Ontology-Driven Hypothetic Assertion (OHA) for Drug InteractionPrediction

@inproceedings{citeulike:2861331, abstract = {In 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 a 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. Method: On-stimulus dynamic pathway generation has been adopted to evade dealing with redundant static pathways in exponential numbers. Potential drug-drug interactions are detected from drug metabolic pathways dynamically generated by molecular events triggered after the administration of certain drugs. New hypothetic assertions of potential drug interactions are deduced from the Drug Interaction Ontology (DIO) written in Web Ontology Language (OWL). Results and Conclusion: The concept of Ontology- Driven Hypothetic Assertion (OHA) was demonstrated with known interactions between irinotecan (CPT-11) and ketoconazole. The system automatically detected four drug- drug interactions that involved cytochrome p450 (CYP3A4) and albumin as potential drug interaction genes. Future plans regarding in silico prediction of individual differences in the response to the drug and drug-drug interactions after the administration of multiple drugs are also discussed. Keyword: Biomedical Ontology, Drug Interaction, Metabolic Pathway, Web Ontology Language (OWL), Pharmacokinetics}, author = {Arikuma, Takeshi and Yoshikawa, Sumi and Watanabe, Kentaro and Konagaya, Akihiko }, booktitle = {Computer and Computational Sciences, 2007. IMSCCS 2007. Second International Multi-Symposiums on}, citeulike-article-id = {2861331}, doi = {10.1109/IMSCCS.2007.72}, journal = {Computer and Computational Sciences, 2007. IMSCCS 2007. Second International Multi-Symposiums on}, keywords = {medical, ontology, semantic\_web}, pages = {1--8}, posted-at = {2008-06-04 15:48:30}, priority = {2}, title = {Ontology-Driven Hypothetic Assertion (OHA) for Drug InteractionPrediction}, url = {http://dx.doi.org/10.1109/IMSCCS.2007.72}, year = {2007} }

TY - CONF ID - citeulike:2861331 L3 - citeulike-article-id:2861331 TI - Ontology-Driven Hypothetic Assertion (OHA) for Drug InteractionPrediction T2 - Computer and Computational Sciences, 2007. IMSCCS 2007. Second International Multi-Symposiums on JF - Computer and Computational Sciences, 2007. IMSCCS 2007. Second International Multi-Symposiums on SP - 1 EP - 8 N2 - In 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 a 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. Method: On-stimulus dynamic pathway generation has been adopted to evade dealing with redundant static pathways in exponential numbers. Potential drug-drug interactions are detected from drug metabolic pathways dynamically generated by molecular events triggered after the administration of certain drugs. New hypothetic assertions of potential drug interactions are deduced from the Drug Interaction Ontology (DIO) written in Web Ontology Language (OWL). Results and Conclusion: The concept of Ontology- Driven Hypothetic Assertion (OHA) was demonstrated with known interactions between irinotecan (CPT-11) and ketoconazole. The system automatically detected four drug- drug interactions that involved cytochrome p450 (CYP3A4) and albumin as potential drug interaction genes. Future plans regarding in silico prediction of individual differences in the response to the drug and drug-drug interactions after the administration of multiple drugs are also discussed. Keyword: Biomedical Ontology, Drug Interaction, Metabolic Pathway, Web Ontology Language (OWL), Pharmacokinetics KW - medical KW - ontology KW - semantic_web AU - Arikuma, Takeshi AU - Yoshikawa, Sumi AU - Watanabe, Kentaro AU - Konagaya, Akihiko PY - 2007/// UR - http://dx.doi.org/10.1109/IMSCCS.2007.72 ER -