How the Ongoing Digital Revolution Is Transforming Clinical Trial Design
Better processing and data management are needed to further innovate digital clinical trial design. Learn how.
The increased adoption of decentralized clinical trial (DCT) designs has revolutionized how the industry approaches clinical studies. When the design and execution of DCT and hybrid models initially gathered momentum, much of the focus was appropriately on the advantages of patient convenience and accessibility, as well as increased data collection opportunities. For example, decentralized and hybrid clinical trials introduced the ability to collect data using wearable and other remote devices, which was not possible given the moment-in-time nature of traditional trials.
Although new technologies will continue to advance data collection capabilities, clinical trial design innovation is often directed toward better processing, managing and creating value from the vast data companies already have stored. In short, for clinical trial design to truly benefit from the digital revolution, data flow, management and analysis are the next frontiers. Incredible work is happening in this area, including harmonizing clinical trial data, enhancing data quality and reliability and exploring the power of digital twins.
Harmonized Clinical Trial Data Structures to Drive Innovation within Decentralized Clinical Trial Models
Data is the fuel of the digital enterprise — it is the lifeblood needed to execute key processes, make decisions and achieve regulatory compliance. However, each stakeholder organization uses multiple technology solutions with distinct data standards and models. Within large companies, disparate technology platforms and data structures often coexist within the same organization.
Consequently, data cannot flow seamlessly throughout the clinical trial value chain. Instead, custom integrations that are expensive, time-consuming and prone to breaking must be built to move data between parties.
Because DCT data is vital for efficiency, accuracy and innovation, numerous life science organizations have teamed up with technology leaders like Microsoft and Accenture to develop and implement solutions to increase clinical trial efficiency and effectiveness.
Initiatives like the Digital Data Flow (DDF) initiative, a vendor-agnostic program creating a repository for standards-based study definitions and data structures, aim to facilitate better and faster end-to-end data sharing across clinical trials.
Increasing DCT Data Quality and Reliability
Arguably, one of the most valuable assets of DCT models is the tremendous amount of real-world data collected by wearable technologies and other devices. However, many of these new data collection methods are in their infancy, and uncertainty exists over which methods produce the most reliable results and how best to compare data collected in different manners.
Additionally, built-in biases and shortcomings of newer technologies can affect data quality and reliability. For example, some pulse oximeters are less accurate on darker skin tones. Therefore, regulators and the industry must agree on accepted methods for analyzing data collected from wearable devices.
Accessing structured, clean data is also a significant challenge because robust data-cleansing processes are typically required before clinical trial data can be used. Data structure differences exist even among organizations within the same company, let alone different companies.
Yet another data quality challenge is that patient information data is frequently fragmented across multiple systems. Creating data flows across secure environments is a significant challenge, given inconsistent data structures, and not all clinical trial value chain stakeholders have migrated to full digital recordkeeping. Clearly, if real-world and other valuable data reside in discrete or paper constructs, they cannot effectively contribute to digital clinical trials.
Fortunately, the U.S. Food and Drug Administration (FDA), European Medicines Agency (EMA), Medicines & Healthcare Products Regulatory Agency (MHRA), and other global regulatory bodies are investing heavily in data quality, validation and security guidance creation.
Digital Twins to Further Enhance Digital Clinical Trials
Decentralized clinical trial data are valuable for creating increased efficiency and effectiveness within the clinical value chain as it exists today and form the basis of genuinely groundbreaking innovation.
The objective of a clinical trial is to measure the difference between outcomes when a patient receives the experimental treatment and outcomes when they do not. Logically, both outcomes cannot be observed in the same patient. Digital twins, copies of biological entities like cells, organs or individual people, permit researchers to explore outcome possibilities for actual patients in a digital clinical trial. Essentially, a digital twin is a simulation based on a real person, allowing researchers to study both observed and projected outcomes.
As for many digital clinical trial innovation areas, the critical ingredient is effectively managed and analyzed data. Soon, researchers will be able to model every factor digitally, including genomes and exposomes — basic genetic building blocks to a lifetime of environmental factors for a given patient.
Digital twins will certainly not replace enrolling actual patients in DCTs or conventional clinical trial structures, for that matter. However, digital twins can increase the efficiency of clinical trials by enabling sponsors and contract research organizations (CROs) to model multiple clinical trial scenarios before the treatment of human patients begins, minimizing wasted time, money and personnel resources by launching trials with high probabilities of failure.
The automated collection and analysis required to create digital twins can also help reduce the length of clinical trials. Perhaps most importantly, a digital twin would benefit a clinical trial patient because researchers could better personalize treatments.
This said, there is regulatory uncertainty that must be resolved. For example, although there are established statistical methods acceptable to leading regulators, regulators do not currently have the qualification or validation processes required to allow digital twins to play meaningful roles in clinical studies. Regulatory guidance and resources continue to evolve, and appropriate resources will likely be redirected as the industry creates more established methodologies and use cases.
We Are Advancing Data Management to Unleash the Next Wave of DCT Innovation
The recent Decentralized Clinical Trial Sites Survey from the PPD clinical research business of Thermo Fisher Scientific confirmed that decentralized and hybrid clinical trial models are unquestionably a critical and growing proportion of the clinical trial landscape.
DCT models deliver many demonstrable benefits, such as patient convenience, patient recruitment efficiency, increased patient retention, enhanced data collection opportunities and many other areas of value. However, future DCT innovation and efficiency reside in the ability to harmonize data, allowing it to flow freely throughout the complete clinical trial value chain and ultimately be used to construct digital entities (like digital twins) that promise to bring transformative value to clinicians, sponsors and patients alike.
As an early innovator in creating DCT models, the PPD clinical research team played a pivotal role in facilitating the digitization of critical aspects of clinical trial execution. Namely, PPD clinical research services were vital in facilitating the migration from paper-based clinical outcome assessments (COA) to eCOAs.
Now that the first generation of DCT and hybrid clinical trial models is well-established and being increasingly adopted, we continue to innovate. Cutting-edge data management, flow and manipulation will usher in the next era of decentralized clinical trial progress, and Thermo Fisher Scientific’s PPD clinical research business will help lead the way.