PAREXEL - Sy Pretorius, Chief Scientific Officer talks about Adaptive Designs
My name is Sy Pretorius, and I am PAREXEL’s Chief Scientific Officer. Today, we see more and more clinical trials with adaptive design protocols. Part of my job is to evaluate a need and determine the advantages and benefits for implementing an adaptive design for our clients and, ultimately, for a patient in need of the medicines being developed. Stepping back, it is important to understand what adaptive design trials are, and there are several definitions. The one that I like most and prefer to use is the definition proposed in the FDA draft guidance document issued in 2010. It defines an adaptive trial as a study that includes a prospectively planned opportunity for modification of one or more of the specified aspects of the study design and hypotheses based on the analysis of data. The challenge here is to modify these elements without jeopardizing the validity and the integrity of the study data. An analogy I like to use is to think of the traditional way we conduct clinical trials as somewhat analogous to a cargo plane without windows; in other words there is no way from the outside to tell what is going on inside the plane while the plane is flying to its destination and we typically have to wait until the plane lands before we are able to look into the plane. The analogy of course is that in a traditional trial you need to wait until the end of the study to lock the database before we get a sense of what happened during the study. An adaptive trial is more like a passenger plane with windows – in other words, you have these opportunities / windows during the course of the trial to look at and evaluate the data and make some predefined changes or course adjustments. Examples of adaptive trial designs include: Designs where you stop the trial or an arm because the treatment or dose is not working; sample size re-estimation where we modify incorrect assumptions on effect size and population variance and seamless Phase I/II or Phase II/III trials where we combine studies in order to save time and cost. It is also important to understand the potential benefits of adaptive design, and, in my opinion, there are many. The most important benefit of adaptive design in my opinion is the fact that they provide more information per dollar invested. In other words, they are not necessarily cheaper or faster than traditional trial designs but because you have the opportunity to adjust course and make corrections for some inaccurate assumptions the information gained per dollar invested is typically a lot richer and valuable in these trials. A second, important benefit is the fact that adaptive trials increase flexibility in what is typically a very rigid clinical trial process, all of which can simplify the journey between science and new treatments for the patients who need them most. In other words, you have the flexibility to reduce the trial size or to stop or modify the trials as you go and, by doing so shorten development time and speed up the development process. All of these modifications are planned at the beginning of the trial. Importantly, there is also an ethical benefit to adaptive trials in my opinion. Let me explain: Under the traditional trial paradigm patients would sometimes be in a treatment arm that is clearly not effective; adaptive trials give us the opportunity to either stop subjecting these patients to ineffective, or possible harmful doses, or to move these patients from an ineffective arm to a more effective treatment arm. A very attractive advantage of adaptive trials is that they allow us to address one of the biggest areas of concern in clinical trials today – the high failure rate of Phase III clinical trials. Today, approximately 50% of trials in Phase III fail which have a tremendous economic and social cost. A major reason for this failure is that the wrong dose is used in the Phase III trial. Several commonly-used adaptive designs target getting much more detail on the dose-response relationship by using accumulating data to adjust randomization allocations to the ‘best’ doses – those yielding better results. The result is to give sponsors a better chance to select the right dose in Phase III and to significantly increase the probability of success in Phase III trials.