practice (GMP) if it is delivered late by a courier, stolen or delayed (see GCP Principle 2.12).
The considerable shift in the clinical landscape created by DCTs means sponsors need to reevaluate their practices and potentially implement new mitigation steps to continue to ensure the rights, safety, and well-being of trial subjects, as well as the integrity of clinical trial data.
ZS sought to identify and provide insights into the array of perspectives on GCP quality and risk concerns across common DCT modalities. We undertook a survey in collaboration with senior clinical quality representatives from four top-20 pharmaceutical sponsor organizations. The goal of the survey was to understand sponsors’ perceptions of risk types, magnitude and potential mitigations for nine DCT modalities. Risk magnitude was assessed on a 6-point scale (from “no risk” to “very high risk”) and then converted into a linear numeric scale (0-5) for analysis.
Survey responses from 15 clinical quality representatives representing 11 large and mid-sized pharmaceutical organizations, as well as 14 therapeutic areas (TAs), were collected from November 2021 through January 2022. The survey responses yielded three key insights.
As noted in Figure 1 below, more than 75% of the DCT modality and risk type combinations that sponsors were asked to assess rated between zero (no risk) and four (high risk). Even for combinations with the tightest consensus—like patient safety and welfare risk for local labs—sponsors’ responses still exhibited variability from zero (no risk) to two (low risk).
This lack of consensus among industry-leading professionals on GCP quality and risk for DCT modalities highlights the industry’s nascent understanding of this topic. There is a need for more industry-wide collaboration, education and research to improve collective understanding. Additionally, while our survey looked across all TAs, research to identify specific nuances of DCT quality and risk at the TA level will be important for application to specific trials.
While different DCT modalities pose different risk types and magnitudes, respondents said on average that DCT modalities posed a very low to medium risk (see Figure 2 below). Such results suggest no modality poses a high enough risk to prevent use in DCTs, but almost all modalities have at least one risk type necessitating closer consideration.
Direct data capture and on-demand technology support for patients carry the lowest overall risk of the nine DCT modalities (see Figure 3 below), respondents said. Their responses indicate these modalities could serve as gateways into decentralization for less experienced sponsors. However, it would be important to define and mitigate the specific risk to data integrity and credibility identified by sponsors in the survey.
On the other hand, telemedicine, virtual visits and home health services are viewed as carrying the highest risk of the nine DCT modalities—but respondents still consider them “low risk.” The breadth of contributing risk types suggests the need for a more expansive risk assessment and mitigation process than other modalities in order to deploy these foundational elements of decentralization.
While incorporating decentralized modalities will bring about new risks, sponsors can integrate these modalities into protocols without fear that doing so will pose a significant risk. Leading sponsors can differentiate themselves by not dismissing a DCT modality at the prospect of any new risk, but by objectively comparing the potential benefits to the risk magnitude and required mitigations. This includes considering the dynamic nature of how risks may compound when multiple DCT modalities are used in a single visit or trial.
As seen in Figure 2, survey results show significant differences in risk types and magnitude for different DCT modalities. For sponsors, this highlights the importance of avoiding a “one size fits all” approach for assessing quality and risk. Not every modality poses each risk type—some modalities have specific risk types on which sponsors should focus, while others raise multiple risk concerns.
For remote patient monitoring and direct data capture, data integrity and credibility are the main risk posed; for electronic informed consent and digital communication, patient rights and privacy are the primary concern. In both cases, risk types are closely associated with the primarymodality purpose. For example, electronic informed consent is designed to ensure patients understand their rights in the research study. It’s not designed to collect endpoint data for demonstrating efficacy to a regulatory body.
Modalities that involve clinical care or complex logistics are characterized by a broader set of risk concerns. For example, telemedicine and home health services each have four risk types with scores of at least two (low risk). For these instances, sponsors will need to consider and invest in broader mitigation approaches for multiple risk types.
Sponsors utilizing DCT modalities should evaluate and, as necessary, choose to track and mitigate additional risks created by DCT use. Effectively doing so will require investment in three areas: A DCT-specific risk assessment and categorization tool (RACT), analytics for risk signal detection and clinical stakeholder training.
Developing a DCT-specific RACT—which can identify, assess, monitor and mitigate risks that could affect data quality and patient safety—can serve as a starting point for sponsors to facilitate organizational discussion and consensus on DCT risks.Sponsors may also consider appointing a DCT risk management lead who can oversee the enterprise level DCT quality risk management plan.
There is a systematic three-step process that can enable organizations to make robust DCT risk assessments. The first step is to identify new potential risks associated with DCT modalities. Engaging with study teams who have experience deploying the modalities in practice can help inform this set of potential risks. The second step is to assess the likelihood, detectability and impact of risks to determine an overall risk score, which, alongside potential impact and cost, can be an input into an overall DCT modality implementation assessment. And the third step is todeploy mitigations for the highest-risk score categories.
