II-073

Pharmacogenetics-guided isoniazid dosing in individuals with tuberculosis and HIV

Allan Kengo1,2, Dr Brian Otaalo1, Dr Ruth Nabisere1, Dr Irene Mbabazi1, Elisa Ignatius3, Mr Allan Buzibye1, Dr. Barbara Castelnouvo1, Dr. Suzan Adakun4, Prof Kelly Dooley3, Dr. David Meya1,5, Dr Christine Sekaggya-Wilshire1

1University of Cape Town, 2Infectious Diseases Institute, 3Johns Hopkins University, 4Mulago National Referral Hospital, 5Makerere University

Keywords: Pharmacogenetics, isoniazid, NAT2 polymorphism, tuberculosis, HIV, personalized medicine Background: Tuberculosis (TB) remains a major global health challenge, particularly in individuals with HIV coinfection (1). Isoniazid (INH) is a cornerstone of TB treatment, but its pharmacokinetics exhibit significant interindividual variability due to genetic polymorphisms in NAT2, the gene encoding the N-acetyl transferase 2 (NAT2) enzyme responsible for its metabolism (2). Single nucleotide polymorphisms (SNPs) in the NAT2 gene classify individuals into slow, intermediate, or fast acetylators (3), affecting drug exposure and possibly therapeutic outcomes. The target concentration of INH is a maximum concentration (Cmax) of 3 to 6 mg/L (4). Suboptimal INH concentrations have been reported in some patients, yet the drug exhibits concentration-dependent bactericidal activity against Mycobacterium tuberculosis (5). We investigated the pharmacokinetics of INH at the standard (5 mg/kg) dose in slow acetylators and a higher (10 mg/kg) dose in intermediate and fast acetylators to optimize drug exposure and efficacy. Methods: The PHINX study was conducted at the Infectious Diseases Institute in Kampala, Uganda, enrolling people living with HIV and TB co-infection. The study received ethical approval from the Joint Clinical Research Center research and ethics committee, and participants provided written informed consent. Participants were programmatically initiated on standard-dose INH alongside other standard anti-TB therapy. Blood samples for DNA extraction and genotyping were collected within the first week of treatment. NAT2 acetylator status was determined based on four SNPs: rs1801279 (c.191G>A), rs1801280 (c.341T>C), rs1799930 (c.590G>A), and rs1799931 (c.857G>A), as previously described (6). Patients subsequently identified as intermediate or fast acetylators had their INH dose increased to 10 mg/kg. Pharmacokinetic sampling was performed after four weeks of TB treatment at pre-dose and at 0.5, 1, 2, and 4-hours post-dose. INH concentrations were assayed using a previously published high-performance liquid chromatography method with a lower limit of quantification of 0.5 mg/L. At week 8, the INH dose was adjusted back to the standard 5 mg/kg, and participants were followed up until week 24. INH data were modeled in NONMEM. Results: Data from 31 participants (52% male) were analyzed. Their median (IQR) weight and age were 54 kg (50-61) and 36 years (18-61) respectively. NAT2 genotyping classified 68% of participants as slow acetylators and 32% as intermediate acetylators. A previously published two-compartment pharmacokinetic model with transit absorption and liver elimination adequately described INH pharmacokinetics with minor modification (7). The typical clearance values were 7.83 L/h for slow acetylators and 16.0 L/h for intermediate acetylators. The INH bioavailability was 20% higher in participants whose dose was increased. Model-informed simulations showed that 98% of slow acetylators and 82% of intermediate acetylators achieved optimal INH peak concentrations at the standard 5 mg/kg dose. The simulations also predicted that increasing the INH dose to 10 mg/kg could raise the number of fast acetylators achieving optimal INH peak concentrations from 37% to 63%. Conclusions: The standard 5 mg/kg INH dose is appropriate for slow and most intermediate acetylators, while fast acetylators may benefit from a dose increase to 10 mg/kg. Notably, no fast acetylators were identified in our study population.

 1.         WHO. 2024 Global tuberculosis report. 2024 Oct. 2.         Klein DJ, Boukouvala S, McDonagh EM, Shuldiner SR, Laurieri N, Thorn CF, et al. PharmGKB summary: Isoniazid pathway, pharmacokinetics. Pharmacogenet Genomics. 2016 Sep 1;26(9):436–44. 3.         Boukouvala S, Fakis G. Arylamine N-acetyltransferases: What we learn from genes and genomes. Vol. 37, Drug Metabolism Reviews. 2005. p. 511–64. 4.         Alsultan A, Peloquin CA. Therapeutic drug monitoring in the treatment of tuberculosis: An update. Vol. 74, Drugs. Springer International Publishing; 2014. p. 839–54. 5.         Aljayyoussi G, Jenkins VA, Sharma R, Ardrey A, Donnellan S, Ward SA, et al. Pharmacokinetic-Pharmacodynamic modeling of intracellular Mycobacterium tuberculosis growth and kill rates is predictive of clinical treatment duration. Sci Rep. 2017 Dec 1;7(1). 6.         Hein DW, Doll MA. Accuracy of various human NAT2 SNP genotyping panels to infer rapid, intermediate and slow acetylator phenotypes. Pharmacogenomics. 2012 Jan;13(1):31–41. 7.         Kengo A, Nabeemeeah F, Denti P, Sabet R, Okyere-Manu G, Abraham P, et al. Assessing potential drug-drug interactions between clofazimine and other frequently used agents to treat drug-resistant tuberculosis. Antimicrob Agents Chemother. 2024 Apr 10; 

Reference: PAGE 33 (2025) Abstr 11499 [www.page-meeting.org/?abstract=11499]

Poster: Drug/Disease Modelling - Infection

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