Masayo Hayakawa1, Takeshi Imai2, Yoshimasa Kawazoe3, Kouji Kozaki4, Kazuhiko Ohe3. 1. Center for Cancer Control and Information Services, National Cancer Center, 5-1-1, Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan. hayakawam-tky@umin.org. 2. Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. 3. Department of Biomedical Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. 4. Faculty of Information and Communication Engineering, Osaka Electro-Communication University, Osaka, Japan.
Abstract
INTRODUCTION: Patients often take several different medications for multiple conditions concurrently. Therefore, when adverse drug events (ADEs) occur, it is necessary to consider the mechanisms responsible. Few approaches consider the mechanisms of ADEs, such as changes in physiological states. We proposed that the ontological framework for pharmacology and mechanism of action (pharmacodynamics) we developed could be used for this approach. However, the existing knowledge base contains little data on physiological chains (PCs). OBJECTIVE: We aimed to investigate a method for automatically generating missing PC from the viewpoint of anatomical structures. This study was conducted to determine dysuria-related adverse events more likely to occur during multidrug administration. METHODS: We adopted a systematic approach to determine drugs suspected to cause adverse events and incorporated existing data and data generated in our newly developed method into our ontological framework. The performance of automated data generation was evaluated using this newly developed system. Suspected drugs determined by the system were compared with those derived from adverse events databases. RESULTS: Of the 242 drugs involving suspected drug-induced urinary retention or dysuria, 26 suspected drugs were determined. Of these, five were drugs with side effects not listed in drug package inserts. The system derived potential mechanisms of action, PCs, and suspected drugs. CONCLUSION: Our method is novel in that it generates PC data from anatomical structural properties and could serve as a knowledge base for determining suspected drugs by potential mechanisms of action.
INTRODUCTION:Patients often take several different medications for multiple conditions concurrently. Therefore, when adverse drug events (ADEs) occur, it is necessary to consider the mechanisms responsible. Few approaches consider the mechanisms of ADEs, such as changes in physiological states. We proposed that the ontological framework for pharmacology and mechanism of action (pharmacodynamics) we developed could be used for this approach. However, the existing knowledge base contains little data on physiological chains (PCs). OBJECTIVE: We aimed to investigate a method for automatically generating missing PC from the viewpoint of anatomical structures. This study was conducted to determine dysuria-related adverse events more likely to occur during multidrug administration. METHODS: We adopted a systematic approach to determine drugs suspected to cause adverse events and incorporated existing data and data generated in our newly developed method into our ontological framework. The performance of automated data generation was evaluated using this newly developed system. Suspected drugs determined by the system were compared with those derived from adverse events databases. RESULTS: Of the 242 drugs involving suspected drug-induced urinary retention or dysuria, 26 suspected drugs were determined. Of these, five were drugs with side effects not listed in drug package inserts. The system derived potential mechanisms of action, PCs, and suspected drugs. CONCLUSION: Our method is novel in that it generates PC data from anatomical structural properties and could serve as a knowledge base for determining suspected drugs by potential mechanisms of action.
Authors: Zhichao Liu; Hong Fang; Kelly Reagan; Xiaowei Xu; Donna L Mendrick; William Slikker; Weida Tong Journal: Drug Discov Today Date: 2012-08-28 Impact factor: 7.851
Authors: Sirarat Sarntivijai; Shelley Zhang; Desikan G Jagannathan; Shadia Zaman; Keith K Burkhart; Gilbert S Omenn; Yongqun He; Brian D Athey; Darrell R Abernethy Journal: Drug Saf Date: 2016-07 Impact factor: 5.606
Authors: Paul Avillach; Preciosa M Coloma; Rosa Gini; Martijn Schuemie; Fleur Mougin; Jean-Charles Dufour; Giampiero Mazzaglia; Carlo Giaquinto; Carla Fornari; Ron Herings; Mariam Molokhia; Lars Pedersen; Annie Fourrier-Réglat; Marius Fieschi; Miriam Sturkenboom; Johan van der Lei; Antoine Pariente; Gianluca Trifirò Journal: J Am Med Inform Assoc Date: 2012-09-06 Impact factor: 4.497
Authors: Eugen Lounkine; Michael J Keiser; Steven Whitebread; Dmitri Mikhailov; Jacques Hamon; Jeremy L Jenkins; Paul Lavan; Eckhard Weber; Allison K Doak; Serge Côté; Brian K Shoichet; Laszlo Urban Journal: Nature Date: 2012-06-10 Impact factor: 49.962