Publications
Monolix Tutorial - Remifentanil Case Study
This tutorial presents a step‐by‐step pharmacokinetic (PK) modeling workflow using MonolixSuite 2019, including how to visualize the data, set up a population PK model, diagnose and improve the model incrementally, perform a covariate search, and keep track of the different runs in the workflow.
The COSSAC approach for automated covariate selection
This publication presents a novel stepwise method to build a covariate model based on statistical tests between individual parameters sampled from their conditional distribution and the covariates. This strategy, called the COnditional Sampling use for Stepwise Approach based on Correlation tests (COSSAC), makes use of the information contained in the current model to choose which parameter‐covariate relationship to try next. In this article, we detail the COSSAC method and its implementation in Monolix, and evaluate its performance.
Beyond the linear model in concentration-QT analysis
This work introduces a more flexible and robust approach to assessing QT liability of drugs through advanced concentration-QT modeling. Moving beyond the standard linear regression model outlined in regulatory white papers, it incorporates nonlinear and delayed-effect models such as log-linear, Emax, and indirect-response structures. By integrating pharmacometric tools—including visual predictive checks and model selection criteria like BIC—this method enables a more accurate evaluation of drug-induced QTc prolongation. It provides a semi-automated framework for comparing models, assessing hysteresis, and selecting the best-fit model, thereby improving the reliability of QTc predictions at relevant drug concentrations.
With our R package, formatting your dataset, running the concentration-QTc analysis and generating a typical report takes only a few minutes: Package "conc-QTc".
Model-Based Meta-Analysis With MonolixSuite: A Tutorial
for Longitudinal Categorical and Continuous Data
This tutorial provides a comprehensive guide for conducting an MBMA with MonolixSuite, focusing on longitudinal continuous and categorical data. It includes two case studies: naproxen in osteoarthritis and canakinumab compared to existing treatments in rheumatoid arthritis.
The tutorial explains the process of model building and handling study heterogeneity in Monolix,
shows how to include between-study variability and between-treatment-arm variability and how to apply appropriate weighting due to the use of summary-level data. For model evaluation, it explains the use of automatically generated diagnostic plots, statistical tests, and convergence assessment tools. Furthermore, it presents how to use the model in Simulx to support the decision-making process, such as by simulating clinical trials. This step-by-step guidance offers practical
insights for leveraging MBMA in model-informed drug development.