Linked Life Data

10602692

Source:http://linkedlifedata.com/resource/pubmed/id/10602692

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
25
pubmed:dateCreated
2000-1-24
pubmed:abstractText
Several statistical regression models and artificial neural networks were used to predict the hepatic drug clearance in humans from in vitro (hepatocyte) and in vivo pharmacokinetic data and to identify the most predictive models for this purpose. Cross-validation was performed to assess prediction accuracy. It turned out that human hepatocyte data was the best predictor, followed by rat hepatocyte data. Dog hepatocyte data and dog and rat in vivo data appear to be uncorrelated with human in vivo clearance and did not significantly contribute to the prediction models. Considering the present evaluation, the most cost-effective and most accurate approach to achieve satisfactory predictions in human is a combination of in vitro clearances on human and rat hepatocytes. Such information is of considerable value to speed up drug discovery. This study also showed some of the limitations of the approach related to the size of the database used in the present evaluation.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
0022-2623
pubmed:author
pubmed:issnType
Print
pubmed:day
16
pubmed:volume
42
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
5072-6
pubmed:dateRevised
2004-11-17
pubmed:meshHeading
pubmed:year
1999
pubmed:articleTitle
Combining in vitro and in vivo pharmacokinetic data for prediction of hepatic drug clearance in humans by artificial neural networks and multivariate statistical techniques.
pubmed:affiliation
F. Hoffmann-La Roche Ltd., Pharmaceuticals Division, CH-4070 Basel, Switzerland. gilbert.schneider@roche.com
pubmed:publicationType
Journal Article