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Publications

Monolix Tutorial - Remifentanil Case Study

Traynard, P, Ayral, G, Twarogowska, M, Chauvin, J (2020). Efficient Pharmacokinetic Modeling Workflow With the MonolixSuite: A Case Study of Remifentanil. CPT Pharmacometrics Syst Pharmacol.

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

Ayral Geraldine, Si Abdallah Jean-François, Magnard Claude, Chauvin Jonathan (2021). A novel method based on unbiased correlations tests for covariate selection in nonlinear mixed effects models: The COSSAC approach. CPT Pharmacometrics Syst Pharmacol.

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

Cellière, G., Krause, A., Bonnefois, G. et al. Beyond the linear model in concentration-QT analysis. J Pharmacokinet Pharmacodyn 52, 31 (2025)

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".

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