Quantitative Drug Interactions Prediction System (Q-DIPS) View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2001-09

AUTHORS

Pascal Bonnabry, Johann Sievering, Thierry Leemann, Pierre Dayer

ABSTRACT

Metabolic drug interactions are a major source of clinical problems, but their investigation during drug development is often incomplete and poorly specific. In vitro studies give very accurate data on the interactions of drugs with selective cytochrome P450 (CYP) isozymes, but their interpretation in the clinical context is difficult. On the other hand, the design of in vivo studies is sometimes poor (choice of prototype substrate, doses, schedule of administration, number of volunteers), with the risk of minimising the real potential for interaction. To link in vitro and in vivo studies, several authors have suggested using extrapolation techniques, based on the comparison of in vitro inhibition data with the active in vivo concentrations of the inhibitor. However, the lack of knowledge of one or several important parameters (role of metabolites, intrahepatocyte accumulation) often limits the possibility for safe and accurate predictions. In consequence, these methods are useful to complement in vitro studies and help design clinically relevant in vivo studies, but they will not totally replace in vivo investigation in the future. We have developed a computerised application, the quantitative drug interactions prediction system (Q-DIPS), to make both qualitative deductions and quantitative predictions on the basis of a database containing updated information on CYP substrates, inhibitors and inducers, as well as pharmacokinetic parameters. We also propose a global approach to drug interactions problems--'good interactions practice--to help design rational drug interaction investigations, sequentially associating in vitro studies, in vitrolin vivo extrapolation and finally well-designed in vivo clinical studies. More... »

PAGES

631-640

Identifiers

URI

http://scigraph.springernature.com/pub.10.2165/00003088-200140090-00001

DOI

http://dx.doi.org/10.2165/00003088-200140090-00001

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1007061884

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/11605713


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JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.2165/00003088-200140090-00001'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.2165/00003088-200140090-00001'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.2165/00003088-200140090-00001'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.2165/00003088-200140090-00001'


 

This table displays all metadata directly associated to this object as RDF triples.

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