Predicting the Outcome of Phase III Trials using Phase II Data: A Case Study of Clinical Trial Simulation in Late Stage Drug Development

Open Access
Publisher Website
Google Scholar
Abstract
Maximizing the likelihood of success in Phase III is the ultimate goal of the use of modelling and simulation in the drug development process. The success in Phase III depends primarily on two questions: 1) Is the drug regimen actually efficacious and safe in the targeted patient population?, and 2) Will the planned Phase III clinical trial(s) be successful in demonstrating this? Traditionally, the first question is addressed in a qualitative, overall interpretation of available study results. Integrating this information into a formal statistical model of the action of the drug, allows running simulations to investigate the impact of uncertainties and imprecision in this knowledge. The second question is related to having an adequately designed clinical trial. Clinical trial simulation, using a drug action model, supplemented with appropriate models for disease progression and trial execution, allows assessing the impact of typical design features such as doses, sample size, in‐/exclusion criteria, drop‐out and trial duration on the trial outcome and thus optimising trial design. In this contribution, the use of modelling and simulation in the Phase II to Phase III transition is illustrated using real data of a drug for symptom relief in a chronic condition. A dose‐response model of the clinical response was developed using data from Phase II. Simulations were performed to 1) generate the range of possible outcomes of ongoing Phase III trials and compare these to the blinded data being generated from these trials; 2) assess the robustness of the ongoing Phase III trials with respect to uncertainty of the true dose‐response, patient variability in baseline severity and drug‐response, and 3) assess the likelihood of achieving a clinically relevant response with a dose lower than those included in the trials.