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

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http://hdl.handle.net/10138/164952

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Legehar , A , Xhaard , H & Ghemtio , L 2016 , ' IDAAPM : integrated database of ADMET and adverse effects of predictive modeling based on FDA approved drug data ' , Journal of Cheminformatics , vol. 8 , 33 . https://doi.org/10.1186/s13321-016-0141-7

Title: IDAAPM : integrated database of ADMET and adverse effects of predictive modeling based on FDA approved drug data
Author: Legehar, Ashenafi; Xhaard, Henri; Ghemtio, Leo
Contributor: University of Helsinki, Division of Pharmaceutical Chemistry and Technology
University of Helsinki, Faculty of Pharmacy
University of Helsinki, Faculty of Pharmacy
Date: 2016-06-14
Language: eng
Number of pages: 11
Belongs to series: Journal of Cheminformatics
ISSN: 1758-2946
URI: http://hdl.handle.net/10138/164952
Abstract: 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 bio-logics) 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.
Subject: FDA approved drugs
ADMET
Adverse effects
Targets
Database
Predictive modeling
Drug-target database
Data analysis
IN-SILICO PREDICTION
PHARMACEUTICAL RESEARCH
TRANSPORTER DATABASE
MEDICINAL CHEMISTRY
PHARMACOLOGY
INFORMATION
TOXICOLOGY
DISCOVERY
TARGET
RATES
116 Chemical sciences
113 Computer and information sciences
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