PhotoGraphy: Bruce Laurance, Getty Images Illustration: Paul A. Belci

In recent years there has been increased industry interest and utilization of adaptive clinical trials. Although the term "adaptive" covers a large range of study features and designs, much of the current excitement is around designs that enable treatment groups to be dropped during the trial to enable more doses to be investigated and/or to reduce time between development phases using seamless designs.

Perhaps the most interesting of these applies to dose finding, where critical decisions regarding the dose to take forward into Phase III is sometimes made on limited information due to practical limitations regarding number of doses that can be investigated in Phase II studies. Taking a suboptimal dose into Phase III can result in having to repeat studies with different doses or can lead to incorrectly terminating development.

Of those drugs that finally reach the market, it is estimated that one in five is launched with a flawed dosage,¹ which can be expensive when discovered after pricing and reimbursement details have been agreed upon.

Implementation challenges

Despite the promise of adaptive designs, some sponsors consider their complexity a barrier to implementation. Challenges such as real-time collection of accumulated subject response and safety data and rapid implementation of randomization adaptations can be overcome by careful selection of integrated technologies.²

Another potential barrier relates to the planning of these designs. Specifically, determining the optimal characteristics of the study design can be a complex yet critical decision. Typical questions might include:

• How many interim analyses should be used?
• What sample size would be optimal at each interim analysis?
• Is a conventional design a suitable alternative?
• Should a Bayesian response-adaptive algorithm be used instead of preplanned interim analyses?

In addition to answering these questions, planning for an adaptive design creates new challenges, not least of which is the estimation of the quantity of drug supply required by the study. This is often a complex question for conventional designs, how much more so for designs that have the possibility of adjusting the proportion of subjects allocated to each treatment as the study progresses?

This article explores how simulation has become a vital tool in answering these important questions, enabling researchers to confidently plan and implement adaptive clinical trials.

Simulation

Simulation is a valuable tool used by many industries to understand and investigate the properties of complex systems. In clinical trials, simulation is commonly used to explore and optimize study design, for example, in selecting between different randomization methodologies to achieve the desired treatment balance. Monte Carlo simulation uses computer-generated random numbers to model real-life variability. This variability enables a true picture of the likely range of outcomes that might result, rather than a single average estimate.

In this article, we demonstrate via a case study how simulation can be used to explore the optimal statistical design for an adaptive trial and to estimate the supply requirements for this design. To achieve this we have combined two proprietary simulation tools. The first is a design simulator that enables the study and statistical analysis to be simulated, including planned interim analyses and decision rules, CytelSim.³ The second is a supply chain simulation tool that models the supply and use of medication packs in studies where the supply chain is controlled using Interactive Voice Response (IVR) systems, MedSim.⁴

Our novel approach has been to link the two simulation engines in such a way that the simulated profile of treatment allocations (including the dropping of treatment arms) generated by the adaptive design simulation can be used by the supply chain simulation in determining the study medication requirements.

As illustrated in the case study that follows, this combination of simulation tools enables researchers to confidently plan and implement these complex study designs, and further to allow the impact of supplies to be taken into account when determining the optimal study design.