Although existing problems in clinical trial design have been intensified by COVID-19, the pandemic was not the root cause of these issues. Many trials are at risk due to design issues, resulting in costly protocol amendments and delays. At Phesi, we take a four pillars approach that lets sponsors tackle long-standing problems in trial design. We help our clients to ask the right questions, and we consistently deliver reliable, actionable answers.
Pillar one: Transforming trial design with modal values
Modal values are data that appear most often in historic trial data as the most commonly used protocol elements – including age, gender, treatment duration, outcome measures and treatment comparators, disease measures, concomitant medications, comorbidities and laboratory parameters. In other words, all the possible design elements in a protocol. These data points can be used to apply modal value-based design and so help to identify potential anomalies in a protocol that may cause operational failure or delay.
Pillar two: Creating a synthetic patient
Synthetic patient profiles are created from large historical datasets combined with real-world data from patient records. Synthetic patient profiles allow us to understand how different protocol design elements impact the size and various characteristics of targeted patient population, effectively lead to optimized protocol design. Further, we use synthetic patient profiles to assess safety and efficacy outcomes of patients in placebo/active comparator arms, which leads to digital twin and synthetic control arm. A synthetic control arm that reduces patient burden by allowing sponsors to accurately model comparator outcomes. A synthetic arm addresses the longstanding ethical questions around the use of placebos. Eliminating the placebo arm doesn’t only reduce delays and lower trial costs – it can also improve trial compliance and participation.
Pillar three: Preventing amendments before they happen
Tufts University estimates that nearly half (45%) of amendments are avoidable – our approach at Phesi helps sponsors spot and mitigate amendments before they happen. For example, by identifying variables and trends in an element such as patient age distribution. In a Phesi analysis of 4,000 human diseases it was clear that each disease has a distinct pattern in age distribution. This pattern is both dynamic and predictable – but only with sufficient access to data. Further, with protocol amendments dynamically collected from over 300,000 protocols, we systematically detect soft spots of design in similar trials and effectively proactively prevent repeating of similar mistakes. We help sponsors by facilitating access to such knowledge, enabling them to take a data-driven approach that optimizes trails and minimizes amendments.
Pillar four: Bridging the gap between design and operational implementation
Using the power of predictive analytics, you can understand the relationship between design and execution. For example, a detailed Clinical Trial Enrollment Rate (CTER) analysis can help sponsors understand how different design modifications can impact trial feasibility. CTER is the effective site-level enrollment rate, expressed as patients/site/month. Then when paired with the number of investigator sites in a trial and the enrollment cycle time (time elapsed from first enrolled patient to last enrolled patient), you can then see the benefit of increasing investigator sites, and the point of diminishing returns.
We help our clients to ask the right questions,
and we consistently deliver reliable,
actionable answers.
Leaving the past behind
The pandemic has given the industry a prime opportunity to rethink how trials are designed and conducted. Sponsors now have access through third party suppliers to huge amounts of patient data that can be analyzed via predictive analytics. The industry’s focus should be on optimizing trials so we can replicate the speed and success we’ve seen in COVID-19 studies. The Phesi four pillars will help you to get there, fast.
