Lixoft offers webinars on real test cases and short features of the week videos.

## Webinars

**Warfarin 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.**Tobramycin 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.**Remifentanil case study**: 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.**Longitudinal Model-Based Meta-Analysis (MBMA) with Monolix Suite**: 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.**Analysis of time-to-event data**: 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.**Veralipride 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.**Mini case study: time-varying clearance**: In this mini-case study, we show step-by-step how to write phenomenological and mechanistic models in mlxtran language to capture the time-varying clearance of an example data set.**Mini case study: Advanced VPC: effect of censored data and dropout**: If there are missing observations in your dataset, for example because of missing censored data or non-random dropout, chances are that the VPC is biased. This advanced understanding of the VPC is explained in this mini case-study, along with approaches to correct the bias with the MonolixSuite.**Mini case study: implementing a dose-dependent bioavailability**: In this mini-case study, we show step-by-step how to implement models in mlxtran language to capture the dose-dependent bioavailability of an example data set.

## Features of the week

**#01 The beautiful interactive plots of Monolix2018****#02 Using the built-in libraries of models**: Discover our greatly enriched model libraries and how to efficiently browse through them.**#03 Stratifying plots in Monolix**: Stratifying plots: a simple but impressively powerful new feature of Monolix for data exploration and model diagnosis.**#04 Transforming and adding covariates**: This video unravels how to add covariates, and transform them to get the desired relationship.**#05 Residual error models**: How to diagnose and choose the best residual error model for continuous data in Monolix.**#06 Shrinkage**: Shrinkage is said to bias diagnostic plots but Monolix has a special technique to get around the shrinkage problem. Watch the video to understand how it works.**#07 Initial estimates**: Choosing relevant initial estimates is crucial to get a fast convergence. Discover the features that guide this step in Monolix2018.**#08 Correlations between random effects**: Two clicks are all it takes to define a correlation between random effects in Monolix 2018! As you will see in this video, and more.**#09 Using the table statement**: Learn about how to output variables such as the half-life or AUC from your monolix runs.**#10 Working with several types of measurements**: Find out how to match different types of observations to different model outputs, or use only part of your data set.**#11 Customizing the plots**: Are you looking for publication-ready diagnostic plots? Monolix allows you to change the appearance of the plots to save high-quality figures.**#12 Modelling different types of drug administration**: In Monolix, different types of administrations can be defined in the dataset to be flexibly modelled in different ways.**#13 Encoding and representation of TTE data**: Learn how to encode different types of time-to-event data for flexible modelling in Monolix.**#14 Writing a structural model with Mlxeditor**: In Monolix, it is easy to write a new model or modify a model from the library without mistake thanks to the integrated text editor, with features like syntax highlighting and a compiling check.**#15 Using time-varying covariates**:Time-varying covariates can be used in Monolix, but they have to be defined in the structural model file. This video shows how.**#16 Using regression variables in Monolix**: This video shows with some examples how regressors can be used in Monolix.**#17 Taking censored data into account**: Monolix automatically uses censored data information for estimation tasks, and simulates BLQ values for efficient graphical diagnosis of the model. All you have to do is tag the censored observations in the data set.**#18 Tasks of the Monolix workflow**: This video explains how the different estimation and diagnosis tasks in Monolix are used together in a workflow, and what their results are.**#19 Using the “use last estimates” button**: To speed up the convergence of a run, the initial parameter values can be read from the estimates of the previous run. Learn how and when to use it.**#20 Using the pkmodel() macro to define the PK part of a model**: When writing a custom model, the pkmodel macro permits to define all typical PK models in one line of code.**#21 Defining additional doses with columns Steady State and Additional Doses**: Multiple doses can be encoded in a compact way in a dataset handled by Monolix. Here is how it works.**#22 Preferences of Monolix**: Would you like to export automatically all your plots, or keep a trace of all your runs? This kind of advanced features can be enabled in the Preferences.**#23 Interpreting the correlation matrix of the estimates**: The correlation matrix can help detect a model overparameterization. Discover how in this video!**#24 Modifying the plot layout**: Find out how to fully customize your plot layout in Monolix.**#25 Using the “ignored observation” column**: Save time by working with a unique data set and selectively tagging columns as “ignored observation”**#26 Defining delay differential equations in MonolixSuite**: Learn how to easily implement delay differential equations in the MonolixSuite for complex delay based PKPD models.