Different cases studies are presented here to show how to use Monolix and the MonolixSuite for Modeling and Simulation.
- Warfarin simple PK
- Remifentanil simple PK
- Veralipride double peak PK
- Vanorexine PK/PD (QTc)
- Typical PK and PK/PD
- Time-to-event (TTE)
- Longitudinal model-based meta-analysis (MBMA)
- Tobramycin dose individualization
Warfarin PK case study
This video case study shows a simple PK modeling workflow in Monolix2018, with the example of warfarin. It explains the main features and algorithms of Monolix, that guide the iterative process of model building: from validating the structural model to adjusting the statistical model step-by-step.
It includes picking a model from the libraries, choosing initial estimates with the help of population predictions, estimating parameters and uncertainty, and diagnosing the model with interactive plots and statistical tests.
Remifentanil is an opioid analgesic drug with a rapid onset and rapid recovery time. It is used for sedation as well as combined with other medications for use in general anesthesia. It is given in adults via continuous IV infusion, with doses that may be adjusted to age and weight of patients.
This case-study shows how to use Monolix to build a population pharmacokinetic model for remifentanil in order to determine the influence of subject covariates on the individual parameters.
Link to Remifentanil case study
Veralipride double peak PK case study
Multiple peaking in plasma concentration-time curves is not uncommon, and can create difficulties in the determination of pharmacokinetic parameters.
For example, double peaks have been observed in plasma concentrations of veralipride after oral absorption. While multiple peaking can be explained by different physiological processes, in this case site-specific absorption has been suggested to be the major mechanism. In this webinar we explore this hypothesis by setting up a population PK modeling workflow with the MonolixSuite 2018.
The step-by-step workflow includes visualizing the data set to characterize the double peaks, setting up and estimating a double absorption model, assessing the uncertainty of the parameter estimates to avoid over-parameterization, and simulations of the model.
Sudden cardiac death is among the most common types of mortality in developed countries. One of the causes is the prolongation of the ventricular re-polarization, that is the QT interval, which can be induced by certain drugs. Vanoxerine is a dopamine uptake inhibitor studied to treat cocaine dependence, but human trials were stopped due to adverse effects on the QT interval. Concentration-QTc analysis is used to asses the risk of new drugs on a cardiac safety. This case study, based on the Vanoxerine example, explains a possible workflow using MonolixSuite applications of the C-QTc modeling and simulations: heart rate QT correction, PKPD model development and analysis of a drug induced QT prolongation. The workflow combines the use of Daxplore for data visualization, Monolix for parameter estimation and Simulx for the QT interval prolongation simulations.
These three case studies show the typical workflow to stepwise model PK and PKPD data sets. The case studies are part of the Pharmacometrics web-based learning resource from the center for translational medicine of the University of Maryland. They are designed for beginners and show the data set formatting and visualization, the development of a structural model, the development of the statistical model including the correlations and covariates, the model diagnosis using plots and results, as well as different approches for PK/PD modeling.
Within the MonolixSuite, the mlxtran language allows to describe and model time-to-event data using a parametric approach. This page provides an introduction on time-to-event data, the different ways to model this kind of data, and typical parametric models. A library of common TTE models is also provided.
Two modeling and simulation workflows illustrate this approach, using two TTE data sets:
Longitudinal model-based meta-analysis (MBMA) models can be implemented using the MonolixSuite. These models use study-level aggregate data from the literature and can usually be formulated as non-linear mixed-effects models in which the inter-arm variability and residual error are weighted by the number of individuals per arm. We exemplify the model development and analysis workflow of MBMA models in Monolix using a real data set for rheumatoid arthritis, following publication from Demin et al (2012). In the case study, the efficacy of a drug in development (Canakinumab) is compared to the efficacy of two drugs already on the market (Adalimumab and Abatacept). Simulations using Simulx were used for decision support to see if the new drug has a chance to be a better drug.
Link to MBMA case study
This case study presents the modeling of the tobramycin pharmacokinetics, and the determination of a priori dosing regimens in patients with various degrees of renal function impairment. It takes advantage of the integrated use of Datxplore for data visualization, Mlxplore for model exploration, Monolix for parameter estimation and Simulx for simulations and best dosing regimen determination.
The case study is presented in 5 sequential parts, that we recommend to read in order: Part 1: Introduction, Part 2: Data visualization with Datxplore, Part 3: Model development with Monolix, Part 4: Model exploration with Mlxplore, and Part 5: Dosing regimen simulations with Simulx.