Claude Magnard, Géraldine Ayral, Monika Twarogowska, Pauline Traynard, Jonathan Chauvin
Lixoft, Antony, France
Introduction:
Now, more than ever, to increase drug success rate and accelerate clinical development, it is important to incorporate new technology – like clinical trials simulators. They improve the quality and efficiency of the decision making process. Modeling&simulation approach models molecules and mechanisms from the available data and then uses these models to generate new information that can optimize your strategies in terms of time, money and commercial success.
PK/PD models can be used to simulate the expected outcome in new situations such as future clinical trials. We present Simulx-GUI – an independent advanced simulation software interconnected with Monolix and flexible in building user-designed scenarios. This application combines a user-friendly interface with the highest computational capabilities to help you make faster and more informed decisions.
Using the same model language as the other applications of the MonolixSuite, models estimated with Monolix (continuous models, TTE, Count/Categorical) can be exported to Simulx in a single step. Not only will the model itself be exported, but also the estimated population parameters, the EBEs, the treatments and covariates defined in the data set used in the Monolix project. These elements can be easily reused for the simulations.
The clear interface allows for the definition of new simulation elements, the setup of the simulation scenario and the post-processing of the simulation outputs.
Objectives: The functionalities of Simulx-GUI are illustrated using a PK/PD model for Remifentanil. The goal is to simulate a clinical trial with 2 arms corresponding to two different infusion rates.
Methods:
The Monolix project, based on a 3-compartment model with an indirect response model, was first exported to Simulx-GUI. This creates the following elements: the observation and the individual model coded in the Mlxtran language, the population parameters estimated by SAEM algorithm, the design (treatments and measurement times) of the original data set and the covariates read from the original dataset.
To set up the simulation, new elements were created. Two treatment elements of an infusion of 120 min with different infusion rates. The model includes the effect of covariates, which were resampled in a simulation from the original data set.
Assessment of the efficacy target was done by post-processing the results into an endpoint: the percentage of individuals reaching a target PD interval after 20 minutes. The uncertainty of this endpoint was then assessed via simulation replicates.
Results:
The simulation produced two model outputs: the smooth PK prediction over a regular grid and PD measurements (with residual error) at manually selected time points. The simulation setup included two groups, one for each treatment arm, with the same number of individuals.
Simulx uses the calculation engine in efficient C++ code and, after running the simulation scenario, generates automatically plots and results for immediate feedback.
The simulation of the predicted PD showed that 68% of the individuals were within the target interval for the higher infusion rate and 53% for the lower infusion rate.
Conclusions:
Simulx-GUI is a flexible, fast and integrated application for simulations. It can be used to simulate a wide variety of models: population PK/PD (including count, categorical and time-to-event), QSP and PBPK with or without inter-individual and inter-occasion variability. Clinical trial simulations can be easily set up via the definition of groups with group-specific elements, such as treatments, number of individuals or covariates. Outcomes and endpoints feature allows for post-processing of the simulation outputs. Finally, using replicates of a simulation scenario enables to assess the uncertainty of the endpoints and can also include the uncertainty of the population parameters.
Reference: PAGE 29 (2021) Abstr 9616 [www.page-meeting.org/?abstract=9616]
Poster: Study Design