Limits...
The simcyp population based simulator: architecture, implementation, and quality assurance.

Jamei M, Marciniak S, Edwards D, Wragg K, Feng K, Barnett A, Rostami-Hodjegan A - In Silico Pharmacol (2013)

Bottom Line: Interconnection between peripheral modules, the dynamic model building process and compound and population data handling are all described.The Simcyp Data Management (SDM) system, which contains the system and drug databases, can help with implementing quality standards by seamless integration and tracking of any changes.This also helps with internal approval procedures, validation and auto-testing of the new implemented models and algorithms, an area of high interest to regulatory bodies.

View Article: PubMed Central - PubMed

Affiliation: Simcyp Limited (a Certara Company), Blades Enterprise Centre, John Street, Sheffield, S2 4SU UK.

ABSTRACT
Developing a user-friendly platform that can handle a vast number of complex physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) models both for conventional small molecules and larger biologic drugs is a substantial challenge. Over the last decade the Simcyp Population Based Simulator has gained popularity in major pharmaceutical companies (70% of top 40 - in term of R&D spending). Under the Simcyp Consortium guidance, it has evolved from a simple drug-drug interaction tool to a sophisticated and comprehensive Model Based Drug Development (MBDD) platform that covers a broad range of applications spanning from early drug discovery to late drug development. This article provides an update on the latest architectural and implementation developments within the Simulator. Interconnection between peripheral modules, the dynamic model building process and compound and population data handling are all described. The Simcyp Data Management (SDM) system, which contains the system and drug databases, can help with implementing quality standards by seamless integration and tracking of any changes. This also helps with internal approval procedures, validation and auto-testing of the new implemented models and algorithms, an area of high interest to regulatory bodies.

No MeSH data available.


The overall autotesting process which starts from running the repository of workspaces to the generation of summary reports.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4230310&req=5

Fig2: The overall autotesting process which starts from running the repository of workspaces to the generation of summary reports.

Mentions: A final by-product of the Autotest system is metrics. A wide range of metrics are gathered for each simulation and can be compared against previous Autotest runs in order to ensure that either programmatic issues (memory usage, simulation run time, etc.) and/or algorithmic issues (stiff differential equations) have not crept into the design and had any adverse effect on time and/or memory usage. The overall autotesting process is shown in FigureĀ 2.Figure 2


The simcyp population based simulator: architecture, implementation, and quality assurance.

Jamei M, Marciniak S, Edwards D, Wragg K, Feng K, Barnett A, Rostami-Hodjegan A - In Silico Pharmacol (2013)

The overall autotesting process which starts from running the repository of workspaces to the generation of summary reports.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4230310&req=5

Fig2: The overall autotesting process which starts from running the repository of workspaces to the generation of summary reports.
Mentions: A final by-product of the Autotest system is metrics. A wide range of metrics are gathered for each simulation and can be compared against previous Autotest runs in order to ensure that either programmatic issues (memory usage, simulation run time, etc.) and/or algorithmic issues (stiff differential equations) have not crept into the design and had any adverse effect on time and/or memory usage. The overall autotesting process is shown in FigureĀ 2.Figure 2

Bottom Line: Interconnection between peripheral modules, the dynamic model building process and compound and population data handling are all described.The Simcyp Data Management (SDM) system, which contains the system and drug databases, can help with implementing quality standards by seamless integration and tracking of any changes.This also helps with internal approval procedures, validation and auto-testing of the new implemented models and algorithms, an area of high interest to regulatory bodies.

View Article: PubMed Central - PubMed

Affiliation: Simcyp Limited (a Certara Company), Blades Enterprise Centre, John Street, Sheffield, S2 4SU UK.

ABSTRACT
Developing a user-friendly platform that can handle a vast number of complex physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) models both for conventional small molecules and larger biologic drugs is a substantial challenge. Over the last decade the Simcyp Population Based Simulator has gained popularity in major pharmaceutical companies (70% of top 40 - in term of R&D spending). Under the Simcyp Consortium guidance, it has evolved from a simple drug-drug interaction tool to a sophisticated and comprehensive Model Based Drug Development (MBDD) platform that covers a broad range of applications spanning from early drug discovery to late drug development. This article provides an update on the latest architectural and implementation developments within the Simulator. Interconnection between peripheral modules, the dynamic model building process and compound and population data handling are all described. The Simcyp Data Management (SDM) system, which contains the system and drug databases, can help with implementing quality standards by seamless integration and tracking of any changes. This also helps with internal approval procedures, validation and auto-testing of the new implemented models and algorithms, an area of high interest to regulatory bodies.

No MeSH data available.