Machine Learning Approach in Dosage Individualization of Isoniazid for Tuberculosis.

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.