Investment in decentralized clinical trials pays off: analysis
Since well before the arrival of COVID-19 acceleration of decentralized clinical trial (DCT) use, drug development professionals have been weighing the risks, benefits, and costs of adopting the format. Now, an analytical collaboration between Medable and the Tufts Center for the Study of Drug Development (CSDD) examining the potential return on investment (ROI) associated with DCTs indicates that diving into decentralized just might be worth the effort.
During the recent DIA Annual Meeting, Pam Tenaerts (Medable’s chief scientific officer) and Ken Getz (CSDD executive director) presented “The Financial Return on Investment of DCTs, a session discussing how putting money and time into decentralized clinical trials can not only help studies survive during trying times (like the COVID-19 pandemic) but it also can pay off in numerous ways.
“It's really amazing when you get to deliver data that shows that not only are you doing the right thing, it's actually the most sensible thing from a financial perspective,” Tenaerts remarked.
Tenaerts talked about the work Medable commissioned from the Tufts CSDD group to dive into the matter.
“This is what I believe to be one of the first hard metrics around DCTs—it’s a financial one, but it's based on some operational metrics as well,” she said. “We are committed to providing metrics and data for responsible DCT adoption, and this is just the first step in that journey; we are committed to providing evidence metrics, key studies, and best practices around a slew of these things that we really need to understand about DCTs and how they affect clinical trials.”
According to Tenaerts, DCTs do (at least at the beginning of the adoption and transition process) require more money to get started, but it does pay off. Compared to a “brick and mortar” onsite trial, the investment in a DCT seemingly begins showing an ROI sooner.
Getz said at the outset, the Medable-Tufts analysis sought to answer the question of why measuring the ROI of DCTs is important, a process that is ongoing: “As we collect more data, we will know so much more about the impact that DCT has on many of the value drivers, including, speed, quality efficiency, risk, and cost,” Getz advised.
Additionally, Getz pointed out, the pandemic accelerated the adoption of DCTs in an unprecedented way.
“The typical adoption of a technology supporting clinical trial execution takes about six years,” he said. “There's that planning and initiation stage, there's an evaluation stage—each a little more than a year long. There's the period that long period that companies take to evaluate their pilot tests and choose to ultimately move forward with a full implementation.”
With the COVID-19 pandemic crashing down on the industry and bringing studies to a halt, though, trial teams did not necessarily have the luxury of a long lead time.
“We did not have the opportunity to follow many of these traditional stages for some organizations,” Getz said. “The adoption of DCTs was almost instantaneous with no real piloting effort; they just jumped right into it.”
The analysis at the heart of the discussion examined real data whenever possible, Getz said, and examined protocols supported by DCT solutions as well as those with no virtual or remote support. Parameters examined included standard development, commercialization, and performance.
“We're looking at a 77-month time horizon for the typical project that is initiated in Phase II and goes all the way through to approval, and, 47 months for the Phase III program,” he said. “The probability of a program making it through Phase II and into Phase III is less than 20%.”
Approximately 60% of any projects that make it into Phase III ultimately make it through to approval, Getz said, adding that the time to reach peak sales is about 10 years, with average peak sales of about $1.8b USD.
Important to note, Getz said, is the cycle time comparison.
“If we look at all of the cycle time data here with one exception, the cycle times are faster for that group of protocols that were supported by a DCT solution,” Getz pointed out. “In fact, in the total duration from protocol approval to database lock, we're looking at a mean speed improvement of 246 days for a Phase II trial, and 360 days for a Phase III trial.”
These reduced cycle times can reap significant savings.
“If we look at even a 5% reduction in cycle time, we're looking at a 5.5 times multiple over that $3.1m investment, which delivers roughly a $17m-plus increase in eNPV [expected net present value],” Getz said.
He added the analysis team aims to expand upon the promising data about the ROI of DCT solutions as time goes on.
“We hope to really build on the insights from this as we move forward with even more data and a larger sample size,” he commented, noting they hope to increase sample size, break out by disease and condition, and add metrics.
“We are really excited to continue to expand on this model and to populate it with even more data moving forward,” Getz said, adding that they plan to release the publication in a couple of months.
