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Lewis Sheiner


2018
Montreux, Switzerland



2017
Budapest, Hungary

2016
Lisboa, Portugal

2015
Hersonissos, Crete, Greece

2014
Alicante, Spain

2013
Glasgow, Scotland

2012
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2011
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2010
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2009
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2008
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2007
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2006
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2005
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2004
Uppsala, Sweden

2003
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2002
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2001
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2000
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1999
Saintes, France

1998
Wuppertal, Germany

1997
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1996
Sandwich, UK

1995
Frankfurt, Germany

1994
Greenford, UK

1993
Paris, France

1992
Basel, Switzerland



Printable version

PAGE. Abstracts of the Annual Meeting of the Population Approach Group in Europe.
ISSN 1871-6032

Reference:
PAGE 25 (2016) Abstr 5809 [www.page-meeting.org/?abstract=5809]


PDF poster/presentation:
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Poster: Drug/Disease modeling - Other topics


II-01 Khaled Abduljalil Simcyp Simulator within the DDMoRe Interoperability Framework – Proof of Concept Cases

Khaled Abduljalil, Craig Lewin, Adrian Barnett, Steve Marciniak, Masoud Jamei

Simcyp Limited (a Certara company)

Objectives: A corner stone of model-informed drug development is interoperability among various models, tools and platforms. The Drug Disease Model Resources (DDMoRe) consortium aims at establishing such a framework. As part of this initiative the Simcyp population based physiologically-based pharmacokinetic and pharmacodynamics (PBPK/PD) platform has been further developed and bundled with a command line console which adds support for the DDMoRe Interoperability Framework. In this work two proof of concept case studies are presented.

Methods: Case 1: The Model Description Language Integrated Development Environment (MDL-IDE) was used together with leveraging Simcyp-specific functions within the DDMoRe R package to generate a population data with different CYP2D6 phenotypes as well as PBPK profiles for metoprolol using Simcyp Simulator V15. These data were in the form of a Standard Output (SO) file and numerous Comma Separated Values (CSV) files which were parsed by an R script within the MDL-IDE. The data were then used to construct a table containing dosing, covariates and concentration profiles and passed within mdl file to NONMEM.

Case 2: The MDL-IDE was used to create and execute an R script which utilised functions within the Simcyp R package to execute a pre-saved Simcyp workspace for full PBPK model of midazolam after i.v. administration to paediatric and adult populations. The simulated concentration and PK parameters for both populations were retrieved using Simcyp R package and compared.

Results: While the DDMoRe interoperability framework support is still a 'work in progress', capabilities of the Simcyp Simulator were successfully used from within the MDL-IDE to produce population, compound, and trial design data in the form of a SO file. This was achieved through the use of new Simcyp features, including; the Simcyp R package, Simcyp functions within the DDMoRe R package, and the Simcyp Console application. The obtained PK profiles and database can be submitted for additional tasks within the DDMoRe interoperability framework.  

Conclusion: The unique functionality within Simcyp Simulator V15 will allow DDMoRe partners with a Simcyp Simulator licence to run simulations and generate populations in scripted workflows with other software such as NONMEM, Monolix, PFIM, and PopED. The new features may be used independently of DDMoRe facilities and users will benefit from the Simulator’s vast databases of populations, compounds and PBPK/PD models.