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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 Parameter Estimation (PE) module that allows either of simulation or estimation modes. The observed clinical data are loaded in XML format and in the shown case the data include both plasma concentration and a PD response profile for simultaneous fitting of PK and PD dependent variables.
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Fig4: A screen shot of the Parameter Estimation (PE) module that allows either of simulation or estimation modes. The observed clinical data are loaded in XML format and in the shown case the data include both plasma concentration and a PD response profile for simultaneous fitting of PK and PD dependent variables.

Mentions: A range of least square objective functions with different weighting methods can be selected for fitting. These objective functions can be fitted using classical Nelder-Mead (Nelder and Mead 1965) or Hooke-Jeeves (Hooke and Jeeves 1961) methods or more modern methods such as Genetic Algorithms (Goldberg 1989). For NLME fitting the Expectation-Maximisation (EM) (Dempster et al. 1977) method is used to solve either of the Maximum Likelihood or Maximum A Posterior problem. A screen shot of the main interface of the PE module is shown in FigureĀ 4.Figure 4


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 Parameter Estimation (PE) module that allows either of simulation or estimation modes. The observed clinical data are loaded in XML format and in the shown case the data include both plasma concentration and a PD response profile for simultaneous fitting of PK and PD dependent variables.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig4: A screen shot of the Parameter Estimation (PE) module that allows either of simulation or estimation modes. The observed clinical data are loaded in XML format and in the shown case the data include both plasma concentration and a PD response profile for simultaneous fitting of PK and PD dependent variables.
Mentions: A range of least square objective functions with different weighting methods can be selected for fitting. These objective functions can be fitted using classical Nelder-Mead (Nelder and Mead 1965) or Hooke-Jeeves (Hooke and Jeeves 1961) methods or more modern methods such as Genetic Algorithms (Goldberg 1989). For NLME fitting the Expectation-Maximisation (EM) (Dempster et al. 1977) method is used to solve either of the Maximum Likelihood or Maximum A Posterior problem. A screen shot of the main interface of the PE module is shown in FigureĀ 4.Figure 4

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.