Model-Informed Precision Dosing of Isoniazid: Parametric Population Pharmacokinetics Model Repository

Abstract

Introduction

Isoniazid (INH) is a crucial first-line anti tuberculosis (TB) drug used in adults and children. However, various factors can alter its pharmacokinetics (PK). This article aims to establish a population pharmacokinetic (popPK) models repository of INH to facilitate clinical use.

Methods

A literature search was conducted until August 23, 2022, using PubMed, Embase, and Web of Science databases. We excluded published popPK studies that did not provide full model parameters or used a non-parametric method. Monte Carlo simulation works was based on RxODE. The popPK models repository was established using R. Non-compartment analysis was based on IQnca.

Results

Fourteen studies included in the repository, with eleven studies conducted in adults, three studies in children, one in pregnant women. Two-compartment with allometric scaling models were commonly used as structural models. NAT2 acetylator phenotype significantly affecting the apparent clearance (CL). Moreover, postmenstrual age (PMA) influenced the CL in pediatric patients. Monte Carlo simulation results showed that the geometric mean ratio (95% Confidence Interval, CI) of PK parameters in most studies were within the acceptable range (50.00–200.00%), pregnant patients showed a lower exposure. After a standard treatment strategy, there was a notable exposure reduction in the patients with the NAT2 RA or nonSA (IA/RA) phenotype, resulting in a 59.5% decrease in AUC_0-24 and 83.2% decrease in C_max (Infants), and a 49.3% reduction in AUC_0-24 and 73.5% reduction in C_max (Adults).

Discussion

Body weight and NAT2 acetylator phenotype are the most significant factors affecting the exposure of INH. PMA is a crucial factor in the pediatric population. Clinicians should consider these factors when implementing model-informed precision dosing of INH. The popPK model repository for INH will aid in optimizing treatment and enhancing patient outcomes.

Keywords:

• Isoniazid
• model-informed precision dosing
• population pharmacokinetics
• nonlinear mixed-effects model

Acknowledgments

Thanks to Changsha Duxact Clinical Laboratory Co., Ltd and Phamark Data Technology Co, Ltd, Changsha, Hunan, China for the statistical support.

Disclosure

The authors report no competing interests in this work.

Additional information

Funding

The research was funded by the Hunan graduate Research Innovation Project (grant number CX20220131), the National Natural Science Foundation of China (grant number 81803837), the Natural Science Foundation of Hunan Province (grant number 2022JJ80100, 2019JJ50839), the Hunan Province Foundation of High-level Health Talent (grant number 225), and the Science and Technology Key Program of Hunan Provincial Health Committee (grant number 20201904).