monolix Suite 2023R1 (x64)

[mitsumi]

monolix Suite 2023R1 (x64)
x64 | File Size: 193 MB

Description
Lixoft develops modeling and simulation software for advanced model-based drug development. Its products offer powerful and easy solutions for population analysis in pre-clinical, clinical trials and in treatment individualization.

Lixoft's technologies are the result of a ten years research program in statistics and modeling, led by Inria on non-linear mixed effect models for advanced population analysis, pharmacometrics, pre-clinical and clinical trial modeling & simulation.
PKanalix is a user friendly and fast application for compartmental and non-compartmental analysis (NCA)
Very intuitive interface
PKanalix can be used via a graphical interface to easily define settings and rules, check the calculations and display the results. It can also be used via R for powerful scripting.
Straightforward NCA
PKanalix calculates NCA parameters with industry-standard methods, and automatically generates a full set of plots to visualize the data, the distributions of calculated parameters and correlations with covariates.
Reliable and clear results
All results are available as tables and summaries, and as interactive plots for a fast and intuitive interpretation. All settings are saved in the project for reproducible results, and the integrated validation suite ensures correct installation and calculation.
Compartmental analysis
Calculation of the parameters in the Compartmental Analysis framework is also proposed with a large library of PK models. In addition, it includes a direct link toward population modeling using Monolix.
Monolix, the best tool for Model Based Drug Development
Monolix is the most advanced and simple solution for non-linear mixed effects modeling (NLME) for pharmacometrics. It is based on the SAEM algorithm and provides robust, global convergence even for complex PK/PD models. Monolix is used for preclinical and clinical population PK/PD modeling and for Systems Pharmacology.
Monolix enjoys a large user community. Monolix is widely used by academia, the pharmaceutical industry as well as the US regulatory agencies.
Advanced Statistical Methodologies
Reliable convergence for all type of data is a centerpiece in population PKPD modeling, which is why Lixoft pioneered in collaboration with Inria the SAEM algorithm.
Automated generation of diagnostic tests
Monolix automatically generates a full set of diagnostic plots even for complex PK/PD models. For example, you can instantaneously create the Visual Predictive Check, split by any patient subgroup you would want to investigate.
Increased productivity and quality
Efficient C++ solver package, standardized model language with Mlxtran, PK/PD model library and integrated software all contribute to better productivity and quality.
Very easy to use with its GUI
Our solutions are designed for ease of use. Monolix can be used via a graphical interface or command lines for powerful scripting. This means less programming for you and more focus on exploring models and pharmacology to deliver in time to your customers.
Key features
-Continuous, categorical, count and repeated time to event data.
-Mixture models and mixtures of models.
-Inter-occasion variability with any number of levels.
-Proper handling of BLQ data.
-Normal, lognormal, logit, probit and user defined distributions for the individual parameters.
Simulx is an easy, efficient and flexible application for clinical trial simulations
Very easy to use with its GUI
Our solutions are designed for ease of use. Simulx can be used via a graphical interface. This means less programming for you and more focus on exploring different treatments and effects of model parameters on a typical individual and/or simulating a clinical trial using a population of individuals in one or several groups with specific treatment or features. All the results are plotted and easy interpretable.
Amazing flexibility
The interface allows an amazing flexibility to be able to simulate any scenarrii. You can define any population, any treatment, any configuration in a few clicks. Clinical trial design has never been so simple.
Advanced Statistical Methodologies
Outcomes and endpoints can be defined in the dedicated section. It provides a simple and efficient comparison between groups in the good statistical framework.
Increased productivity and quality
Efficient C++ solver package, standardized model language with Mlxtran, PK/PD, TMDD, . model libraries and integrated software all contribute to better productivity and quality. You can make your simulation from scract or use a Monolix project.
System Requiremens
-Architecture: 64 bits
-Operating system: Windows 7, Windows 8, Windows 10, Windows 11, Windows server 2012, Windows server 2016, Windows server 2019, and Windows server 2022.
-1GB RAM or above
-Optimally standard screen resolution not less than 800x600 pixels is recommended.
Whats New
Release Notes MonolixSuite2023R1
This document contains most of the evolution and bug fixes of the software in the 2023R1 version.
NEW FEATURES
-Global interoperability
-Extended interoperability which enables exporting a PKanalix, Monolix or Simulx run to any other application of the MonolixSuite.
-In addition to the exports/imports already available, it is now possible to
-export a PKanalix CA run to Simulx to predict how these same individuals would respond to another dosing regimen.
-export a Simulx run, or Monolix individual fits or VPC simulations, to PKanalix to run NCA on simulated data.
Global display
-A reporting module to generate reports automatically based on a custom Word template file
-Placeholders can be written in any Word template file and upon report generation they will be replaced by the plots and tables of a PKanalix or Monolix run
-Placeholders can be easily generated in the GUI for each plot and table, and then copied into the Word template
-Placeholders for plots and tables can be customized so they appear as desired in the report
-Custom axis limits can be set for Observed data and VPC plots.
-In the plot Observed Data, trendlines (mean and standard error) can be merged for different groups on the same plot after splitting by a covariate.
-It is now possible to send feedback on the software directly from the interface of any application of the MonolixSuite.
Data set
-A data formatting module in the interface, to make the input format more flexible.
From an input dataset (not yet in MonolixSuite format), it is now possible to
-merge several observation types
-add censoring information based on BLQ tags
-add treatment information from an external file or specify it manually with a myriad of possibilities (multidose regimen, repeat cycles, infusions, combined therapies, amount depending on a category, use dose intervals as occasions, etc)
-add covariate and regressor columns from another file
PKanalix
