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The Simcyp Population Based Simulator: Architecture, Implementation, and Quality Assurance
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 Simulat...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer-Verlag
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4230310/ https://www.ncbi.nlm.nih.gov/pubmed/25505654 http://dx.doi.org/10.1186/2193-9616-1-9 |
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author | Jamei, Masoud Marciniak, Steve Edwards, Duncan Wragg, Kris Feng, Kairui Barnett, Adrian Rostami-Hodjegan, Amin |
author_facet | Jamei, Masoud Marciniak, Steve Edwards, Duncan Wragg, Kris Feng, Kairui Barnett, Adrian Rostami-Hodjegan, Amin |
author_sort | Jamei, Masoud |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4230310 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Springer-Verlag |
record_format | MEDLINE/PubMed |
spelling | pubmed-42303102014-12-11 The Simcyp Population Based Simulator: Architecture, Implementation, and Quality Assurance Jamei, Masoud Marciniak, Steve Edwards, Duncan Wragg, Kris Feng, Kairui Barnett, Adrian Rostami-Hodjegan, Amin In Silico Pharmacol Original Research 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. Springer-Verlag 2013-06-03 /pmc/articles/PMC4230310/ /pubmed/25505654 http://dx.doi.org/10.1186/2193-9616-1-9 Text en © Jamei et al.; licensee Springer. 2013 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Research Jamei, Masoud Marciniak, Steve Edwards, Duncan Wragg, Kris Feng, Kairui Barnett, Adrian Rostami-Hodjegan, Amin The Simcyp Population Based Simulator: Architecture, Implementation, and Quality Assurance |
title | The Simcyp Population Based Simulator: Architecture, Implementation, and Quality Assurance |
title_full | The Simcyp Population Based Simulator: Architecture, Implementation, and Quality Assurance |
title_fullStr | The Simcyp Population Based Simulator: Architecture, Implementation, and Quality Assurance |
title_full_unstemmed | The Simcyp Population Based Simulator: Architecture, Implementation, and Quality Assurance |
title_short | The Simcyp Population Based Simulator: Architecture, Implementation, and Quality Assurance |
title_sort | simcyp population based simulator: architecture, implementation, and quality assurance |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4230310/ https://www.ncbi.nlm.nih.gov/pubmed/25505654 http://dx.doi.org/10.1186/2193-9616-1-9 |
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