Trial predictions vs. trial simulations in early clinical development: a framework to evaluate the predictive probability of success based on NONMEM outputs
B. Boulanger, A. Jullion & P. Lebrun
UCB Pharma and Université de Liège
Objective: In a Model-Based Drug Development strategy, the very first objective is to design studies such that the most reliable model estimates are obtained, in order to optimize the design of future studies and to take decisions based on predictions. The objectives of the work is to present from a theoretical and practical point of view how to perform trial predictions, as opposed to trial simulations, by integrating the uncertainty of the parameters directly from NONMEM outputs. The difference between prediction and simulation is particularly important in early development when limited data or prior information are available: in that case ignoring the uncertainty of parameter estimates can bias the predictive probability of success and yield to wrong decisions.
Method: First, in the light of Bayesian statistical prediction, will be provide methodology to perform trial predictions from the parameter estimates and their uncertainty, when obtained with conventional frequentist population methods as those used by NONMEM. Second, a practical implementation in R will be shown. This implementation extracts directly the necessary information from NONMEM outputs into a generalized prediction shell that can cope with any kind of structural population models: ODE, single & multiple doses, infusion, loading dose etc... The proposed shell is also flexible enough to allow the testing of various scenarios and study designs, including drop-outs for example
Results: When limited prior information is available as in early development, integrating the uncertainty of the parameter estimates is crucial for making prediction-based decision and optimizing study designs. The proposed approach permits to directly evaluate the predictive probability of success in different conditions, such as dose, regimen etc... When several joint models for efficacy and safety are established, the Prediction-based Clinical Utility Index (P-CUI) and its distribution can directly be obtained for more reliable decision making. This is the Design Space thinking applied to dose & regimen conditions. Examples with different amount of prior information will be made to highlight in early phases the differences existing between trial prediction and trial simulation. In late phases, when information is rich, the difference becomes practically negligible.
Conclusion: The proposed approach derived and adapted from the Bayesian statistical prediction methodology, combined with flexible technology as provided by R, permits to establish simple and practical solutions for conducting trial prediction, deriving P-CUI and more important, supporting decision making. The interfacing with NONMEM makes this methodology easy to implement for supporting Model-Based Drug Development strategy and impacting decision, particularly in early clinical phases.
