Population Pharmacokinetic Modeling and Simulation of Rifapentine Supports Concomitant Antiretroviral Therapy with Efavirenz and Non-Weight Based Dosing

Clinical considerations and pharmacokinetic interactions between HIV and tuberculosis therapeutics

INTRODUCTION

Tuberculosis (TB) is a leading infectious disease cause of mortality worldwide, especially for people living with human immunodeficiency virus (PLWH). Treating TB in PLWH can be challenging due to numerous drug interactions.

AREAS COVERED

This review discusses drug interactions between antitubercular and antiretroviral drugs. Due to its clinical importance, initiation of antiretroviral therapy in patients requiring TB treatment is discussed. Special focus is placed on the rifamycin class, as it accounts for the majority of interactions. Clinically relevant guidance is provided on how to manage these interactions. An additional section on utilizing therapeutic drug monitoring (TDM) to optimize drug exposure and minimize toxicities is included.

EXPERT OPINION

Antitubercular and antiretroviral coadministration can be successfully managed. TDM can be used to optimize drug exposure and minimize toxicity risk. As new TB and HIV drugs are discovered, additional research will be needed to assess for clinically relevant drug interactions.

Genetic and clinical predictors of rifapentine and isoniazid pharmacokinetics in paediatrics with tuberculosis infection

OBJECTIVES

Twelve weekly doses of rifapentine and isoniazid (3HP regimen) are recommended for TB preventive therapy in children with TB infection. However, they present with variability in the pharmacokinetic profiles. The current study aimed to develop a pharmacokinetic model of rifapentine and isoniazid in 12 children with TB infection using NONMEM.

METHODS

Ninety plasma and 41 urine samples were collected at Week 4 of treatment. Drug concentrations were measured using a validated HPLC-UV method. MassARRAY® SNP genotyping was used to investigate genetic factors, including P-glycoprotein (ABCB1), solute carrier organic anion transporter B1 (SLCO1B1), arylacetamide deacetylase (AADAC) and N-acetyl transferase (NAT2). Clinically relevant covariates were also analysed.

RESULTS

A two-compartment model for isoniazid and a one-compartment model for rifapentine with transit compartment absorption and first-order elimination were the best models for describing plasma and urine data. The estimated (relative standard error, RSE) of isoniazid non-renal clearance was 3.52 L·h-1 (23.1%), 2.91 L·h-1 (19.6%), and 2.58 L·h-1 (20.0%) in NAT2 rapid, intermediate and slow acetylators. A significant proportion of the unchanged isoniazid was cleared renally (2.7 L·h-1; 8.0%), while the unchanged rifapentine was cleared primarily through non-renal routes (0.681 L·h-1; 3.6%). Participants with the ABCB1 mutant allele had lower bioavailability of rifapentine, while food prolonged the mean transit time of isoniazid.

CONCLUSIONS

ABCB1 mutant allele carriers may require higher rifapentine doses; however, this must be confirmed in larger trials. Food did not affect overall exposure to isoniazid and only delayed absorption time.

Evaluation of drug-drug interaction between rilpivirine and rifapentine using PBPK modelling

Tuberculosis remains the leading cause of death among people living with HIV. Rifapentine is increasingly used to treat active disease or prevent reactivation, in both cases given either as weekly or daily therapy. However, rifapentine is an inducer of CYP3A4, potentially interacting with antiretrovirals like rilpivirine. This in silico study investigates the drug-drug interaction (DDI) magnitude between daily oral rilpivirine 25 mg with either daily 600 mg or weekly 900 mg rifapentine. A physiologically based pharmacokinetic (PBPK) model was built in Simbiology (Matlab R2018a) to simulate the drug-drug interaction. The simulated PK parameters from the PBPK model were verified against reported clinical data for rilpivirine and rifapentine separately, daily rifapentine with midazolam, and weekly rifapentine with doravirine. The simulations of concomitant administration of rifapentine with rilpivirine at steady-state lead to a maximum decrease on AUC0-24 and Ctrough by 83% and 92% on day 5 for the daily rifapentine regimen and 68% and 92% for the weekly regimen on day 3. In the weekly regimen, prior to the following dose, AUC0-24 and Ctrough were still reduced by 47% and 53%. In both simulations, the induction effect ceased 2 weeks after the interruption of rifapentine's treatment. A daily double dose of rilpivirine after initiating rifapentine 900 mg weekly was simulated but failed to compensate the drug-drug interaction. The drug-drug interaction model suggested a significant decrease on rilpivirine exposure which is unlikely to be corrected by dose increment, thus coadministration should be avoided.