-nits Additional normalized units for the amount column, per body weight or body surface area.
-[CA task] The possibility to use any model in CA, self-written in mlxtran syntax or from one of the MonolixSuite libraries.
-[CA task] Improved algorithm in CA to escape better local minima and improve the fit (important for more complex models than standard pk).
-[CA task] New cost function based on Generalized Least Squares which enables to compare models with AIC and BIC.
-[CA task] New weighting option 1/|Ypred*Yobs|.
-[CA results] Individual cost is reported in the individual parameters table, and a new Cost tab shows the value of the global cost, -2LL, AIC and BIC.
-[CA plots] For CA, new Observations vs predictions plot to diagnose the fit.
-[results] Additional summary metrics geometric CV and harmonic mean.
-[preferences] Custom aliases for NCA parameter names and summary metrics names, which appear in the tables, plots and in the report.
Monolix
-[results] Coefficients of variation are reported in addition to standard deviation of random effects in the table of population parameters.
* -data] Sequential approach is now easier since PK parameters from another run can be added as regressors to the dataset directly in the interface (via data formatting module).
Simulx
-[results] In tables and plots, mapping of the simulated ids of the individuals to original identifiers coming from the Monolix run or from external elements defined by the user.
-[plots] It is now possible to merge the simulated output distributions for different groups on the same plot.
-[definition] New elements created after importing a Monolix run to simulate the effect of covariates or parameter uncertainty on a typical individual (omegas set to zero).
-[outcomes] More post-processing possibilities to calculate outcomes such as
-the duration below, above or between specific values,
-the value of the output at a custom time,
-normalization relative to min/max values (in addition to baseline)
-defining an outcome reporting a time as continuous or event type
-[definition] Conversion of an individual element to a population element in one click, and vice versa.
-[plots] Visual cues automatically switches to OFF after setting a visual cue.
Lixoft Connectors / Command Line
-New Monolix connector to run convergence assessment from R.
-New Simulx connectors to define and compute outcomes and endpoints as in the interface.
-New connectors to cover the new features available in the interface (formatData, generateReport, getCACost).
-New function reportGenerator to generate report directly from the command line.
-New connector to select a library model using the same filters as in the GUI.
-Improved error and warning messages in case of non-recognized argument.
-Extensive documentation of Simulx connectors with detailed description and working examples for every function.
-setConsoleMode (displaying the progress of the computations as in the GUI) is now also available for Simulx and PKanalix.
UPDATES
-The ODE solver CVODE was upgraded to version 5.5.
BUGS FIXED
Global interoperability
-[export] Slow file export when exporting on a shared drive has been fixed.
Global display
-[observed data] Dosing times set as always visible disappeared when removing dots and lines.
-[observed data] Axis limits were based on dots even if dots were not displayed on the plot.
-[plots] For all plots, "random colors" option was not working (nothing happened when clicking on it).
-[observed data] When a plot was split and paginated, legend was not properly updated when going from one page to another page.
-[libraries] Parameters displayed when selecting a model from the library were not always the input parameters of the model.
Data set
-[data interpretation] In some very specific cases, when loading a dataset with '1' in CENS column, it was considered as right censoring instead of left.
-[covariate statistics] Stratification covariates did not appear in the covariate statistics tab.
PKanalix
-[calculations] Acceptance criteria was used to filter individuals even for parameters that do not use lambda_z.
-[settings] If partial AUC was selected, it was not possible to remove any of the partial parameters from the list of computed parameters.
-[results] Ids and occasions were displayed as double instead of integers in the table of individual estimates.
-[data] PKanalix crashed when reloading a project where Cc was used as header in the dataset.
-[preferences] Significant digits did not apply to all values in result tables (only to the lines currently displayed).
Monolix
-[check initial estimates] Observation models other than normal distribution were not handled correctly by the auto-initialization algorithm.
-[statistical model] The names of the error parameters were based on the default observation names first given by Monolix after loading the model, and they were not modified if one changes manually the mapping and the observation names.
-[statistical model] The vertical scroll bar disappeared when a formula was displayed.
-[plots / VPC] Selecting and then unselecting "time after last dose" did not bring back to the same state in terms of bins.
Sycomore
-In the comparison tab, NaNs for RSEs were not displayed, the cells appeared empty.
-When selecting a folder containing only one mlxtran file, folder and "scan" button did not appear.
Simulx
-[simulation] Inter-occasion variability was not taken into account when there was only one occasion period.
-[results] Endpoints summary tab appeared in the results even if there were only one replicate.
-[results] Renaming outcome duplicated the result in group comparison summary.
Lixoft Connectors / Command Line
-[getChartsData] getChartsData computed the charts data for all plots even if a specific plot was requested.
-[defineTreatementElement] The option to scale treatment by a covariate was available in the GUI but not in the connector.
-[definePopulationElement] There was a crash instead of an error when trying to set a population parameter element with several lines.
-[setGroupElement] There was an error when setting several elements to the same simulation group.
-[command line] Option --no-gui was not taken into account if option "-t monolix" was used.
-[newProject] newProject did not work with modelFile="lib:...".
-[deleteOccasionElement] deleteOccasionElement deleted mlx_Cov if no occasions were present in the project.
-[setData] setData (and functions of Rsmlx that use it: bootmlx, confintmlx) did not properly set the data on some machines in certain cases, giving the error "Inconsistent observation names between observation types and data file inputs".
-[define..Element] Occasion-wise definition of common elements did not properly take into account the occasion column.
-[getTreatmentElements] getTreatmentElements() returned a truncated number of lines.

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