Case Studies

In the MonolixSuite, the mlxtran language facilitates the description and modeling of time-to-event data. This introduction provides an overview of time-to-event modeling within Monolix, covering various modeling approaches and detailing typical parametric models that are commonly used for this type of analysis.

This case study shows how to build and validate a parametric survival model using the NCCTG lung cancer dataset, identify key covariates, and simulate survival in new patient cohorts. The step-by-step workflow uses Monolix, Simulx, and R to support prognosis and explore the impact of patient characteristics on survival outcomes.

This case study shows how to build and validate a parametric survival model using a lung cancer dataset, identify key covariates, and simulate survival in new patient cohorts. The step-by-step workflow uses Monolix, Simulx, and R to support prognosis and explore the impact of patient characteristics on survival outcomes.

This case study focuses on modeling the pharmacokinetics of veralipride, a drug known for its double peak plasma concentration after oral absorption.

In this example NCA, CA and popPK workflow, we introduce inter-occasion variability to analyse the absorption and elimination of alcohol with or without a dietary additive of guar gum.

In this example modeling and simulation workflow, we model tobramycin pharmacokinetics and simulate dosing regimens in patients with various degrees of renal function impairment.

In this case study, we show how Monolix is used to develop a model for the kinetics of tisagenlecleucel and the impact of therapies for treating cytokine release syndrome (tocilizumab and corticosteroids) on expansion.

In this example, we approximate the response of PCA to warfarin with a neural network.

This case study is for modelers designing pediatric PK trials. It shows how to use simulation and re-estimation to evaluate how precisely model parameters can be estimated with a given design. The step-by-step workflow helps ensure your trial collects data reliable enough for modeling and decision-making.

This case study demonstrates application of Quantitative Systems Pharmacology (QSP) modeling in drug development, using the example of a Fatty Acid Amide Hydrolase (FAAH) inhibitor. It is a practical, step-by-step guide to build, calibrate, and use a QSP model to address key questions. It shows how to integrate various data sources—from in vitro and preclinical studies to human clinical data—to build a robust mechanistic model. This case study also explains the impact of the sensitivity analysis and uncertainty assessment, showing how these techniques can inform critical go/no-go decisions and guide future research.