Machine Learning Approach in Dosage Individualization of Isoniazid for Tuberculosis

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_2h), 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_2h, 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_2h datasets under different conditions (demographics, genotype, ethnicity, etc.). Machine learning models were trained on simulated C_2h obtained from the four PopPK models. Five different algorithms were used for ML model building to predict C_2h. 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_2h 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.