**#27 Mixture of distributions with latent covariates**: What is a latent covariate? How can it be used in Monolix to model mixtures of distributions for parameters? Find out in this video.**#28 When to save, what to save to avoid losing information**: Struggling reloading projects? Watch this video to understand the best saving workflow.**#29 Exporting the plots data to replot elsewhere**: Learn how to export the plots data from Monolix to reuse them with your other favorite software.**#30 Exploring a TMDD model with Mlxplore**: The application Mlxplore of the MonolixSuite can help you identify the impact of some parameter on your model. This video shows how this is done with a TMDD model.**#31 Inter-individual variability with random effects**: Which standard distribution should you choose for a parameter with random effect, and how can you verify that it is appropriate? Find out in this video.**#32 Parameters with no variability**: Find out how to remove random effect on a parameter, and how this affects its estimation.**#33 Implementing a custom parameter distribution**: Want to use another distribution than those implemented in Monolix? Learn how in this video.**#34 Using different error models for different studies**: Find out how to model data from different studies with different error models while keeping the same population model.**#35 Introducing a scale factor to control parameter units**: Wondering in which units your estimated parameters are? This video tells you how to change them using scale factors.**#36 Exploring new dosing regimens in Mlxplore**: Quick simulations of your model estimated with Monolix can be computed with Mlpxlore, for example to explore new dosing regimens.**#37 Understanding the error messages**: Afraid of writing your own model? This video details the meaning of the most typical error m essages encountered when writing a new model.**#38 Computing the AUC within a PK model**: Computing the AUC can be done easily within a PK model. Watch this video to see an example of AUC simulation in Mlxplore, Monolix and Simulx.**#39 Good practices for ODE-based models**: Watch this video to get all the tricks to be sure that your ODE model will behave as you expect!**#40 Defining a covariate-dependent standard deviation for a parameter**: Find out how to define a parameter with different standard deviations for covariate groups**#41 Calculating the coefficient of variation**: The coefficient of variation can easily be calculated based on the Monolix outputs. This video shows you how.**#42 Convergence assessment**: Find out how easy it is to evaluate the convergence on multiple replicates.**#43 Understanding how and when the analytical solution is used**: Wondering when the analytical solution of the model is used? This video explains all the details.**#44 Understanding how simulated annealing is used for parameter estimation**: This video explains what is simulated annealing, an advanced setting of the parameter estimation algorithm, and in which case it may be disabled.**#45: Understanding how SAEM works**: Frustrated to use Monolix as a black box? This video explains what is exactly going on during the population parameters estimation.**#46 A few useful SAEM settings**: Not sure what the settings exactly mean? Discover the main ones in this video.**#47 The convergence indicator**: This video explains what the convergence indicator exactly is and how to use it.**#48 What is the VPC, and how to get the most out of it**: This video explains how the VPC is built and how to modify it in Monolix to get the most informative plot.**#49 Estimation methods and Bayesian approach**: Bayesian estimation allows to take into account prior information for the estimation of parameters. Find out with this video how to use it in Monolix.**#50 Encoding and visualization of count and categorical data**: Monolix also handles count and categorical data! Learn how to encode this type of data and how to explore it in Datxplore.**#51 Encoding data with occasions**: Occasions can be useful to define inter-occasion variability, or to use covariates that vary between occasions. There are several possible ways to encode occasions in a dataset to be used in the MonolixSuite. They are shown in this video.**#52 Using dose-related keywords in the structural model**: The Mlxtran langage includes reserved keywords to use information from the dosing design in the structural model. Learn which ones in this video.**#53 Applying a washout or a selective reset**: Do you want to reset your model? Watch this recap of the use of the EVENT ID column and reset() macro.**#54 Check lambda_z regression in PKanalix**: For this first feature of the week on PKanalix, discover the “check lambda_z” tab, very useful to visualize and control the calculation of the terminal elimination phase λz.**#55****How to set header preferences**: Tired of tagging your data set columns? Learn how to define which columns headers you want to be automatically recognized in this video!**#56****Reference in check initial estimates**:**#57****Writing a model for 2 drugs**:**#58****Automatic initialization of parameters**: Setting up your PK model is now even faster, thanks to the new automatic initialization of parameters for models from the PK library.**#59****Quick compartmental analysis with PKanalix**:In addition to NCA, one of the main features of PKanalix is a quick calculation of PK parameters in the Compartmental Analysis framework.**#60****Id highlighting across plots**:Monolix now includes an extended feature to highlight ids across plots! Watch this video to see what a great help it is to diagnose quickly your model.