J Chem Inf Model - Network-based analysis and characterization of adverse drug-drug interactions.

Tópicos

{ drug(1928) target(777) effect(648) }
{ activ(1452) weight(1219) physic(1104) }
{ system(1976) rule(880) can(841) }
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{ compound(1573) activ(1297) structur(1058) }
{ health(3367) inform(1360) care(1135) }
{ gene(2352) biolog(1181) express(1162) }
{ time(1939) patient(1703) rate(768) }
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{ data(3008) multipl(1320) sourc(1022) }
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{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
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{ cancer(2502) breast(956) screen(824) }
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Resumo

Co-administration of multiple drugs may cause adverse effects, which are usually known but sometimes unknown. Package inserts of prescription drugs are supposed to contain contraindications and warnings on adverse interactions, but such information is not necessarily complete. Therefore, it is becoming more important to provide health professionals with a comprehensive view on drug-drug interactions among all the drugs in use as well as a computational method to identify potential interactions, which may also be of practical value in society. Here we extracted 1,306,565 known drug-drug interactions from all the package inserts of prescription drugs marketed in Japan. They were reduced to 45,180 interactions involving 1352 drugs (active ingredients) identified by the D numbers in the KEGG DRUG database, of which 14,441 interactions involving 735 drugs were linked to the same drug-metabolizing enzymes and/or overlapping drug targets. The interactions with overlapping targets were further classified into three types: acting on the same target, acting on different but similar targets in the same protein family, and acting on different targets belonging to the same pathway. For the rest of the extracted interaction data, we attempted to characterize interaction patterns in terms of the drug groups defined by the Anatomical Therapeutic Chemical (ATC) classification system, where the high-resolution network at the D number level is progressively reduced to a low-resolution global network. Based on this study we have developed a drug-drug interaction retrieval system in the KEGG DRUG database, which may be used for both searching against known drug-drug interactions and predicting potential interactions.

Resumo Limpo

coadministr multipl drug may caus advers effect usual known sometim unknown packag insert prescript drug suppos contain contraind warn advers interact inform necessarili complet therefor becom import provid health profession comprehens view drugdrug interact among drug use well comput method identifi potenti interact may also practic valu societi extract known drugdrug interact packag insert prescript drug market japan reduc interact involv drug activ ingredi identifi d number kegg drug databas interact involv drug link drugmetabol enzym andor overlap drug target interact overlap target classifi three type act target act differ similar target protein famili act differ target belong pathway rest extract interact data attempt character interact pattern term drug group defin anatom therapeut chemic atc classif system highresolut network d number level progress reduc lowresolut global network base studi develop drugdrug interact retriev system kegg drug databas may use search known drugdrug interact predict potenti interact

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