Linked Life Data

19603833

Source:http://linkedlifedata.com/resource/pubmed/id/19603833

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
14
pubmed:dateCreated
2009-7-16
pubmed:abstractText
The prediction of human pharmacokinetics early in the drug discovery cycle has become of paramount importance, aiding candidate selection and benefit-risk assessment. We present herein computational models to predict human volume of distribution at steady state (VD(ss)) entirely from in silico structural descriptors. Using both linear and nonlinear statistical techniques, partial least-squares (PLS), and random forest (RF) modeling, a data set of human VD(ss) values for 669 drug compounds recently published ( Drug Metab. Disp. 2008 , 36 , 1385 - 1405 ) was explored. Descriptors covering 2D and 3D molecular topology, electronics, and physical properties were calculated using MOE and Volsurf+. Model evaluation was accomplished using a leave-class-out approach on nine therapeutic or structural classes. The models were assessed using an external test set of 29 additional compounds. Our analysis generated models, both via a single method or consensus which were able to predict human VD(ss) within geometric mean 2-fold error, a predictive accuracy considered good even for more resource-intensive approaches such as those requiring data generated from studies in multiple animal species.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
1520-4804
pubmed:author
pubmed:issnType
Electronic
pubmed:day
23
pubmed:volume
52
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
4488-95
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
In silico prediction of volume of distribution in human using linear and nonlinear models on a 669 compound data set.
pubmed:affiliation
Department of Chemistry, Laboratory of Chemometrics, University of Perugia, 06123 Perugia, Italy.
pubmed:publicationType
Journal Article