PAGE. Abstracts of the Annual Meeting of the Population Approach Group in Europe.
PAGE 22 (2013) Abstr 2670 [www.page-meeting.org/?abstract=2670]
Poster: Other Modelling Applications
Novartis Pharma AG
Objectives: Model-based drug development (MBDD) emphasizes the quantitative integration of relationships among diseases and disease targets, drug characteristics, and individual variability across studies and development phases for rational and scientifically based decision making . MBDD deals with a large model space, consisting of, e.g., Systems Biology, PBPK/PD, dose-concentration-response, and mechanism based disease models. Fast turnaround of modeling and simulation activities is crucial in order to influence the decision making process in drug development projects. Such a fast turnaround, however, requires that workflows are efficiently supported by powerful and user-friendly tools.
Methods: The “SBPOP Package” has been developed as a toolbox for MATLAB , supporting MBDD from mechanistic modeling to complex trial simulations. An important focus has been on user friendliness, documentation, training material, and extensibility.
Results: It consists of three parts: SBTOOLBOX2 , SBPD, and SBPOP. The first two parts are widely used in the area of Systems Biology, implementing functionality for general representation of dynamic models, simulation, analysis, and parameter estimation. While these parts are ideal for support of data and information integration in research and preclinical phases of drug discovery and development, the third part (SBPOP) adds powerful functionality for PBPK, population PK/PD modeling via a seamless interface to MONOLIX , and clinical trial simulation. The “SBPOP Package” is continuously developed to include additional analyzes and industrializations of standard modeling approaches, such as dose-concentration and concentration-response relationship characterizations. The development is driven by real needs in drug development projects at Novartis, with the goal to increase Modeling&Simulation efficiency, allowing for timely feedback of insights to the decision making process.
Conclusions: The “SBPOP Package” efficiently supports the process of drug discovery and development, providing powerful and user-friendly functionality, from mechanistic modeling to complex clinical trial simulations. Additionally, due to its level of documentation and robustness, it is extraordinarily well suited for educational purposes. The “SBPOP Package” is published as open source under a GNU General Public License and available upon request.