Considerations and Optimization of Adaptive Trial Design in Clinical Development Programs

Abstract

Although the efficiency of adaptive design on the trial level is well recognized, its impact is even greater when applied at the program or portfolio level. Besides its simplest form of sample size reestimation or early stopping in a given trial, the adaptive design achieves efficiency by combining in a single trial objec-tives that are usually addressed in two separate conventional studies. Another feature of adaptive design is population enrichment where drug response can be optimized to specific patient subpopulations that respond better to treatment. More complex adaptive strategies integrate the development of several compounds and/or indica-tions into one process. We provide an overview of these types of adaptive designs and illustrate their value added in a case study of an adaptive " compound " finder that investigates several compounds in Alzheimer's disease area simultaneously approaching the proof-of-concept stage. Keywords Adaptive compound finder • Adaptive compound/population finder • Adaptive design • Adaptive dose finder • Adaptive indication finder • Adaptive population finder • Allocation rule • Longitudinal modeling • Seamless design • Stopping rule The adoption of an adaptive design strategy across the product development process brings a number of important benefits. These include increased R&D effi-ciency, increased R&D productivity, and importantly increased probability of suc-cess at phase III. We are all too familiar with the worrying industry statistic that 50 % of phase III studies fail and in some therapeutic areas such as oncology or Alzheimer's disease the failure rate is even higher. Innovative adaptive design trials offer the potential to change this industry statistic and dramatically increase the ability of pharmaceutical companies to successfully bring more effective treat-ments to the market. Adaptive designs enhance development efficiency by mitigating the need to repeat trials that just miss their clinical endpoint or fail to identify the effective dose–response at the first attempt. By avoiding the need to run these trials again, significant cost and time savings are achieved. This is possible through use of adaptive designs that enable additional patients to be added to achieve statistical significance the first time around or by allowing a wider dose range to be studied and a better understanding of the dose–response relationship. In addition, early stopping of development programs because a product is ineffective enables scarce resources to be redeployed in additional trials which may show more promise. Early stopping of a trial for efficacy is also possible. All of these factors increase development efficiency. Adaptive design increases development productivity by enabling more accurate definition of the effective dose in a phase II trial which enables better design of the pivotal phase III program, which in turn increases the probability of success of this trial. A number of phase III trials fail because the dose is either too high and causes unwanted safety issues or too low to show sufficient efficacy. Adaptive design enables optimized dose selection before the pivotal trial is initiated. Another feature of adaptive design is population enrichment where drug response can be optimized to specific patient subpopulations that respond better to treatment. Many phase III studies fail because the overall efficacy of treatment is diluted as a consequence of the drug being evaluated in the full population rather than in the specific subset where the drug works best. Adaptive design enables early selection of the appropriate patient population and increases the probability of success. Phases I and II are critical steps in the product development process as this is where important information about the product has to be generated and assessed, before the decision is taken to commit to expensive phase III pivotal studies. This early phase of development is known as the " learn phase " and the data that has to be generated relates to the effective dose–response, the safety profile and therapeutic index, appropriate endpoints, and the population of patients that will benefit best from the product under evaluation. Choosing which development candidate to back when there is a large portfolio of products competing for a fixed level of investment can be a difficult and complex process. The adoption of an adaptive design strategy at the portfolio level can M. Krams and V. Dragalin