You are here

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 1 year 6 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 2 years 3 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 2 years 3 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 2 years 8 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 2 years 9 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 2 years 9 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 2 years 10 months 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 3 years 1 week 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 3 years 7 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 3 years 7 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