July 17, 2024, 1:03 PM EDT
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When NASA plans to launch a spaceship to Mars, their engineers carefully draft a considered flight plan with a calculated, multi-step trajectory: the initial rocket thrust required to achieve exit velocity, a clever slingshot around the moon to gain some additional acceleration, a reverse thrust burn to slow the approach, and so on.
But along the voyage, NASA engineers also know there will be many smaller details of the flight path to monitor, and certainly some necessary adjustments to make – small engine burns, course corrections, vehicle attitude adjustments. NASA anticipates these mid-flight modifications will be needed, even without knowing exactly what those modifications might be, or what specific challenges might necessitate them. This anticipatory planning is also reflected in the overall budget - the amount of reserve fuel kept aboard the ship and the expectations of everyone involved. Prepping for the unknown, closely monitoring in-flight progress, and executing minor “just in time” adjustments are essential for NASA to be successful.
This is exactly the data-driven engineering mentality that Lokavant brings to the clinical trial industry.
The initial feasibility analyses for a clinical trial begin at the start of portfolio planning, often referred to as the “Strategic Feasibility” phase. Typically, this is a pre-planning “thumb in the wind” exercise, a gut check: Could we enroll this trial within six months, given our budget? No way? Then how about in five years? Definitely. Okay then, what is a realistic target window for completed enrollment narrower than six months and five years? At what cost? With a regulatory plan that supports our market strategy?
Then the questions get stickier as the team moves from Strategic to Operational Feasibility: What are the operational risks? Are there competitors running similar trials that might drive us to consider an accelerated enrollment plan? What are the operational plan requirements, with respect to total enrollment, and country recruitment targets? What’s a reasonable recruitment rate to expect, in total or by country? What regulatory timelines do we need to consider, and how will that impact our planning? What enrollment plans are not only doable but are optimally risk-reduced, given a fixed budget? How much more would it cost to get enrollment finished six months sooner?
Initial goals and constraints around total costs, enrollment time, patient eligibility criteria and collected endpoints will all impact enrollment expectations and inherent probabilities for success and are some of the adjustments that study teams can make during the initial planning phase – often leaving one aspect “better” at the expense of the others.
To increase the probability of success, you may need to reduce the number of patients, plan for a bigger recruitment budget to achieve a shorter timeline, or instead extend the timeline to allow an existing budget to do its work. If this is the most important trial in a sponsor’s portfolio, the plan may require a probability of enrollment success of 95% or higher for it to be considered feasible, so what adjustments will be necessary to hit that goal? Managers must work the available levers and find an optimal solution, given the various goals and constraints. With the right balance of conditions, feasibility is agreed upon and a trial moves forward.
Once is never enough
All of the above processes are great – and standard fare for any clinical study. The problem is, it’s too often a one-and-done exercise, a point in time in which all these factors are considered, without the “What if?” planning and foresight of our NASA spaceship analogy. Once an initial feasibility analysis and operational planning are complete, feasibility is rarely revisited to a meaningful degree while a trial is “in flight.”
We’re in a time where that must change. According to a 2022 study by the Tufts Center for the Study of Drug Development, the prevalence of Phase 1 to 4 clinical trial protocols with at least one significant protocol amendment, many designed to improve enrollment, has climbed from 57 to 76% since 2015. Plus, the average number of amendments per protocol increased 60% from 2.1 to 3.3. Redoing a feasibility analysis at an appropriate point mid-study – either one or several times, depending on the protocol amendments and challenges – could be the critical step in keeping a trial on track, and avoiding expensive and time-consuming protocol amendments. Right now, when there are small course corrections needed (i.e., opening new sites; launching a new patient outreach campaign; engaging a centralized patient registry), they’re often identified too late, or without clear direction on how to handle them in a way that will keep a trial from failing to meet its goal, as 80 to 90% of trials do.
Consider this common scenario: A year and a half into a trial, six months from the expected close, management wants to understand the trial’s status and if it’s on track. “Not good”, says the operations team – enrollment numbers are off and we’re going to run six months over. Had a feasibility revisit been triggered sooner, perhaps more budget could have been allotted, the timeframe extended, and additional sites opened to help meet the goal. Like our NASA analogy, engines could have been burned, and some course corrections made, to reach the target successfully.
This is not to say the operational experts behind trials aren’t vigilant in their monitoring of recruitment progress and data review during trials. However, they may not be using the natural milestones in the lifecycle of a trial to regularly re-examine the plan, with an updated forecast, such as when a study expands into a new country, or when that country is granted regulatory approval sooner or later than originally expected.
And they may not be responding quickly enough to red flags – or have the proper tools to know what the red flags really are.
Help is on the way
So let’s talk about tools and what we can do to improve this process.
Unfortunately, not all of them get the job done.
In fact, some of the AI-fueled technology platforms currently available to trial planners don’t understand red flags correctly, either. For example, some sites recruit more participants in a month than projected, others less. The next month could be different. Many predictive tools inadvertently identify decreases in rates – spiky behavior that’s part of the natural ebb and flow of recruitment – as red flags. Many of the tools on the market also don’t properly account for realities like site exhaustion, when there are simply no more trial participants left to recruit in each area.
Most are, essentially, just scenario-forecast planners – plug-and-chug calculators. You enter the numbers for what’s expected in a trial, click a button, and get an answer about what that scenario equates to in terms of cost, time, and probability of success.
To conduct continuous feasibility analysis in a way that matters, operations teams need better than just basic forecasting with the potential for false alarms. They need the ability to see the projected particulars of what would happen in alternate scenarios, combined with informed guidance on what to do when advance tooling detects things are beginning to veer off course, or when a new factor emerges that wasn’t there when feasibility was first examined.
What if a competitor suddenly enters the picture after a trial is underway? What if an opportunity arises to end a trial six months earlier? What would that look like? We don't want to go to Mars anymore – we want to go to Saturn. How would we change the plan?
These kinds of insights are coming soon, and can lead operations teams to consider scenarios they might not have otherwise considered – ones that could mean the difference between success and failure because they’ll be getting more of the “why.”
Above all else, start looking at feasibility analysis as an ongoing need, an iterative process that is not just a box to check once at the beginning. And be ready for the imminent technology that your skilled experts can turn to make your feasibility checks informative, insightful and prescriptive.
And just like NASA, the advanced preparation and planned mid-journey checks and adjustments will be your rocket ship to success.