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Developing an AI-based prediction model for anaphylactic shock from injection drugs using Japanese real-world data and chemical structure-based analysis

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Abstract

Background

This study aims to develop an AI-based prediction model for injection drugs that cause anaphylactic shock using Japanese Real-World Data (JADER database) and chemical structure-based analysis.

Methods

Data sourced from the JADER database included adverse drug reaction reports from April 2004 to December 2020. Only drugs with an adverse reaction named "anaphylactic shock" were selected for analysis. For model building, various models were constructed to predict anaphylactic shock-inducing drugs, such as logistic regression, LASSO, XGBoost, RF, SVM, and NNW. These models used chemical properties and structural similarities as feature variables. Dimension reduction was applied using principal component analysis. The dataset was split into training (80%) and validation (20%) sets. Six different models were trained and optimized through fivefold cross-validation.

Results

From April 2004 to December 2020, 947 drugs with the adverse reaction name "anaphylactic shock" were extracted from the JADER database. 320 drugs were excluded due to analytical challenges, and another 400 were removed due to their administration route. 227 drugs were finalized as target medicines. For model validation, the performance of each model was evaluated based on metrics like AUCs of ROC curve, sensitivity, and specificity. Additionally, two ensemble models, constructed from the six models were assessed using bootstrap sampling. Interestingly, it was identified that mepivacaine structural similarity had the highest importance in the final model.

Conclusions

The study successfully developed an AI-based prediction model for anaphylactic shock inducing-injection drugs. The model would offer potential for drug safety evaluation and anaphylactic shock risk assessment.

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Data Availability

Data may be made available upon reasonable request to the principal investigator of the study. However, it must comply with applicable legal and ethical restrictions.

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Acknowledgements

This study did not receive any specific funding from public, commercial, or not-for-profit sectors.

Funding

The authors did not receive support from any organization for the submitted work.

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Authors and Affiliations

  1. Laboratory of Pharmacoinformatics, Department of Pharmaceutical Sciences, Faculty of Pharmaceutical Sciences, Suzuka University of Medical Science, 3500-3 Minamitamagakichō, Suzuka, Mie Prefecture, 513-8670, Japan

    Tomoyuki Enokiya & Kaito Ozaki

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  1. Tomoyuki Enokiya
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  2. Kaito Ozaki
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Contributions

TE designed the study, collected and analyzed the data, and wrote the manuscript. KO collected and analyzed the data.

Corresponding author

Correspondence to Tomoyuki Enokiya.

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Competing interests

The authors declare no competing financial or non-financial interests.

Ethics approval

This study adheres to the ethical standards of the Declaration of Helsinki and the Ethical Guidelines for Research with Human Participants in Medical and Health Care. Due to the use of publicly available and/or anonymized data, this research was exempt from ethical review. The absence of identifiable details in the data negated the necessity for informed consent. We have notified the Pharmaceuticals and Medical Devices Agency about our use of this data and our intention to publish the study's findings.

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Enokiya, T., Ozaki, K. Developing an AI-based prediction model for anaphylactic shock from injection drugs using Japanese real-world data and chemical structure-based analysis. DARU J Pharm Sci (2024). https://doi.org/10.1007/s40199-024-00511-4

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  • DOI: https://doi.org/10.1007/s40199-024-00511-4

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