Machine Learning Approach in Dosage Individualization of Isoniazid for Tuberculosis

Document Type

Journal Article

Publication Date

7-1-2024

Journal

Clinical pharmacokinetics

Volume

63

Issue

7

DOI

10.1007/s40262-024-01400-4

Abstract

INTRODUCTION: Isoniazid is a first-line antituberculosis agent with high variability, which would profit from individualized dosing. Concentrations of isoniazid at 2 h (C), 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 C, 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 C datasets under different conditions (demographics, genotype, ethnicity, etc.). Machine learning models were trained on simulated C obtained from the four PopPK models. Five different algorithms were used for ML model building to predict C. 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 C 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.

Department

Pediatrics

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