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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
Venice, Italy

2011
Athens, Greece

2010
Berlin, Germany

2009
St. Petersburg, Russia

2008
Marseille, France

2007
KÝbenhavn, Denmark

2006
Brugge/Bruges, Belgium

2005
Pamplona, Spain

2004
Uppsala, Sweden

2003
Verona, Italy

2002
Paris, France

2001
Basel, Switzerland

2000
Salamanca, Spain

1999
Saintes, France

1998
Wuppertal, Germany

1997
Glasgow, Scotland

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 5792 [www.page-meeting.org/?abstract=5792]


Poster: Methodology - Other topics


II-42 Jonathan Chard Pharmacometrics workflow: standards for provenance capture and workflow definition

Jonathan Chard (1), Justin Wilkins (2), Amy Cheung (3), Evan Wang (4), Mike K Smith (5), Phylinda Chan (5), Gareth Smith (6), Richard Kaye (1), Maria Luisa Sardu (7), Stuart Moodie (8)

(1) Mango Solutions, (2) Occams, (3) Astra Zeneca, (4) Eli Lilly, (5) Pfizer, (6) Cyprotex, (7) Merck Serono, (8) Eight Pillars Ltd

Objectives: To develop a standard, implemented with a workflow software tool, for capturing the full range of activities and entities that are performed during a pharmacometric analysis, based on existing standards. Capturing the provenance of task outputs (how was this created) as well as providing knowledge management for the pharmacometrics workflow (how did we get to this model) facilitates reproducibility, sharing, and communication of results with others. Using this standard, we can visualise the steps taken during the analysis, reproduce analysis steps, and capture decisions, assumptions, key steps, and support the process of quality control, as suggested in the definition of Model-Informed Drug Discovery and Development (MID3)[1]

Methods: Several existing workflow tools and provenance capture standards were evaluated [3,4,5], but the PROV-O ontology[2] was selected due to its wide adoption, extensibility and suitability for capturing the provenance and relationships between activities and entities within and across projects. Analysis artefacts, actions, information and relationships were mapped onto concepts defined within PROV-O. Tools were developed to support the pharmacometric workflow; storing files in Git [6], generating the provenance information representing the steps taken by the pharmacometrician, and to query the captured information to visualise, report, and regenerate the artefacts within an analysis.

Results: The standard allows tracking of users, software tools, and files in an analysis, while capturing assumptions, decisions and relationships extending beyond input to output. Information can be captured at multiple levels of detail, allowing a reviewer to understand key decisions taken during an analysis, or to trace through the software used to generate results. It is possible to identify project artefacts that are out of date (e.g. a diagnostic plot that should be recreated due to dataset change), and re-run activities. Analysts can apply this information to generate documentation, from run records to complete reports. Knowledge shared between team members is enhanced, avoiding duplication of work, increasing quality and reproducibility.  Traceability assists reviewers and regulators to evaluate assumptions, results and conclusions.

Conclusions: Capturing structured information with software tools helps to ensure data integrity, facilitating QC and adoption of MID3 concepts.

Acknowledgements: This work is presented on behalf of the DDMoRe project (www.ddmore.eu).



References:
[1] EFPIA MID3 Workgroup. Good Practices in Model-Informed Drug Discovery and Development (MID3): Practice, Application and Documentation. CPT: Pharmacometrics Syst. Pharmacol. 2016 doi: 10.1002/psp4.12049
[2] PROV-O: The PROV Ontology. Timothy Lebo, Satya Sahoo, Deborah McGuinness, https://www.w3.org/TR/prov-o/
[3] Paolo Missier, Saumen Dey, Khalid Belhajjame, Victor Cuevas-Vicenttin, Bertram Ludascher. D-PROV: Extending the PROV Provenance Model with Workflow Structure. https://www.usenix.org/system/files/conference/tapp13/tapp13-final3.pdf
[4] Khalid Belhajjame , Oscar Corcho , Daniel Garijo , Jun Zhao , Paolo Missier , David Newman , Raul Palma , Sean Bechhofer , Esteban Garcia Cuesta , Jose Manuel Gomez-Perez , Graham Klyne , Kevin Page , Marco Roos , Jose Enrique Ruiz , Stian Soiland-Reyes , Lourdes Verdes-Montenegro , David De Roure , Carole A. Goble. Workflow-Centric Research Objects: First Class Citizens in Scholarly Discourse. http://users.ox.ac.uk/~oerc0033/preprints/sepublica2012.pdf
[5] Khalid Belhajjame, Jun Zhao, Daniel Garijo, Matthew Gamble, Kristina Hettne, Raul Palma, Eleni Mina, Oscar Corcho, José Manuel Gómez-Pérez, Sean Bechhofer, Graham Klyne, Carole Goble : Using a suite of ontologies for preserving workflow-centric research objects. http://www.sciencedirect.com/science/article/pii/S1570826815000049
[6] Git Distributed Version Control System. https://git-scm.com/