Drug interaction prediction using ontology-driven hypothetical assertion framework for pathway generation followed by numerical simulationReportar como inadecuado

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BMC Bioinformatics

, 9:S11

First Online: 28 May 2008


BackgroundIn accordance with the increasing amount of information concerning individual differences in drug response and molecular interaction, the role of in silico prediction of drug interaction on the pathway level is becoming more and more important. However, in view of the interferences for the identification of new drug interactions, most conventional information models of a biological pathway would have limitations. As a reflection of real world biological events triggered by a stimulus, it is important to facilitate the incorporation of known molecular events for inferring unknown possible pathways and hypothetic drug interactions. Here, we propose a new Ontology-Driven Hypothetic Assertion OHA framework including pathway generation, drug interaction detection, simulation model generation, numerical simulation, and hypothetic assertion. Potential drug interactions are detected from drug metabolic pathways dynamically generated by molecular events triggered after the administration of certain drugs. Numerical simulation enables to estimate the degree of side effects caused by the predicted drug interactions. New hypothetic assertions of the potential drug interactions and simulation are deduced from the Drug Interaction Ontology DIO written in Web Ontology Language OWL.

ResultsThe concept of the Ontology-Driven Hypothetic Assertion OHA framework was demonstrated with known interactions between irinotecan CPT-11 and ketoconazole. Four drug interactions that involved cytochrome p450 CYP3A4 and albumin as potential drug interaction proteins were automatically detected from Drug Interaction Ontology DIO. The effect of the two interactions involving CYP3A4 were quantitatively evaluated with numerical simulation. The co-administration of ketoconazole may increase AUC and Cmax of SN-38active metabolite of irinotecan to 108% and 105%, respectively. We also estimates the potential effects of genetic variations: the AUC and Cmax of SN-38 may increase to 208% and 165% respectively with the genetic variation UGT1A1*28-*28 which reduces the expression of UGT1A1 down to 30%.

ConclusionThese results demonstrate that the Ontology-Driven Hypothetic Assertion framework is a promising approach for in silico prediction of drug interactions. The following future researches for the in silico prediction of individual differences in the response to the drug and drug interactions after the administration of multiple drugs: expansion of the Drug Interaction Ontology for other drugs, and incorporation of virtual population model for genetic variation analysis, as well as refinement of the pathway generation rules, the drug interaction detection rules, and the numerical simulation models.

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2105-9-S6-S11 contains supplementary material, which is available to authorized users.

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Autor: Takeshi Arikuma - Sumi Yoshikawa - Ryuzo Azuma - Kentaro Watanabe - Kazumi Matsumura - Akihiko Konagaya

Fuente: https://link.springer.com/

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