You are here

Error message

The MailChimp PHP library is missing the required GuzzleHttp library. Please check the installation notes in README.txt.

Warning message

The subscription service is currently unavailable. Please try again later.

WP6

It is the aim of all developments within WP6 to provide new tools to facilitate Model-Based Drug Development (MBDD) in the pharmaceutical industry. Indeed, demands for MBDD go beyond the facilitation of interactions between existing software tools.

The demands include:

  • The building of mechanistic models that combine existing knowledge on the disease and the project level with new data from a recent experiment or study. New methodologies for parameter estimation in complex models will be developed.
  • The initial focus is on complex models for diabetes. Specific plans include: visual diagnostics for time-to-event models, guidance for model building with correlated covariates, and new types of normalised prediction distribution errors.
  • The simulation of future experiments or studies in order to support development decisions that lead to an optimal portfolio management. A second prototype of a Clinical Trial Simulator has been distributed for testing to interested EFPIA participants in the work package.
  • The optimal design of future experiments or studies. These are not just designs that increase the probability of success, but designs that deliver results within a reasonable timeframe and within a predefined budget.

Each of the four tasks within WP6 addresses one of these four demands. The specific challenges of WP6 are:

  • To develop new tools that will fit into a framework that itself is evolving during the lifespan of DDMoRe.
  • To develop new tools which meet the expectations of the customers, i.e. the EFPIA members in the consortium.
  • To develop new tools not from scratch but from existing tools which are already in use among the customers.
  • To find a balance between what is doable within the budget of DDMoRe and what is expected by the customer.

 

 

  • Timeline posted on 2 years 8 months ago

    Simulx is a function of the R package mlxR. Simulx allows one to simulate complex models for longitudinal data by interfacing the C++ MlxLibrary with R. http://simulx.webpopix.org/ Learn how to use... Read more

  • Timeline posted on 3 years 5 months ago

    Simulx is a function of the R package mlxR. Simulx allows one to simulate complex models for longitudinal data by interfacing the C++ MlxLibrary with R. http://simulx.webpopix.org/ Learn how to use... Read more

  • News posted on 3 years 5 months ago

    Simulx is a function of the R package mlxR. Simulx allows one to simulate complex models for longitudinal data by interfacing the C++ MlxLibrary with R. http://simulx.webpopix.org/ Learn how to use... Read more

  • Sep 15, 2014
    Afghanistan
    Events posted on 3 years 10 months ago

    September 15 - 17, London, UK, 2014 Effective Applications of the R Language, EARL conference 2014. EARL 2014 is a conference for users and developers of the open source R programming language.  The... Read more

  • News posted on 3 years 12 months ago

    Use of a linearization approximation facilitating stochastic model building.  E.M. Svensson , M.O. Karlsson. Journal of Pharmacokinetcs and Pharmacodynamics 2014, 41: 153   Abstract The objective of... Read more

  • Events posted on 3 years 12 months ago

    September 11, Basel, Germany 10th Basel M&S Seminar, Optimal design strategies in drug development Dose selection for late clinical stage is one of the key challenges in drug development. This is... Read more

  • News posted on 4 years 1 month ago

    A note on BIC in mixed-effects models Maud Delattre, Marc Lavielle and Marie-Anne Poursat Abstract The Bayesian Information Criterion (BIC) is widely used for variable selection in mixed effects... Read more

  • Timeline posted on 4 years 2 months ago

    Simulx is an R function for easily computing predictions and simulating data from both Mlxtran and PharmML models.   Simulx is based on MlxCompute, the model simulation engine developed by Lixoft.... Read more

  • News posted on 4 years 10 months ago

    Comparison of Methods for Handling Missing Covariate Data. Johansson AM, Karlsson MO. AAPSJ AbstractMissing covariate data is a common problem in nonlinear mixed effects modelling of clinical data.... Read more

  • News posted on 4 years 10 months ago

    Multiple Imputation of Missing Covariates in NONMEM and Evaluation of the Method's Sensitivity to η-Shrinkage. Johansson AM, Karlsson MO. AAPSJ AbstractMultiple imputation (MI) is an approach widely... Read more

Pages