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Machine Learning Approach in Dosage Individualization of Isoniazid for Tuberculosis

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Abstract

Introduction

Isoniazid is a first-line antituberculosis agent with high variability, which would profit from individualized dosing. Concentrations of isoniazid at 2 h (C2h), as an indicator of safety and efficacy, are important for optimizing therapy.

Objective

The objective of this study was to establish machine learning (ML) models to predict the C2h, that can be used for establishing an individualized dosing regimen in clinical practice.

Methods

Published population pharmacokinetic (PopPK) models for adults were searched based on PubMed and ultimately four reliable models were selected for simulating individual C2h datasets under different conditions (demographics, genotype, ethnicity, etc.). Machine learning models were trained on simulated C2h obtained from the four PopPK models. Five different algorithms were used for ML model building to predict C2h. Real-world data were used for predictive performance evaluations. Virtual trials were used to compare ML-optimized doses with PopPK model-optimized doses.

Results

Categorical boosting (CatBoost) exhibited the highest prediction ability. Target C2h can be predicted using the ML model combined with the dosing regimen and three covariates (N-acetyltransferase 2 [NAT2] genotypes, weight and race [Asians and Africans]). Real-world data validation results showed that the ML model can achieve an overall prediction accuracy of 93.4%. Using the final ML model, the mean absolute prediction error value decreased by 45.7% relative to the average of PopPK models. Using the ML-optimized dosing regimen, the probability of target attainment increased by 43.7% relative to the PopPK model-optimized dosing regimens.

Conclusion

Machine learning models were developed with great predictive performance, which can be used to determine the individualized initial dose of isoniazid in adult patients.

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Acknowledgments

We thank all of the patients who participated in this study and all of the participants and research staff. All authors approved the final version of the manuscript.

Author information

Author notes

  1. Bo-Hao Tang and **n-Fang Zhang contributed equally to this work. Hai-Rong Huang, Guo-**ang Hao, and Wei Zhao contributed equally to this work.

Authors and Affiliations

  1. Department of Pharmacy, The Second Hospital, Cheeloo College of Medicine, Shandong University, **an, China

    Bo-Hao Tang & Wei Zhao

  2. Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, **an, China

    Bo-Hao Tang, **n-Fang Zhang, Shu-Meng Fu, Bu-Fan Yao, Wei Zhang, Yue-E. Wu, Yi Zheng, Yue Zhou, Guo-**ang Hao & Wei Zhao

  3. Division of Clinical Pharmacology, Children’s National Hospital, Washington, DC, USA

    John van den Anker

  4. Departments of Pediatrics, Pharmacology and Physiology, Genomics and Precision Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA

    John van den Anker

  5. Department of Pediatric Pharmacology and Pharmacometrics, University of Basel Children’s Hospital, Basel, Switzerland

    John van den Anker

  6. National Clinical Laboratory on Tuberculosis, Bei**g Key Laboratory on Drug-Resistant Tuberculosis, Bei**g Chest Hospital, Bei**g Tuberculosis and Thoracic Tumor Research Institute, Capital Medical University, Bei**g, China

    Hai-Rong Huang

  7. NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, Qilu Hospital of Shandong University, Shandong University, **an, China

    Wei Zhao

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Corresponding author

Correspondence to Wei Zhao.

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Funding

This paper is funded by the National Key R&D Program of China (2023YFC2706100, 2022YFC0868600), National Natural Science Foundation of China (82173897), Distinguished Young and Middle-aged Scholar of Shandong University, Innovation and Development Joint Fund of Natural Science Foundation of Shandong Province (ZR2022LSW007), Natural Science Foundation of Shandong Province (ZR2022QH004), the Capital’s Funds for Health Improvement and Research (2020-2-2161) and Bei**g High-Level Public Health Talent Program (G2003-2-002). We declare that we have no conflicts of interest relevant to this article.

Conflicts of Interest

John van den Anker is an Editorial Board member of Clinical Pharmacokinetics. John van den Anker was not involved in the selection of peer reviewers for the manuscript nor any of the subsequent editorial decisions. The authors declared no competing interests for this work.

Ethics Approval

All the data were obtained from previous studies. These studies were approved by the institutional ethics committee.

Consent to Participate

All the data were obtained from previous studies. These studies were approved by the institutional ethics committee.

Consent for Publication

All participants received written informed consent in the previous studies.

Availability of Data and Material

The data that support the findings of this study are available on reasonable request from the corresponding authors.

Code Availability

Research codes are available in the supplements.

Authors’ Contributions

B-H.T., X-F.Z., G-X.H. and J.V.D.A. wrote the manuscript; B-H.T., X-F.Z., S-M.F., B-F.Y., Y-E.W., Y.Z., Y.Z., H-R.H., G-X.H.and W.Z. designed the research; B-H.T., X-F.Z., H-R.H., G-X.H.and W.Z. performed the research; B-H.T. and X-F.Z. analyzed the data.

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Tang, BH., Zhang, XF., Fu, SM. et al. Machine Learning Approach in Dosage Individualization of Isoniazid for Tuberculosis. Clin Pharmacokinet (2024). https://doi.org/10.1007/s40262-024-01400-4

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