Predictive Modelling Environment - Infrastructure and functionality for pharmacometric activities in R&D
Lorenzo Ridolfi (1), Chris Franklin (1), Oscar Della Pasqua (1)
(1) Clinical Pharmacology Modelling and Simulation, GlaxoSmithKline, Stockley Park West, Uxbridge, Middlesex UB11 1BT
Objectives: The Predictive Modelling Environment (PME) supports Clinical Pharmacology Modelling & Simulation, enabling the implementation of M&S activities which are used to facilitate decision making within GSK. The system's architecture has been developed taking into account a pre-defined workflow and the interaction between software packages such as R and NONMEM 7.2. Here we describe how a server-based tool has been implemented within the regulated R&D environment. Moreover, we show how workflow and software functionalities are integrated to meet the needs of a continuously growing pharmacometric community.
Methods: Architecture and system requirements to integrate hardware (platforms), software and user interface functionality have been summarised and reviewed against user requirements and workflows for data manipulation, model building, validation and reporting. An overview of system performance is provided, which includes a GAP analysis and a summary of modelling outputs and typical computation times.
Results: The current environment provides access to NONMEM 7.2 as executable software in the web and command line (Linux shell), which are linked to a grid engine with PsN, RStudio and R as ancillary tools supporting the pre-defined M&S workflow. Integrated modules have been identified to provide functionality for project management, data warehouse (DCP), data set creation (DEP) as well for analysis and reporting (DMP). In addition, structured user interface features allow efficient access to previously generated files and templates (e.g. reports). Gaps remain in terms of data reuse, as analysis-ready datasets include software- and model-specific syntax. Likewise, workflows may not always be fully transferred across analyses in an automatic manner due to differences in model selection criteria and data set structure.
Conclusions: PME 2.5 is the result of years of internal and external development where the users' needs have been balanced with the industry standards, taking into account the requirements for an integrated workflow. Many of the technical challenges arising from the development and upgrade of modelling and simulation environment are due to differences in the expectation and expertise within the user community, which impose flexibility and consensus on workflows. System modularity, standard processes and grid computing are essential to ensure M&S tools can be upgraded in a rapidly evolving field.