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IDAAPM: integrated database of ADMET and adverse effects of predictive modeling based on FDA approved drug data

BACKGROUND: The disposition of a pharmaceutical compound within an organism, i.e. its Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET) properties and adverse effects, critically affects late stage failure of drug candidates and has led to the withdrawal of approved drugs. Computatio...

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Autores principales: Legehar, Ashenafi, Xhaard, Henri, Ghemtio, Leo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4906584/
https://www.ncbi.nlm.nih.gov/pubmed/27303447
http://dx.doi.org/10.1186/s13321-016-0141-7
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author Legehar, Ashenafi
Xhaard, Henri
Ghemtio, Leo
author_facet Legehar, Ashenafi
Xhaard, Henri
Ghemtio, Leo
author_sort Legehar, Ashenafi
collection PubMed
description BACKGROUND: The disposition of a pharmaceutical compound within an organism, i.e. its Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET) properties and adverse effects, critically affects late stage failure of drug candidates and has led to the withdrawal of approved drugs. Computational methods are effective approaches to reduce the number of safety issues by analyzing possible links between chemical structures and ADMET or adverse effects, but this is limited by the size, quality, and heterogeneity of the data available from individual sources. Thus, large, clean and integrated databases of approved drug data, associated with fast and efficient predictive tools are desirable early in the drug discovery process. DESCRIPTION: We have built a relational database (IDAAPM) to integrate available approved drug data such as drug approval information, ADMET and adverse effects, chemical structures and molecular descriptors, targets, bioactivity and related references. The database has been coupled with a searchable web interface and modern data analytics platform (KNIME) to allow data access, data transformation, initial analysis and further predictive modeling. Data were extracted from FDA resources and supplemented from other publicly available databases. Currently, the database contains information regarding about 19,226 FDA approval applications for 31,815 products (small molecules and biologics) with their approval history, 2505 active ingredients, together with as many ADMET properties, 1629 molecular structures, 2.5 million adverse effects and 36,963 experimental drug-target bioactivity data. CONCLUSION: IDAAPM is a unique resource that, in a single relational database, provides detailed information on FDA approved drugs including their ADMET properties and adverse effects, the corresponding targets with bioactivity data, coupled with a data analytics platform. It can be used to perform basic to complex drug-target ADMET or adverse effects analysis and predictive modeling. IDAAPM is freely accessible at http://idaapm.helsinki.fi and can be exploited through a KNIME workflow connected to the database. [Figure: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13321-016-0141-7) contains supplementary material, which is available to authorized users.
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spelling pubmed-49065842016-06-15 IDAAPM: integrated database of ADMET and adverse effects of predictive modeling based on FDA approved drug data Legehar, Ashenafi Xhaard, Henri Ghemtio, Leo J Cheminform Database BACKGROUND: The disposition of a pharmaceutical compound within an organism, i.e. its Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET) properties and adverse effects, critically affects late stage failure of drug candidates and has led to the withdrawal of approved drugs. Computational methods are effective approaches to reduce the number of safety issues by analyzing possible links between chemical structures and ADMET or adverse effects, but this is limited by the size, quality, and heterogeneity of the data available from individual sources. Thus, large, clean and integrated databases of approved drug data, associated with fast and efficient predictive tools are desirable early in the drug discovery process. DESCRIPTION: We have built a relational database (IDAAPM) to integrate available approved drug data such as drug approval information, ADMET and adverse effects, chemical structures and molecular descriptors, targets, bioactivity and related references. The database has been coupled with a searchable web interface and modern data analytics platform (KNIME) to allow data access, data transformation, initial analysis and further predictive modeling. Data were extracted from FDA resources and supplemented from other publicly available databases. Currently, the database contains information regarding about 19,226 FDA approval applications for 31,815 products (small molecules and biologics) with their approval history, 2505 active ingredients, together with as many ADMET properties, 1629 molecular structures, 2.5 million adverse effects and 36,963 experimental drug-target bioactivity data. CONCLUSION: IDAAPM is a unique resource that, in a single relational database, provides detailed information on FDA approved drugs including their ADMET properties and adverse effects, the corresponding targets with bioactivity data, coupled with a data analytics platform. It can be used to perform basic to complex drug-target ADMET or adverse effects analysis and predictive modeling. IDAAPM is freely accessible at http://idaapm.helsinki.fi and can be exploited through a KNIME workflow connected to the database. [Figure: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13321-016-0141-7) contains supplementary material, which is available to authorized users. Springer International Publishing 2016-06-14 /pmc/articles/PMC4906584/ /pubmed/27303447 http://dx.doi.org/10.1186/s13321-016-0141-7 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Database
Legehar, Ashenafi
Xhaard, Henri
Ghemtio, Leo
IDAAPM: integrated database of ADMET and adverse effects of predictive modeling based on FDA approved drug data
title IDAAPM: integrated database of ADMET and adverse effects of predictive modeling based on FDA approved drug data
title_full IDAAPM: integrated database of ADMET and adverse effects of predictive modeling based on FDA approved drug data
title_fullStr IDAAPM: integrated database of ADMET and adverse effects of predictive modeling based on FDA approved drug data
title_full_unstemmed IDAAPM: integrated database of ADMET and adverse effects of predictive modeling based on FDA approved drug data
title_short IDAAPM: integrated database of ADMET and adverse effects of predictive modeling based on FDA approved drug data
title_sort idaapm: integrated database of admet and adverse effects of predictive modeling based on fda approved drug data
topic Database
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4906584/
https://www.ncbi.nlm.nih.gov/pubmed/27303447
http://dx.doi.org/10.1186/s13321-016-0141-7
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