Emerging Trends in Clinical Trial Design
Explore the perspectives of clinicians executing hybrid and DCT models.
The COVID-19 pandemic rapidly accelerated the adoption of hybrid and decentralized clinical trial (DCT) models. However, as the world settles into its post-pandemic state and returns to pre-pandemic paradigms in many areas, the pharmaceutical industry remains dedicated to moving beyond traditional, centralized clinical trial constructs.
At the end of 2022, the PPD clinical research business of Thermo Fisher Scientific surveyed key leadership and staff on the frontline of clinical trial execution across the industry. The study’s objective was to supplement the findings of PPD surveys conducted in 2020 and 2021 that examined similar issues from the perspective of sponsor pharmaceutical companies.
Respondents shared their perspectives on the successes and challenges of DCTs and hybrid clinical trials. While there are improvements to be made, the advantages — including expanded data collection opportunities, artificial intelligence (AI)-enabled insights and efficiencies, patient-friendly trial structures for later clinical trial phases and additional disease states, and clinical trial cost reduction — are far outpacing the drawbacks. Ultimately, DCT and hybrid models are transforming modern clinical trial design.
Growing Patient Data Collection Opportunities
DCTs and hybrid clinical trials have introduced the opportunity for comprehensive, real-world data collection — a previously impossible state within the moment-in-time constructs of traditional trials.
Rather than capturing patient information only during an in-person visit, wearable and other remote devices collect increasingly sophisticated real-time data. The heart rate functionality of many smartwatches and fitness monitoring devices is familiar; however, rapid technological advancements enable more health data collection functionality, expanding the opportunities for additional trials to be fully decentralized or include decentralized elements.
For example, wearable electrocardiogram (ECG), continuous glucose monitoring, hydration and sweat sensing, and photoplethysmography (PPG) devices provide holistic insights into a patient’s physiology and response to the therapeutic being tested.
Of course, for almost any technology to be meaningfully adopted by the pharmaceutical industry, regulatory guidance is paramount. The U.S. Food and Drug Administration (FDA), European Medicines Agency (EMA) and other leading global regulatory bodies are investing considerable resources to facilitate the expansion and safety of digital technology devices.
For example, in March 2023, the EMA finalized guidelines for using digital technology to capture electronic data for clinical trials. Likewise, the FDA has prioritized creating a framework for digital health technologies and has released numerous draft and final guidances in this area.
Artificial Intelligence Is Supporting the Growth of Decentralized Clinical Trials
For DCT models to generate maximum value, clinicians must efficiently and effectively analyze the vast sums of real-world data collected via decentralized devices. Therefore, clinical trial designers are exploring the use of artificial intelligence (AI) technologies to create structured and standardized data from the growing number of inputs and sources.
AI tools can intelligently interpret data, feed downstream systems and auto-populate required reports and analyses. These tools can utilize existing systems to integrate the data flow, generate real-time insights from centralized and decentralized data sources, monitor patient adherence, encourage patient compliance, and help guide the study seamlessly and efficiently.
As with data collection, regulatory constructs are needed to guide the adoption and utilization of AI within clinical trial designs. Fortunately, leading global regulators have launched key initiatives. For example, the FDA’s Center for Drug Evaluation and Research (CDER), Center for Biologics Evaluation and Research (CBER), and Center for Devices and Radiological Health (CDRH) have collaborated to develop an initial discussion paper to facilitate communication with a range of stakeholders and will ultimately issue a series of guidance documents.
Decentralized Clinical Trial Models for More Clinical Trial Phases and Disease States
DCT models are being increasingly created for Phase II, III and IV trials. These phases require more patient participants and would benefit the most from a DCT model that makes studies more accessible and efficient among larger study populations. Phase I studies focus heavily on safety and efficacy and will likely be slower to decentralize because in-person monitoring at this early phase has distinct benefits.
While DCT and hybrid clinical trial designs are increasing across nearly all therapeutic areas, metabolic, central nervous system, genetic disorders and infectious diseases are the conditions leading DCT adoption. However, the accessibility of advanced technologies enables the expansion of DCT models to other disease states. For instance, affordable high-resolution web cameras facilitate an increasing number of decentralized clinical trials in dermatology.
DCT Models are Driving the Reduction of Key Costs
A recently released Tufts University study offers one of the first looks at the concrete return on investment (ROI) metrics of digital clinical trial model investment. The study revealed a 400% ROI for Phase II and a 1,200% ROI for Phase III trials. The cost savings come primarily from reduced cycle times, improved patient screening and enrollment, and fewer protocol amendments over the life of a trial.
Additionally, increasing patient convenience, comfort and accessibility support lower patient dropout rates and nonadherence challenges. High patient loss and nonadherence rates, common among traditional study designs, often require expanded patient populations to collect the necessary data, substantially adding to costs.
Partner with Us to Navigate the New Era of Clinical Trial Design
Among the many issues that make clinical research difficult is addressing diverse stakeholders’ ranging interests and challenges. Study sponsors need quality results as quickly as possible to maximize profit-generation opportunities in the market. Clinical investigators, physicians, payers, regulators and certainly patients have their interests, such as quality of care, cost reduction and convenience.
By supporting expanded and novel data collection opportunities, DCT and hybrid clinical models are transforming the fabric of clinical trial design. Using AI and other emerging technologies, real-world data can be efficiently standardized, interpreted and shared across the players in a study’s value chain to improve patient outcomes, maximize opportunities for patient participation and decrease study costs.
Thermo Fisher Scientific’s PPD clinical research business pioneers digital capabilities and deploys solutions to streamline protocols, putting patient experience at the center of every design. Recently recognized by ISG as a leader in the design and deployment of decentralized clinical trial solutions and patient engagement services, our teams can help you drive recruitment, engagement, retention and data quality.