Linked Life Data

18206268

Source:http://linkedlifedata.com/resource/pubmed/id/18206268

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
8
pubmed:dateCreated
2008-8-4
pubmed:abstractText
Computational partial least square (PLS) regression models were developed, which can be applied to predict central nervous system (CNS) penetration of drug-like organic molecules. For modeling, a dataset of 77 structurally diverse compounds was used with reported steady-state rat brain to plasma ratios (BPR). Information on steady-state cerebrospinal fluid distribution (CSF to plasma ratio or CSFPR) was available for 37 of these compounds. The molecules were from different chemical series and included bases, acids, zwitterions and neutral molecules. They were CNS active and were therefore assumed to penetrate the blood-brain barrier and/or the blood-liquor barrier. Using these PLS models, the dataset could be described accurately (r(2)=0.78, StErrorEst=0.30 and r(2)=0.75, StErrorEst=0.28 for BPR and CSFPR, respectively). Molecular descriptors used for the prediction of passive membrane transport were lipophilicity, polar and hydrophobic surface areas as well as structural parameters and net charge at physiological pH. There was no apparent correlation between experimental brain and CSF exposure. Consequently, different PLS models and guiding rules were developed and discussed for the prediction of BPR or CSFPR. The present models provide a cost-effective and efficient strategy to guide synthetic efforts in medicinal chemistry at an early stage of the drug discovery and development process.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
0223-5234
pubmed:author
pubmed:issnType
Print
pubmed:volume
43
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1581-92
pubmed:dateRevised
2008-11-21
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
In silico prediction of brain and CSF permeation of small molecules using PLS regression models.
pubmed:affiliation
F. Hoffmann-La Roche Ltd., Pharmaceutical Research, Discovery Chemistry, Grenzacherstrasse, CH-4070 Basel, Switzerland. stefanie.bendels@roche.com
pubmed:publicationType
Journal Article