Linked Life Data

14960390

Source:http://linkedlifedata.com/resource/pubmed/id/14960390

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2004-2-12
pubmed:abstractText
Many of the drug candidates that fail in clinical trials are withdrawn because of unforeseen effects of human metabolism, such as toxicity and unfavorable pharmacokinetic profiles. Early pre-clinical elimination of such compounds is important but not yet possible. An ideal system would enable researchers to make a confident elimination decision based purely on the structure of a new compound, and incorporate and use multiple pre-clinical experimental data to support such a decision. Currently available resources can be split into three categories: (i). structure-activity relationships (SAR) computational models based on compound structure; (ii). 'pattern' databases of tissue or organ response to drugs, compiled from high-throughput experiments; and (iii). 'systems biology' databases of metabolic pathways, genes and regulatory networks. In this review, we outline the advantages and drawbacks of each of these systems and suggest directions for their integration.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
1359-6446
pubmed:author
pubmed:issnType
Print
pubmed:day
1
pubmed:volume
9
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
127-35
pubmed:dateRevised
2005-11-16
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Early prediction of drug metabolism and toxicity: systems biology approach and modeling.
pubmed:affiliation
GeneGo, 500 Renaissance Drive, Suite 106, St Joseph, MI 49085, USA. andrej@genego.com
pubmed:publicationType
Journal Article, Review