The outputs of the DCT RACT process should be used to inform quality and risk management plan development for DCTs, while also determining which decentralized modalities can be lower-risk starting points for implementation. These risks should be communicated to study teams so they can include, track and mitigate the risks in their risk management plan.
Key risk indicators (KRIs), the clinical tools used to identify anomalous clinical data points by cross referencing and comparing study data across clinical trial dimensions, should be enhanced to also monitor potential risks associated with DCT-related modalities and vendors. This can mitigate the increased vendor oversight risk identified by survey respondents. In particular, statistical monitoring can be established to identify if there are statistically significant differences in clinical values across a range of home health providers, local labs or wearable technologies used in decentralization.
For example, a sponsor could establish KRIs for local labs used in a multi-country study. If one country’s average patient value for a specific trial data point was three times that of all other countries, this could suggest incorrect calibration of a local lab device or mismeasurement. This situation would warrant an investigation,appropriate corrective action by the sponsor and mitigation efforts to prevent future risks.
As the locus of control for a DCT moves outside of a conventional clinical site, it is key that sponsors provide training to a range of new stakeholders that are accountable for performing trial procedures and data collection—these include local imaging companies, couriers and home healthcare providers—to comply with GCP Principle 2.8, which states that each individual involved in conducting a trial should be qualified by education, training and experience to perform his or her respective task(s). For example, an IMP courier must be made aware of the IMP’s proper storing conditions, so that it is stored and delivered in accordance with GMP standards. Additionally, training previously given to investigators and site staff will need to be augmented to account for the incorporation of DCT modalities and their associated processes.
Furthermore, in some DCTs, patients may now be involved in completing procedures previously performed by site staff, like measuring their own heart rate at home using a wearable device. This could expand the originally intended scope of GCP 2.8 to include the patient, who is now playing a role in conducting the trial. Sponsors need to ensure these participants have adequate training to perform tasks.
As an industry, it is important to align on the set of risks uniquely posed by DCTs (while not associating these risks with those that would also be present in a traditional site-based trial). As sponsors across the industry invest in and evaluate DCTs, we will gain greater insight into the actual—rather than perceived—DCT risks. Some risks may be greater than anticipated, and there could even be new, unanticipated risks. Other risks may be lower than anticipated or easily mitigated. This appears to be the case with telemedicine, for which sponsor-stated risk decreases with experience (see Figure 4 below).
Sponsors should also be aware of opportunities for DCTs to reduce quality and risk concerns when compared to traditional trials. For example, using an electronic patient-reported outcome (PRO) could improve data quality and provide more control over who completes data and when, in comparison to a traditional paper PRO completed at home. However, methodologies to reduce one risk may introduce new risks such as technology integration or software glitches.
As DCT use increases, it will become increasingly critical to have a clear understanding of the associated impact and required mitigations from a GCP quality and risk perspective in order to inform decentralized trial design choices and effective implementation. This can be accomplished by investing in uniting a DCT “lens” with existing tools and processes, like RACTs and KRIs, and establishing clear feedback loops to learn from study teams in practice.
It’s vital these teams share newfound insights across the industry and work to consolidate learnings to form greater consensus on DCT risks and effective mitigation approaches. This will ultimately ensure that patient safety and data integrity remain at the core of DCT approaches.
Acknowledgements: We thank Grace Crawford, Patty Donnelly, Federico Feldstein and Kristel Van De Voorde for their advisory support and contribution to the study design.
Note: These survey results were discussed with GCP quality and risk industry leading representatives at a panel discussion at SCOPE 2022 and Virtual Clinical Trials Conference 2022.
Fan Gao, PhD, MS,is part of the ZS R&D excellence leadership team. She leads the digital and DCT vertical in clinical development.
Jonathan Rowe, PhD, MS, MA, has worked in the pharmaceutical industry across clinical, quality and corporate teams for over 25 years. He currently leads ZS’s clinical development quality, operations and risk management functions from the New York office.
Arnab Roy is a leader in ZS’s R&D excellence practice area and leads the development and global commercialization of ZS's DCT strategy and analytics solutions.
William Chaplin is a consultant for ZS's R&D excellence team and member of the digital and DCT vertical in clinical development.
Yuxiao Wang works within ZS’s clinical development quality, operations and risk management practice, and is a consultant at the Princeton, New Jersey, office.
Max Luccock works within ZS’s digital and DCT vertical in clinical development and medical affairs practices, and is an associate consultant at the Evanston, Illinois, office.