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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.


A screen shot of the automated sensitivity analysis tool in Simcyp Version 12 Release 2; an example for assessing the impact of fraction unbound in plasma and the absorption rate constant on specific outputs where the minimum and maximum values, the steps and the step-size distributions are defined.
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Fig3: A screen shot of the automated sensitivity analysis tool in Simcyp Version 12 Release 2; an example for assessing the impact of fraction unbound in plasma and the absorption rate constant on specific outputs where the minimum and maximum values, the steps and the step-size distributions are defined.

Mentions: On occasion there is uncertainty in the true value of an input parameter. This may be due to some particular parameter being unavailable for the drug of interest or because for that particular compound the in vitro data is unreliable. In these cases it is useful to check the impact that the input parameter has on the simulation outcome. This can be achieved using the automated sensitivity analysis (ASA) tool. This is a local sensitivity tool which scans the selected parameters within a given range and reports the selected endpoints for a population representative subject. ASA can be used to assess the impact of changing specific parameters (maximum of two at a time) on a range of PK/PD parameters or concentration-time profiles. For investigating more than two parameters the Batch processor (Jamei et al. 2009a) can be used instead. Identifying whether an input parameter has a significant impact on the outcome of a simulation is highly valuable as it assists with making decision on what in vitro assays should be done at what stages and how much resource should be invested in obtaining a particular parameter for a particular compound. ASA can be performed on virtually any parameter displayed on the Simulator interface. After a parameter is selected the ASA interface tool is called within this interface the user can define the parameter ranges and the number of steps the range should be divided into, as shown below (FigureĀ 3).Figure 3


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)

A screen shot of the automated sensitivity analysis tool in Simcyp Version 12 Release 2; an example for assessing the impact of fraction unbound in plasma and the absorption rate constant on specific outputs where the minimum and maximum values, the steps and the step-size distributions are defined.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig3: A screen shot of the automated sensitivity analysis tool in Simcyp Version 12 Release 2; an example for assessing the impact of fraction unbound in plasma and the absorption rate constant on specific outputs where the minimum and maximum values, the steps and the step-size distributions are defined.
Mentions: On occasion there is uncertainty in the true value of an input parameter. This may be due to some particular parameter being unavailable for the drug of interest or because for that particular compound the in vitro data is unreliable. In these cases it is useful to check the impact that the input parameter has on the simulation outcome. This can be achieved using the automated sensitivity analysis (ASA) tool. This is a local sensitivity tool which scans the selected parameters within a given range and reports the selected endpoints for a population representative subject. ASA can be used to assess the impact of changing specific parameters (maximum of two at a time) on a range of PK/PD parameters or concentration-time profiles. For investigating more than two parameters the Batch processor (Jamei et al. 2009a) can be used instead. Identifying whether an input parameter has a significant impact on the outcome of a simulation is highly valuable as it assists with making decision on what in vitro assays should be done at what stages and how much resource should be invested in obtaining a particular parameter for a particular compound. ASA can be performed on virtually any parameter displayed on the Simulator interface. After a parameter is selected the ASA interface tool is called within this interface the user can define the parameter ranges and the number of steps the range should be divided into, as shown below (FigureĀ 3).Figure 3

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.