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Application of Northern Goshawk Back-Propagation Artificial Neural Network in the Prediction of Monohydroxycarbazepine Concentration in Patients with Epilepsy

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Abstract

Introduction

A northern goshawk back-propagation artificial neural network (NGO-BPANN) model was established to predict monohydroxycarbazepine (MHD) concentration in patients with epilepsy.

Methods

The data were collected from 108 Han Chinese patients with epilepsy on oxcarbazepine monotherapy. The results of 14 genotype variates were selected as the input layer in the first BPANN model, and the variables that had a more significant impact on the plasma concentration of MHD were retained. With demographic characteristics and clinical laboratory test results, the genotypes of SCN1A rs2298771 and SCN2A rs17183814 were used to construct the BPANN model. The BPANN model was comprehensively validated and used to predict the MHD plasma concentration of five patients with epilepsy in our hospital.

Results

The model demonstrated favorable fitness metrics, including a mean squared error of 0.00662, a gradient magnitude of 0.00753, an absence of validation tests amounting to zero, and a correlation coefficient of 0.980. Sex, BMI, and the genotype SCN1A rs2298771 were ranked highest by the absolute mean impact value (MIV), which is primarily associated with the concentration of MHD. The test group exhibited a range of − 20.84% to 31.03% bias between the predicted and measured values, with a correlation coefficient of 0.941 between the two. With BPANN, the MHD nadir concentration could be predicted precisely.

Conclusion

The NGO-BPANN model exhibits exceptional predictive capability and can be a practical instrument for forecasting MHD concentration in patients with epilepsy.

Clinical Trial Registration

www.chiCTR-OOC-17012141.

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Data Availability

All figures are original and all data and materials included in this study are available upon request by contact with the corresponding author.

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Medical Writing and Editorial Assistance.

No medical writing or editorial assistance is received during the writing of this article (including AI).

Funding

This study was supported by the National Major Science and Technology Projects of China (No. 2020ZX09201022). The Rapid Service Fee was funded by the authors.

Author information

Authors and Affiliations

  1. Center of Clinical Pharmacology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China

    Yichao Xu & Rong Shao

  2. Department of Pharmacy, The Second Affiliated Hospital, Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China

    Mingdong Yang, Meng Chen, Junjun Xu & Haibin Dai

Authors
  1. Yichao Xu
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  2. Rong Shao
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  3. Mingdong Yang
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  4. Meng Chen
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  5. Junjun Xu
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  6. Haibin Dai
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Contributions

The study was designed and conceived by Haibin Dai. The study was conducted by Mingdong Yang, Meng Chen, and Junjun Xu. Data collection, analysis, and model construction were performed by Rong Shao and Yichao Xu. The first draft of the manuscript was written by Yichao Xu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Haibin Dai.

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Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ethical Approval

The study was in accordance with the Declaration of Helsinki, and the studies involving human participants were reviewed and approved by the Human Subject Research Ethics Committee of the Second Affiliated Hospital of Zhejiang University School of Medicine. The patients/participants provided their written informed consent to participate in this study.

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Xu, Y., Shao, R., Yang, M. et al. Application of Northern Goshawk Back-Propagation Artificial Neural Network in the Prediction of Monohydroxycarbazepine Concentration in Patients with Epilepsy. Adv Ther 41, 1450–1461 (2024). https://doi.org/10.1007/s12325-024-02792-2

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