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Auto-Generated Physiological Chain Data for an Ontological Framework for Pharmacology and Mechanism of Action to Determine Suspected Drugs in Cases of Dysuria

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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.

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Authors and Affiliations

  1. Center for Cancer Control and Information Services, National Cancer Center, 5-1-1, Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan

    Masayo Hayakawa

  2. Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

    Takeshi Imai

  3. Department of Biomedical Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

    Yoshimasa Kawazoe & Kazuhiko Ohe

  4. Faculty of Information and Communication Engineering, Osaka Electro-Communication University, Osaka, Japan

    Kouji Kozaki

Authors
  1. Masayo Hayakawa
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  4. Kouji Kozaki
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  5. Kazuhiko Ohe
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Corresponding author

Correspondence to Masayo Hayakawa.

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Research involving human participants and/or animals

This manuscript does not contain any studies with human participants or animals performed by any of the authors.

Conflict of interest

Masayo Hayakawa, Takeshi Imai, Yoshimasa Kawazoe, Kouji Kozaki, and Kazuhiko Ohe have no conflicts of interest that are directly relevant to the content of this study.

Funding

This work was partially supported by Health, Labour and Welfare Sciences Research Grants number H28-IRYO-SHITEI-020, Japan, and JSPS KAKENHI Grant number JP26460857.

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Hayakawa, M., Imai, T., Kawazoe, Y. et al. Auto-Generated Physiological Chain Data for an Ontological Framework for Pharmacology and Mechanism of Action to Determine Suspected Drugs in Cases of Dysuria. Drug Saf 42, 1055–1069 (2019). https://doi.org/10.1007/s40264-019-00833-2

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