Lisa M. O’Brien (1), Ron J. Keizer (2), Jonathan Klinginsmith (1), Charles Ratekin (1), Michael A. Heathman (1)
(1) Eli Lilly and Company, Indianapolis, IN, USA; (2) Pirana Software & Consulting BV, the Netherlands
Objectives: Lilly is a global corporation with research facilities in many parts of the world, including North America, Europe, and Asia. A distributed NONMEM [1] environment had previously been developed to support these users, and had been in use for more than twenty years. While this UNIX-based, command line system had many benefits for experienced users, a more user-friendly solution was desired.
Methods: System development was undertaken with the following goals:
- enable scientists with varying backgrounds to perfrom modeling and simulation within a unified framework
- take advantage of existing infrastructure, including the distributetd Linux compute environment and automated parallel NONMEM execution
- leverage available open-source tools for automation of common tasks
- reduce required training for new users by providing a graphical interface
- provide command line access for experienced users
- automate generation of figures and tables
- improve performance for users outside of the United States (OUS).
Results: A group was convened to evaluate available software, both open-source and commercial solutions. Based on group consensus, PsN [2,3] and Pirana [4] were selected as the foundation of the new system. PsN provided an industry standard automation tool for NONMEM analysis, while Pirana provided a user-friendly interface.
Several solutions for hardware infrastructure were evaluated, including: Windows desktop deployment, a Windows-based virtual desktop, and a Linux server using NoMachine’s Server software for remote desktop access. The Linux implementation was chosen for its ease of support, superior performance, and the availability of UNIX command line access for experienced users.
Linux servers were installed in Lilly’s US, UK, and Asia research facilities to provide better performance for OUS users. The servers were integrated with existing computational infrastructure using Sun Grid Engine [5] for batch execution. A common file system was placed in the US facility, with local scratch for the UK and Asian servers to facilitate local access.
Conclusions: Implementation of a user-friendly interface to the existing NONMEM system will significantly increase overall throughput and efficiency. The interface will make population analysis more readily available to scientists cross-functionally, thereby allowing modeling and simulation to be applied to a broader range of drug development programs.
References:
[1] Beal, S., Sheiner, L.B., Boeckmann, A., & Bauer, R.J., NONMEM User’s Guides. (1989-2009), Icon Development Solutions, Ellicott City, MD, USA, 2009.
[2] Lindbom L, Pihlgren P, Jonsson EN. PsN-Toolkit–a collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM. Comput Methods Programs Biomed. 2005 Sep: 79(3):241-57.
[3] Lindbom L, Ribbing J, Jonsson EN. Perl-speaks-NONMEM (PsN)–a Perl module for NONMEM related programming. Comput Methods Programs Biomed. 2004 Aug: 75(2):85-94.
[4] Keizer RJ et al.; Comput Methods Programs Biomed 2011 Jan:101(1):72-9; Pirana and PCluster: a modeling environment and cluster infrastructure for NONMEM.
[5] W. Gentzsch, “Sun Grid Engine: Towards Creating a Compute Power Grid”, 1st International Symposium on Cluster Computing and the Grid, 2001.
Reference: PAGE 22 (2013) Abstr 2804 [www.page-meeting.org/?abstract=2804]
Poster: New Modelling Approaches