It's well recognized that lengthy and expensive clinical trials, high failure rates, and increased patient safety concerns
collectively make the economics of drug development more challenging than ever before. Part of the remedy is likely to be
a heterogeneous group of protocol designs, collectively termed "adaptive clinical trials." The common thread between all adaptive
trial designs is the use of accumulating data to make modifications while the trial is still underway, increasing the likelihood
of selecting the right dose (or endpoint) for the right patient population earlier in a development program.
PHOTOGRAPHY: GETTY IMAGES
Adaptive trials are adaptive by design, and they require a wholesale change in the way drug development has traditionally
been carried out.¹ The entire development of a therapeutic candidate, not just the next trial phase, needs to be mapped out in advance. More,
and earlier, operational planning is a prerequisite to conducting adaptive trials, which may require a major culture shift
at some companies.
Just as the technology enabling the adaptive trials themselves has come of age, so too will organizations need to move beyond
traditional tools and approaches for the operational planning, forecasting, and budgeting of adaptive trials. The planning
spreadsheet, a tool commonly used to estimate budget needs for clinical trials, does not provide the flexibility or granularity
to quickly and accurately model and compare the many potential scenarios for an adaptive study. New, more sophisticated approaches
The rationale for conducting adaptive trials is that they can increase the probability of treatment success, identify ineffective
drugs sooner, and require fewer patients, or fewer patients at less informative doses—all without compromising the trials'
validity and integrity. They utilize learn-and-confirm concepts, whereby results get substantiated in an ongoing manner rather
than waiting for a phase to complete. Examples of adaptations that might be made midstudy are changes in eligibility criteria,
sample size, randomization, treatment regimens, and even the primary endpoint.²
Adaptive trials require the use of prospective statistical simulation to optimize the trial design, and the criteria for making
adaptations (modifications) during the study must be defined prior to the study start.
In order to control for potential bias once an adaptive study is underway, an independent Data Monitoring Committee (DMC)
will usually be established to review planned or pending trial adaptations, including verifying their face validity and making
recommendations concerning further conduct of the trial, in addition to its traditional duties.
A separate independent body, with expertise in statistics, also needs to be assembled to prepare data, conduct interim analyses
of unblinded data, and prepare briefing documents for discussion with the DMC. The current randomization algorithm "in play"
must be known only to unblinded personnel, and any sponsor involvement on the DMC requires careful consideration due to the
increased potential for bias.
Adaptive trials will often be more expensive than similarly sized conventional studies, but the long-term savings over the
course of a development program can be substantial. Savings can come from avoidance of future studies that are no longer necessary,
reduced lag time between phases, and the need for fewer subjects. Ultimately, an adaptive trial may result in a product getting
Importantly, an adaptive clinical trial is not always necessary, and may not be viable. In some cases, enough is known about
the patient population and the investigational drug to make an adaptive design unnecessary. In others, outcomes that can trigger
an adaptive response won't be observable early enough in treatment in order to "adapt."3
From adaptive trials that have been successfully completed to date, a general guideline has become apparent: The recruitment
duration needs to be at least four times longer than the observational period in order to preserve the opportunity to make
adaptations and prevent the need to pull back on enrollment. If the observable endpoint is too far out, it's not worth trying
to do an adaptive trial unless a biomarker or other predictor of the outcome is available that shortens the time frame.
Key Operational Planning Pointers