Linked Life Data

19406954

Source:http://linkedlifedata.com/resource/pubmed/id/19406954

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
8
pubmed:dateCreated
2009-7-20
pubmed:abstractText
Cytochrome P450 3A4 (CYP3A4) is the most important enzyme in drug metabolism and because it is the most frequent target for pharmacokinetic drug-drug interactions (DDIs) it is highly desirable to be able to predict CYP3A4-based DDIs from in vitro data. In this study, the prediction of clinical DDIs for 30 drugs on the pharmacokinetics of midazolam, a probe substrate for CYP3A4, was done using in vitro inhibition, inactivation, and induction data. Two DDI prediction approaches were used, which account for effects at both the liver and intestine. The first was a model that simultaneously combines reversible inhibition, time-dependent inactivation, and induction data with static estimates of relevant in vivo concentrations of the precipitant drug to provide point estimates of the average magnitude of change in midazolam exposure. This model yielded a success rate of 88% in discerning DDIs with a mean -fold error of 1.74. The second model was a computational physiologically based pharmacokinetic model that uses dynamic estimates of in vivo concentrations of the precipitant drug and accounts for interindividual variability among the population (Simcyp). This model yielded success rates of 88 and 90% (for "steady-state" and "time-based" approaches, respectively) and mean -fold errors of 1.59 and 1.47. From these findings it can be concluded that in vivo DDIs for CYP3A4 can be predicted from in vitro data, even when more than one biochemical phenomenon occurs simultaneously.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1521-009X
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
37
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1658-66
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Comparison of different algorithms for predicting clinical drug-drug interactions, based on the use of CYP3A4 in vitro data: predictions of compounds as precipitants of interaction.
pubmed:affiliation
Pfizer Global Research and Development, Groton, CT 06340, UA. odette.a.fahmi@pfizer.com
pubmed:publicationType
Journal Article, Comparative Study