Imagine, for a moment, you’re a data manager overseeing a Phase I study. Your endpoints are set, your database built and deployed, and your data collected. Now it’s time to cross your fingers, unblind your database, and hope for favorable tolerability results for the dosage you established. To your dismay, you discover you’ve overestimated the maximum tolerated dosage. What’s worse, had you been able to access the database during the study, you could have discovered this shortly after deployment and made protocol modifications that would have saved you time, money, and energy that has now been spent in vain. It’s an outcome that would be a shame to repeat. You resolve to empower yourself to make those modifications by taking an adaptive approach to trial design for future studies – and to invest in the tools that make adaptive trials possible.
Adaptive trials and electronic data capture
A traditionally designed clinical trial is, by nature, a roll of the dice. Once a database is deployed, researchers can’t make many changes until the end of the study. In adaptive trials, however, databases can be unblinded while the study is in progress so that researchers can make changes to certain endpoints as needed, creating the potential for substantial time and monetary savings. But while more researchers than ever before are investing in electronic data capture (EDC) software to enhance the efficiency of their research, many EDC companies aren’t effective in accommodating adaptive designs and are thus limited in offering the efficiency those designs make possible. Some EDCs, for example, when a mid-study discovery in an adaptive trial calls for a modification of the study’s endpoints, require researchers to take the entire database offline, perform complex modifications upon it, and ultimately push it live again. The time and resources spent on such an endeavor negate much or all of the efficiency of an adaptive design.
Adaptive trials are most common in Phase I, but some pharma experts see increasing adoption of the approach in Phases II and III.1 As the trend spreads across the clinical research landscape, the EDC companies best positioned to serve the needs of the industry will be those that are capable of managing adaptive trials with efficiency. Researchers taking an adaptive approach to trial design will benefit from seeking out an EDC that provides them with the ability not only to make mid-study changes, but to do so in a surgical and non-disruptive manner. Medrio allows users to make select changes to select forms while a study is in progress, as opposed to the wholesale redesign of a study database that is required elsewhere. Users can also test their changes in a development environment separate from the ongoing study, instead of taking the entire study offline until the changes are finalized and deployed. The ability to make mid-study changes is one thing; the ability to do so without delaying the study or, even worse, starting from scratch will allow researchers to conduct adaptive clinical trials without paying for it in lost efficiency.
Why Phase I?
Given the long-term impact that study design can have on a treatment’s success, the degree to which an EDC company can accommodate adaptive trials is an important consideration. There are, after all, reasons why adaptive designs are particularly prevalent in Phase I trials. According to adaptive trial experts, one of those reasons is that the ability to optimize the results of a Phase I trial significantly increases the chances of success in the next phases of a drug’s development.2 Modifying certain aspects of a study database as needed during an adaptive trial is one of the most effective ways of achieving that optimization.
The question, therefore, of whether an EDC is robust at accommodating adaptive trial designs is one that can have reverberations from Phase I all the way to FDA submission. Researchers would be wise to consider this question thoroughly before making an EDC selection.
1 Miseta, Ed; Adaptive Trials: Complex But Advantageous; Clinical Leader; 28 September 2016
2 Adaptive Trial Design for Phase I and Phase II; Medelis