MoBi® Toolbox for MATLAB®

Integrating Multiscale Physiological Modeling and Simulation with Technical Computing.

The MoBi® Toolbox for MATLAB® is a collection of MATLAB® functions, which allow the processing of models developed in MoBi® from within MATLAB®.

For example, the MATLAB® environment can be used to change parameters in a model developed in MoBi®, simulate the model, and analyze the results. This allows an efficient operation in the model analysis stage, using the programming options as well as the function library available within MATLAB® in combination with the powerful modeling interface and solver kernel included MoBi®. In addition, the Toolbox offers efficient analysis methods tailored towards the needs of systems biology and PBPK modeling including parameter identification and optimization among others.

Basic functionalities such as parameter identification can be operated without any Matlab® or programming knowledge through Graphical User Interfaces (GUIs). Nevertheless, the MoBi® Toolbox also provides an interface to an easy to use programming environment, which allows individual extensions to be quickly implemented. This allows an automation of tasks and the establishment of protocols and standards. These can vary in their level of sophistication from simple batch simulations to large-scale simulations, followed by an automated analysis including data and graphic processing.

For example, we have developed a protocol for studying pediatric populations, which we routinely employ in projects. Starting from an adult PBPK model that has been developed within PK-Sim® (and MoBi®), a set of GUIs guide through a few project-specific specifications needed, allow an evaluation of settings, as well as the selection of output options of interest. The rest is executed in an automated fashion: thousands of virtual individuals are simulated and analyzed; the results are visualized and written into graphical and data files including meaningful table and figure captions; Macros within Microsoft Word® read and format the information generating the basis for reporting to customers or agencies.

Graphical User Interface to configure parameter identification task. 
Snapshots of a progressing parameter identification - selected parameters are varied and optimized with the goal to minimize the error between experimentally observed (crosses) and simulated (lines) data, both for a parament substance and a metabolite.

 (Copyright information: MATLAB® is a registered trademark of The Mathworks, Inc.)

March 28, 2017

Published in npj Systems Biology and Applications: Translational learning from clinical studies predicts drug pharmacokinetics across patient populations

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December 19, 2016

New publications regarding PBPK modeling: Elderly, Isoniazid, DDI and Diabetes. In addition: A white paper regarding good practices in drug discovery and development

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October 18, 2016

Published in CPT Pharmacometrics & Systems Pharmacology: Applied Concepts in PBPK modeling: How to build a PBPK/PD model

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March 17, 2017

PAGE Meeting 2017, June 06-09, Budapest, Hungary

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October 27, 2016

ACoP Meeting 2016, October 23-26, Seattle, WA, USA

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September 24, 2016

ACCP Annual Meeting 2016, September 25-27, Bethesda, MD, USA

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