Is Your EDC a “One-size-fits-all” Platform?
Clinical trial complexity has grown in both size and breadth, from small rare illness studies to massive vaccination trials with tens of thousands of patients. However, not all electronic data capture (EDC) solutions can grow to meet the evolving needs of today's clinical trials.
When clinical trial sponsors reach the capacity limit of their EDC system, they either divide the study execution into smaller, distinct implementations or collaborate with another EDC provider to save the study. Both alternatives drastically interrupt clinical trials and impose extra processes, time, and expenses associated with data aggregation and reconciliation.
To prevent needless interruption, decrease risk, and make operations as easy as feasible, sponsors and CROs want a scalable EDC system for clinical trials capable of fulfilling the particular objectives of their studies.
Why Is EDC Scalability Important?
Although clinical trials share parts, each research phase may contain distinct characteristics, making scalable EDC in clinical trials a crucial component. For example, phase I first-in-human (FIH) trials may involve just 20 to 80 participants, but many gene therapy studies only treat one person. Later-phase and post-marketing studies, on the other hand, might encompass tens of thousands of patients.
The variety and velocity of data utilized in clinical trials have risen as a result of COVID-19's acceleration of the adoption of decentralized trial models. Data for studies may be collected immediately utilizing eCOA apps or wearables, as well as through on-site patient visits. Clinical trials may also use data from electronic health records (EHR) or biobanks.
Depending on the protocol's complexity, a clinical experiment might quickly create millions of data points. Whether data is submitted into the EDC system via an electronic case report form (eCRF), uploaded from lab or imaging reports, or directly obtained from the patient, the EDC system must adapt and scale to give a full picture of all these data points.
The length of a clinical trial determines the requirement for a strong EDC system that can adjust to mid-study alterations as well as complex procedures and dosage regimens. Oncology, which accounts for approximately 30% of pharmaceutical R&D investment, outpaces all other therapeutic categories in terms of complexity due to protocol revisions and data needs.
According to a study conducted by the Tufts Centre for the Study of Drug Development, Phase I-III cancer studies take 14 to 18 months longer than trials in other therapeutic areas. Oncology programs can take up to 12 years to finish, compared to eight years for other courses. Some oncology studies are even longer since patients are monitored for the remainder of their lives. Trial lengths are increasing as medicines become more effective at extending life.
Scale-up for Long-term Clinical Trials
As clinical trials become longer, more complex, and involve more data points, the EDC in clinical research must adapt accordingly. It needs to scale up to add additional patient visits, assessments, and data. It must also provide a comprehensive data view, whether those data come from an ePRO app, from diagnostic assessments, or from vital signs taken by a home health nurse.
Scalability also matters when managing workflows and clinical trial data. User interface and system tasks must remain usable even with high volumes of data streaming through them. In addition, the EDC system needs to use artificial intelligence disciplines like machine learning (AI/ML) to enhance workflows and optimize data management review and data cleaning.
Scaled Down EDC Systems for Precision Medicine and Rare Diseases
When clinical trial data requirements grow more focused, the EDC system must scale down as needed. Smaller organizations doing smaller-scale research may believe they can get by without a specialized EDC system, but this can lead to issues as the investigation advances. Platforms that specialize in simple, quick study constructions, for example, may speed up the launch, but they may not adapt as a study grows. Smaller biotechs and CROs must evaluate the demands of their current trials as well as future trials when selecting an EDC vendor.
How to Scale for the Future With Octalsoft EDC
Because of our robust data management platform, Octalsoft EDC can scale with the trial, which means we can monitor multiple studies in parallel and potentially merge datasets as needed from these studies to support a streamlined development plan, leading to approval when we reach that stage.
Choose an EDC platform with the flexibility and scalability to perform pharma, biopharma, and/or medical device studies, regardless of size, phase, or therapeutic area, as clinical trials become more complicated and large.
In Summation
An EDC platform cannot scale effectively unless it integrates with RTSM, CTMS, eCOA, eConsent, and other systems. When all of these solutions work together on a single platform, all research data is centralized in one place for maximum flexibility. Users may obtain real-time data for speedy decision-making and mid-study adjustments.
When it comes to monitoring adverse events, a single platform integrated with AI/ML enables clinical monitors and data administrators to detect abnormalities more quickly and simply.
Sponsors and contract research organizations (CROs) conduct studies ranging from very targeted precision medicine assessments to worldwide Phase III-IV trials. Choose an EDC that is scalable and flexible enough to support the whole spectrum of studies as well as any mid-study surprises.
Want to know more about how Octalsoft EDC can help you scale your trials? Have a quick chat with one of our experts by following this Link. We look forward to hearing from you. Watch this space for more information, updates, and fresh insights for your clinical trials in Octalsoft’s vast library of scientifically driven publications written by our team and industry key opinion leaders.
